Reducto vs Extend

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round drawn
   Reducto✓fullprobed9/10

   reducto-probe-1 confirms a live, working llms.txt at docs.reducto.ai/llms.txt returning HTTP 200 with structured links to docs, and the .md-suffixed doc pages (e.g. reducto-docs-29 through -40) show agent-oriented markdown docs are served alongside an MCP server and CLI for agent consumption. Missing for 10: no independent/community confirmation of an agent successfully consuming llms.txt in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.reducto.ai/llms.txt # Reducto - [Overview](https://docs.reducto.ai/overview.md): The agentic docum…”
   • [claimed-docs] “Parse is Reducto's foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can classify, parse, ext…”
   • [probe] “official MCP server documented at https://docs.reducto.ai/mcp-server”

   proof ↗⚑ flag

   Extend✓fullprobed9/10

   Extend has a confirmed live llms.txt at docs.extend.ai/llms.txt (HTTP 200) providing agent-oriented documentation, alongside agent-oriented docs like the Agent Quickstart and MCP integration docs that an AI agent could consume directly. Missing for 10: no independent/community confirmation of an agent actually consuming llms.txt successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.extend.ai/llms.txt # Extend > Extend is a platform for building, iterating on, evaluating, and dep…”
   • [claimed-docs] “Agent Quickstart | Extend Documentation”
   • [claimed-docs] “Using Extend via MCP | Extend Documentation”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round to Reducto
   Reducto✓fullprobed8/10

   Reducto offers a documented API/SDK, CLI, async job endpoints, webhooks, and OpenAPI spec — all standard building blocks for headless/CI automation, with docs explicitly showing scriptable calls like client.parse.run() and CLI usage. Missing for 10: no explicit CI/CD pipeline examples (e.g., GitHub Actions), no independent hands-on report confirming headless CI usage.

   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto’s document capabilities: parse, extract, split, classify, and edit.”
   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto's document capabilities: parse, extract, split, classify, and edit.”
   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “client.parse.run_job() ... POST /parse_async ... Job ID (returns immediately)”
   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://docs.reducto.ai/cli”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”

   proof ↗⚑ flag

   Extend~partialprobed6/10

   Extend offers a documented CLI, Python SDK (pip install), and REST API with sync/async modes and a test API key mode, all of which support headless/CI-driven automation of document processing. However, there is no explicit CI/CD integration guide, GitHub Actions example, or independent hands-on report of running it in a CI pipeline. missing for 10: explicit CI pipeline examples/integration guide, independent hands-on verification of headless CI usage, and details on exit codes/CLI scripting ergonomics for automation.

   • [claimed-docs] “pip install extend-ai”
   • [claimed-docs] “CLI | Extend Documentation”
   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “Runs made with a test API key never touch production data and never fire production webhooks”
   • [probe] “official CLI documented at https://docs.extend.ai/cli”

   proof ↗⚑ flag

3. ai-native userConnect an agent via an official MCP server

   weight 3 · round drawn
   Reducto✓fullprobed8/10

   Reducto is a document processing SaaS (not itself an agent), and it publishes an official MCP server that lets agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any MCP client classify, parse, extract, split, and edit documents as part of their reasoning loop. This is documented first-party and confirmed via probe. Missing for 10: independent/hands-on third-party corroboration of the MCP server working in practice.

   • [claimed-docs] “Once installed, agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can clas…”
   • [claimed-docs] “agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can classify, parse, ext…”
   • [claimed-docs] “agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can classify, parse, ext…”
   • [probe] “official MCP server documented at https://docs.reducto.ai/mcp-server”

   proof ↗⚑ flag

   Extend✓fullprobed8/10

   Extend is a document-processing platform (not an agent itself), and it documents an official MCP server ('Using Extend via MCP' at docs.extend.ai/mcp), confirmed by probe as live documentation, allowing AI agents to connect via MCP. missing for 10: no independent/hands-on confirmation of the MCP server working in practice, and limited detail on setup specifics beyond the doc title.

   • [claimed-docs] “Using Extend via MCP | Extend Documentation”
   • [probe] “official MCP server documented at https://docs.extend.ai/mcp”

   proof ↗⚑ flag

4. ai-native userUse an official CLI

   weight 2 · round drawn
   Reducto✓fullprobed8/10

   Reducto ships an official CLI documented at docs.reducto.ai/cli, giving direct terminal access to parse, extract, split, classify, and edit — core document capabilities — confirmed by both docs and a probe. Missing for 10: independent/hands-on community corroboration of CLI usage and more detail on CLI-specific agentic scripting workflows.

   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto’s document capabilities: parse, extract, split, classify, and edit.”
   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto's document capabilities: parse, extract, split, classify, and edit.”
   • [probe] “official CLI documented at https://docs.reducto.ai/cli”

   proof ↗⚑ flag

   Extend✓fullprobed8/10

   Extend documents an official CLI at docs.extend.ai/cli, alongside SDKs and MCP support, indicating a first-party CLI exists for AI-native workflows. Missing for 10: no independent/hands-on confirmation of CLI usage or specific command examples beyond the docs listing page.

   • [claimed-docs] “CLI | Extend Documentation”
   • [probe] “official CLI documented at https://docs.extend.ai/cli”

   proof ↗⚑ flag

5. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   Reducto✓fullprobed9/10

   Reducto ships a documented public API (OpenAPI spec confirmed at openapi.json) covering parse, extract, split, classify, edit/fill, async job workflows, webhooks, and configuration options, plus SDK code examples throughout the docs and a quickstart. It also has a CLI and MCP server that let agents drive the same capabilities programmatically. Missing for 10: independent hands-on developer confirmation of API ergonomics/completeness beyond first-party docs.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”
   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto’s document capabilities: parse, extract, split, classify, and edit.”
   • [probe] “official MCP server documented at https://docs.reducto.ai/mcp-server”
   • [claimed-docs] “Multi-step workflows that chain classification, parsing, extraction, and editing into single API calls”

   proof ↗⚑ flag

   Extend✓fullcommunity9/10

   Extend provides a documented public API with authentication, endpoints (upload, extract, classify, split, parse), sync/async modes, SDKs, CLI, and a dedicated API Quickstart, giving AI-native users a clear path to drive the product programmatically. Community feedback confirms real-world usage of the product's data extraction capabilities. missing for 10: independent third-party validation specifically of API robustness/rate limits beyond docs.

   • [claimed-docs] “Authenticate a request”
   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “This endpoint accepts file contents and registers them as a File in Extend, which can be used for running workflows, creating evaluation set…”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “CLI | Extend Documentation”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

6. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Reducto—none0/10

   No evidence in the pack of scoped/least-privilege API key management, role-based credential issuance, or agent-specific token scoping — only general security/compliance policies (ZDR, SOC2, HIPAA) and enterprise deployment options are documented, none of which address credential scoping for agents.

   ⚑ flag

   Extend—none0/10

   Evidence only shows basic API-key authentication (extend-docs-4) and a separate test-vs-production API key for sandboxing (extend-docs-38), but nothing about issuing scoped, role/permission-limited, or least-privilege credentials for individual agents (e.g., granular scopes, RBAC, per-agent key restrictions).

   ⚑ flag

7. ai-native userBuild against official SDKs

   weight 2 · round to Extend
   Reducto~partialprobed6/10

   Docs show code examples using a client SDK (e.g. client.parse.run, client.extract, run_job) plus an OpenAPI spec, CLI, and MCP server, indicating official SDK-style access for building integrations. However, there is no explicit documentation naming specific language SDKs (e.g. Python/TypeScript packages), install instructions, or versioning/release notes, and no independent developer corroboration of SDK usability. Missing for 10: named official SDK packages with install/versioning docs, independent hands-on validation of SDK usage.

   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://docs.reducto.ai/cli”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Extend documents an official Python SDK (pip install extend-ai), a dedicated SDKs docs page, API quickstart, authentication docs, and API reference endpoints, giving AI-native developers a clear official SDK path alongside CLI and MCP integrations. missing for 10: independent/hands-on developer corroboration of SDK usage beyond first-party docs, and multi-language SDK coverage details.

   • [claimed-docs] “pip install extend-ai”
   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “Authenticate a request”
   • [claimed-docs] “This endpoint accepts file contents and registers them as a File in Extend, which can be used for running workflows, creating evaluation set…”

   proof ↗⚑ flag

8. ai-native userSubscribe to events via webhooks

   weight 2 · round to Reducto
   Reducto✓fullclaimed8/10

   Reducto documents webhook subscriptions via Svix integration, with cryptographic signing, retries, and a delivery dashboard accessible from Reducto Studio, enabling async job completion notifications for agentic workflows. Missing for 10: independent hands-on confirmation of webhook reliability and no detail on event/payload schema variety.

   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”

   proof ↗⚑ flag

   Extend✓fullclaimed7/10

   Extend has a dedicated 'Webhook Configuration' documentation page and separately confirms webhooks fire on production runs (and are suppressed in test mode), showing an established webhook subscription mechanism for async events. Missing for 10: specifics on supported event types, payload schema, and independent/hands-on verification of webhook reliability.

   • [claimed-docs] “Webhook Configuration | Extend Documentation”
   • [claimed-docs] “Runs made with a test API key never touch production data and never fire production webhooks”
   • [claimed-docs] “Async Processing | Extend Documentation”

   proof ↗⚑ flag

Agentic features

1. ai-native userGet AI-generated insights and suggestions from my data inside the product

   weight 2 · round to Extend
   Reducto—none0/10

   Reducto's evidence covers structured document parsing, extraction, classification, and agentic prompting for extraction tasks, but nothing shows the product generating its own insights, summaries, or proactive suggestions from processed data — it only returns what the user's schema/prompt explicitly asks for. No evidence of an insights/recommendations layer inside Reducto Studio or the API.

   ⚑ flag

   Extend~partialclaimed6/10

   Extend's core AI extraction/classification pipeline generates structured data, per-field confidence scores that suggest which values to trust vs. route to human review, and citations pointing back to source locations — a form of AI-generated insight/suggestion from document data. However, there's no evidence of broader analytical insights, trend/summary generation, or proactive suggestions beyond the extraction/review-routing use case. Missing for 10: dedicated 'insights' or summary-generation feature, evidence of proactive suggestions beyond confidence-based review routing, and independent corroboration of this specific capability.

   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [claimed-docs] “Agentic OCR”
   • [claimed-docs] “Parse, extract, and split your hardest documents with unmatched accuracy. Ship reliable document agents in minutes, not months.”
   • [claimed-docs] “Confidence Scores | Extend Documentation”

   proof ↗⚑ flag

2. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Extend
   Reducto~partialclaimed5/10

   Reducto supports async job processing (run_job returns a job ID) plus Svix webhook callbacks for delivery/retry, and Studio pipelines can be deployed as a callable Pipeline ID chaining classify/parse/extract/edit steps — this enables background, non-blocking processing rather than requiring synchronous polling. However, there's no evidence of autonomous triggers (e.g., scheduled jobs, folder/inbox watchers, event-driven ingestion) that would let an automation run on its own without an explicit API call to kick it off. Missing for 10: scheduled/cron-style automation, autonomous trigger mechanisms (e.g., watch new documents arriving), and independent confirmation that pipelines run unattended in production.

   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “client.parse.run_job() ... POST /parse_async ... Job ID (returns immediately)”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”
   • [claimed-docs] “Multi-step workflows that chain classification, parsing, extraction, and editing into single API calls”
   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend's Workflows feature (versioned, deployable pipelines) combined with async processing and webhook callbacks lets document-processing tasks run without a user waiting live, which is the core of 'background automation' — evidenced by extend-docs-19, extend-docs-12, extend-docs-13, extend-docs-37, extend-docs-43. However there's no explicit mention of scheduling/triggers (e.g., cron, event subscriptions beyond webhooks) or autonomous multi-step agent loops running unattended over time, so the 'autonomous' framing is only partially substantiated. Missing for 10: explicit scheduling/trigger mechanisms, evidence of long-running unattended agent loops, and independent confirmation that workflows execute fully autonomously without manual intervention.

   • [claimed-docs] “Workflows”
   • [claimed-docs] “Webhook Configuration | Extend Documentation”
   • [claimed-docs] “Async Processing | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “A workflow has one editable draft and any number of immutable deployed versions.”

   proof ↗⚑ flag

3. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   Reducto~partialprobed6/10

   Reducto supports natural-language commands for defining schemas, split sections, classification categories, and edit instructions across Extract, Split, Classify, and Fill/Edit endpoints, and its MCP server lets AI agents invoke these capabilities conversationally as part of a reasoning loop. However, this is scoped to document-processing operations rather than general product operation (e.g., no evidence of natural-language control over account/admin settings, billing, or deployment config), and there's no independent hands-on verification of the MCP/CLI natural-language experience. missing for 10: broader natural-language control beyond document parsing/extraction tasks, independent corroboration of the MCP/agentic natural-language workflow actually working well in practice.

   • [claimed-docs] “Extract pulls specific fields from documents as structured JSON. You define a schema describing the data you need, and Reducto returns value…”
   • [claimed-docs] “Split identifies which pages contain which sections of a document. You describe sections in natural language, and Reducto returns the page n…”
   • [claimed-docs] “Route documents by type before processing. Define categories in natural language.”
   • [claimed-docs] “Fill PDF forms and modify DOCX files programmatically with natural language instructions.”
   • [claimed-docs] “Once installed, agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can clas…”
   • [claimed-docs] “agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can classify, parse, ext…”
   • [claimed-docs] “Classify determines what kind of document you are looking at before any downstream processing begins. You specify categories with natural la…”
   • [probe] “official MCP server documented at https://docs.reducto.ai/mcp-server”

   proof ↗⚑ flag

   Extend~partialprobed6/10

   Extend documents an official MCP server and Agent Quickstart, which allow AI agents/assistants to drive the platform via natural-language tool calls rather than raw API calls, and it also ships a CLI for programmatic control. However, there is no first-party example or independent corroboration showing an end-user issuing free-form natural-language commands and Extend interpreting/executing them directly. Missing for 10: concrete examples of natural-language command execution, independent/hands-on validation of the MCP/agent workflow.

   • [claimed-docs] “Using Extend via MCP | Extend Documentation”
   • [claimed-docs] “Agent Quickstart | Extend Documentation”
   • [probe] “official MCP server documented at https://docs.extend.ai/mcp”
   • [probe] “official CLI documented at https://docs.extend.ai/cli”
   • [claimed-docs] “CLI | Extend Documentation”

   proof ↗⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round to Reducto
   Reducto~partialprobed5/10

   Reducto's docs include code snippets (quickstart, chunking, async job examples) and an openapi.json spec, suggesting a structured API reference, but there's no evidence of an interactive, in-browser 'try it now' or runnable-example experience typical of tools like Swagger UI or Postman-style docs. Missing for 10: explicit interactive/try-it API console, evidence of live code execution in docs, independent confirmation of interactivity.

   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.reducto.ai/llms.txt # Reducto - [Overview](https://docs.reducto.ai/overview.md): The agentic docum…”

   proof ↗⚑ flag

   Extend~partialclaimed4/10

   Extend has a documented API reference (authentication, endpoints like upload-file, quickstarts) and SDKs/CLI, but there's no evidence of an interactive 'try it' console or runnable code examples embedded in the docs. missing for 10: explicit interactive API playground, in-browser runnable examples, live request/response testing.

   • [claimed-docs] “Authenticate a request”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “This endpoint accepts file contents and registers them as a File in Extend, which can be used for running workflows, creating evaluation set…”
   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “CLI | Extend Documentation”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round to Reducto
   Reducto✓fullprobed9/10

   A probe confirms a live OpenAPI spec is downloadable at https://docs.reducto.ai/openapi.json returning HTTP 200 with an 'openapi' key, directly satisfying the machine-readable API spec requirement. Missing for 10: no independent/community confirmation of spec usage or completeness beyond the probe check.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Extend—none0/10

   Extend documents a full API reference, authentication, and multiple endpoints, but no evidence pack item mentions a downloadable OpenAPI/Swagger spec or any machine-readable API definition file. missing for 10: OpenAPI/Swagger spec file, documented spec download link, machine-readable API schema.

   ⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Extend
   Reducto—none0/10

   Reducto's docs describe Studio (build/test workflows on real documents before deploying as a Pipeline ID) and a quickstart sample PDF, but there is no evidence of a distinct sandbox/test environment, test API keys, or staging mode explicitly separated from production data. Missing for 10: dedicated sandbox/test mode or environment, sample/test data set isolated from production, documentation contrasting sandbox vs production usage.

   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”
   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Extend explicitly documents a test environment where runs made with a test API key never touch production data and never fire production webhooks, directly matching the sandbox story. missing for 10: no independent/hands-on corroboration of the test-key isolation beyond first-party docs, and no detail on how thoroughly sandbox data mirrors production for realistic testing.

   • [claimed-docs] “Runs made with a test API key never touch production data and never fire production webhooks”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Reducto—none0/10

   Evidence shows an OpenAPI spec and API docs exist, but there is no mention of API versioning scheme or a documented deprecation policy anywhere in the pack. missing for 10: versioning scheme documentation, explicit deprecation policy, changelog/migration guides.

   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Extend—none0/10

   Evidence shows versioning for internal artifacts (processors, workflows, base models) but no documentation of API endpoint versioning (e.g., v1/v2 paths) or any deprecation/sunset policy for the REST API/SDK itself.

   • [claimed-docs] “the thing you version and iterate on in Extend Studio, and the unit you evaluate and optimize”
   • [claimed-docs] “A workflow has one editable draft and any number of immutable deployed versions.”
   • [claimed-docs] “Base model versions are Extend's underlying model releases, selected with the baseVersion config field.”
   • [claimed-docs] “Authenticate a request”
   • [claimed-docs] “API Quickstart | Extend Documentation”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round drawn
   Reducto~partialcommunity6/10

   Reducto documents a batch queue with discounted pricing for Parse/Extract jobs, async job submission (run_job), a CLI for scripting over many files, and webhooks for tracking completions — all of which support processing many documents in bulk. However, there is no explicit documentation of a single bulk/batch API call accepting a list of many items, nor independent evidence of throughput/scale at high volume (one community report even notes struggles with long/large documents). missing for 10: explicit bulk-submission API (array of documents in one call), documented scale/performance benchmarks for large batches, independent confirmation of reliable bulk throughput.

   • [claimed-docs] “Parse and Extract jobs submitted through the batch queue receive a 20% usage discount.”
   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto’s document capabilities: parse, extract, split, classify, and edit.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend supports batch-like operations via multifile extraction (run over a collection of files with shared context), async processing endpoints for extract/classify/split/parse/edit, and evaluation set runs that score a processor against every item in a set — all pointing to bulk/batch capability across many documents. However, there's no explicit documentation of a bulk API endpoint for arbitrary large-scale item batches, rate limits/throughput guarantees, or dedicated bulk-operations tooling beyond these features. missing for 10: dedicated bulk-operation API/endpoint documentation, throughput/scale guarantees, independent hands-on evidence of large-scale bulk runs.

   • [claimed-docs] “Multifile extraction lets you run a single extraction over a collection of files with a shared context.”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “An evaluation set run scores one processor version against every item in an evaluation set and returns aggregate metrics”
   • [claimed-docs] “Async Processing | Extend Documentation”

   proof ↗⚑ flag

2. ai-native userDefine rules that trigger actions automatically on events

   weight 3 · round to Extend
   Reducto~partialclaimed4/10

   Reducto supports webhook-based event notifications on job completion (Svix, with retries) and conditional routing/classification that lets downstream steps branch by document type, plus chainable multi-step workflows — these are event/condition-triggered automations. However there is no documented general-purpose 'rules engine' where a user defines arbitrary if-this-then-that triggers/actions; the automation is limited to job-lifecycle webhooks and document-type routing within Reducto's own pipeline. Missing for 10: a user-facing rules/trigger configuration UI or API for arbitrary event-action bindings, examples of rules beyond routing/classification, and independent confirmation of this automation working in production.

   • [claimed-docs] “Route documents by type before processing. Define categories in natural language.”
   • [claimed-docs] “Multi-step workflows that chain classification, parsing, extraction, and editing into single API calls”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”
   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”

   proof ↗⚑ flag

   Extend~partialclaimed5/10

   Extend supports webhooks to fire on processing events and has a Workflows system with versioning, which lets users react to events (e.g., file processed, extraction complete) with automated actions, but there's no documented general-purpose rule engine for defining custom trigger-condition-action logic across arbitrary events. Missing for 10: an explicit rules/conditions builder beyond simple webhook firing, and evidence of triggering on non-document events.

   • [claimed-docs] “Webhook Configuration | Extend Documentation”
   • [claimed-docs] “Workflows”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “A workflow has one editable draft and any number of immutable deployed versions.”

   proof ↗⚑ flag

3. ai-native userSchedule recurring jobs or workflows

   weight 2 · round drawn
   Reducto—none0/10

   Reducto offers async job execution, webhooks, and pipelines, but there is no evidence of a scheduler or cron-like mechanism for recurring/automated jobs triggered on a time basis. Missing for 10: any documentation of scheduled/recurring job triggers, cron syntax, or periodic workflow execution.

   ⚑ flag

   Extend—none0/10

   Extend documents workflows, webhooks, and async processing, but no evidence of a scheduling mechanism (cron-like triggers, recurring runs) for workflows or jobs. Missing for 10: any mention of scheduled/recurring workflow triggers, cron configuration, or time-based automation.

   • [claimed-docs] “Workflows”
   • [claimed-docs] “Webhook Configuration | Extend Documentation”
   • [claimed-docs] “Async Processing | Extend Documentation”
   • [claimed-docs] “A workflow has one editable draft and any number of immutable deployed versions.”

   proof ↗⚑ flag

Compliance

1. data engineerUploaded documents get zero-retention handling with SOC 2 and HIPAA options, so I can process contracts and medical records

   weight 3 · round drawn
   Reducto✓fullclaimed8/10

   Reducto documents a Zero Data Retention policy (24h expiry) for Growth+ tiers, completed SOC 2 Type I/II, and a HIPAA-compliant pipeline with BAA available for Growth/Enterprise tiers, plus VPC/on-prem deployment options for sensitive data like contracts and medical records. missing for 10: independent/third-party audit confirmation or customer case study validating these compliance claims in practice, and clarity on whether ZDR/HIPAA apply below Growth tier.

   • [claimed-docs] “Security policies, data retention: "We have a Zero Data Retention policy (ZDR) for users on our 'Growth' tier and above, meaning all data su…”
   • [claimed-docs] “Security policies, compliance: "SOC 2 Type 2: We have completed our SOC 2 Type I and Type II process." and "HIPAA Compliance: We currently o…”
   • [claimed-docs] “We have a Zero Data Retention policy (ZDR) for users on our “Growth” tier and above, meaning all data submitted via API is set to expire wit…”
   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “Customers can choose between a dedicated VPC deployment, a hybrid VPC model, and a fully hosted SaaS model, balancing control, cost, and com…”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Extend's docs explicitly document SOC 2 Type II alignment, HIPAA (with BAA add-on) and GDPR compliance, plus configurable zero data retention (including for AI subprocessors) at the workspace level, directly matching the data-engineer's need to process contracts and medical records securely. Missing for 10: independent third-party audit confirmation or hands-on customer verification of ZDR/HIPAA in production use beyond vendor docs.

   • [claimed-docs] “Compliance: "We maintain controls aligned with SOC 2 Type II requirements. Reports and security documentation are available in the Trust Cen…”
   • [claimed-docs] “Data handling, Zero Data retention: "We maintain automatic data-retention policies that vary in default length by billing tier and can be co…”
   • [claimed-docs] “Compliance | Extend Documentation”
   • [claimed-docs] “Data Handling | Extend Documentation”
   • [claimed-docs] “Zero data retention”
   • [claimed-docs] “HIPAA Compliance and BAA add-on”

   proof ↗⚑ flag

Deployment

1. data engineerRun the extraction stack in my own VPC or fully self-hosted when documents can't leave my infrastructure

   weight 2 · round to Reducto
   Reducto✓fullclaimed8/10

   Docs explicitly describe dedicated VPC, hybrid VPC, and fully hosted deployment options, with data/storage residing in customer VPC and only ephemeral processing on Reducto's GPU infra, directly matching the self-hosted/VPC-for-sensitive-documents requirement. Missing for 10: independent/hands-on verification of an actual on-prem deployment and more detail on air-gapped or fully offline operation beyond the docs' description.

   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “Customers can choose between a dedicated VPC deployment, a hybrid VPC model, and a fully hosted SaaS model, balancing control, cost, and com…”
   • [claimed-docs] “Security policies, compliance: "SOC 2 Type 2: We have completed our SOC 2 Type I and Type II process." and "HIPAA Compliance: We currently o…”

   proof ↗⚑ flag

   Extend~partialclaimed5/10

   Pricing page explicitly lists 'BYOC (your VPC) and Hybrid deployments' as an offering, indicating self-hosted/VPC deployment options exist, but there is no dedicated documentation detailing setup, architecture, or infrastructure requirements for such deployments, and no independent/hands-on confirmation. missing for 10: detailed self-hosting/VPC deployment docs, architecture guides, independent confirmation of BYOC actually working, clarity on which components run in customer VPC vs SaaS.

   • [claimed-docs] “BYOC (your VPC) and Hybrid deployments”

   proof ↗⚑ flag

Formats

1. developerOne API handles my whole document mix — PDF, DOCX, PPTX, XLSX, HTML, images, email — without per-format plumbing

   weight 2 · round to Extend
   Reducto~partialclaimed4/10

   Reducto's Parse/Extract/Classify docs describe a single API that ingests "documents" generically and Fill/Edit explicitly covers PDF forms and DOCX, but the evidence pack never explicitly confirms PPTX, XLSX, HTML, image, or email ingestion as first-class formats through the same endpoint, so the full "whole document mix" claim is only inferred, not documented. Missing for 10: explicit docs/examples confirming PPTX, XLSX, HTML, raw image, and email (.eml/.msg) inputs handled by the same Parse/Extract calls, and any independent/hands-on confirmation of cross-format consistency.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Fill PDF forms and modify DOCX files programmatically with natural language instructions.”
   • [claimed-docs] “Classify determines what kind of document you are looking at before any downstream processing begins. You specify categories with natural la…”
   • [claimed-docs] “For files larger than 100MB, use the presigned URL method. This uploads directly to cloud storage, bypassing the 100MB limit of the standard…”
   • [claimed-docs] “Files over 100MB? Use the presigned URL method which supports files up to 5GB.”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend documents a single API surface (extract, classify, split, parse, edit) that ingests uploaded files and auto-converts Word/PowerPoint to PDF, and maintains a dedicated 'Supported File Types' doc, showing a unified pipeline for mixed document types. However, the evidence never explicitly confirms XLSX, HTML, or email format support, or shows a worked example spanning all these formats without extra plumbing. Missing for 10: explicit confirmation of XLSX/HTML/email support, and hands-on proof of one-call handling across all listed formats.

   • [claimed-docs] “Supported File Types | Extend Documentation”
   • [claimed-docs] “This endpoint accepts file contents and registers them as a File in Extend, which can be used for running workflows, creating evaluation set…”
   • [claimed-docs] “If an uploaded file is detected as a Word or PowerPoint document, it will be automatically converted to a PDF.”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”

   proof ↗⚑ flag

Scale limits

1. data engineerThousand-page documents and multi-gigabyte files process reliably without timeouts or silent truncation

   weight 2 · round to Extend
   Reducto!disputedcontradicted4/10

   Reducto documents async job processing, presigned uploads up to 5GB, chunking controls, and batch queuing that suggest large-file/long-document support (reducto-docs-9, 16, 21, 25), but a concrete hands-on community report states Reducto 'did struggle with long documents' on 300+ page financial documents, prompting the reporter to switch to another model for accuracy/speed (reducto-comm-7). This is a direct, specific counter-example to the reliability claim for thousand-page-scale documents, not just general skepticism. Missing for 10: no vendor benchmark or case study specifically addressing multi-thousand-page reliability, no rebuttal or fix documented for the reported struggle, no independent confirmation of timeout-free processing at that scale.

   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “Files over 100MB? Use the presigned URL method which supports files up to 5GB.”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “Parse and Extract jobs submitted through the batch queue receive a 20% usage discount.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend explicitly advertises 2,000+ page support, async processing for all endpoints (to avoid timeouts on long jobs), and automatic Word/PPT-to-PDF conversion, plus a test environment guide—strong first-party evidence targeting large-document reliability. However, there's no explicit mention of multi-gigabyte file size limits, chunking/truncation safeguards for extremely large files, or independent/hands-on confirmation that thousand-page or multi-GB documents process without silent truncation. missing for 10: independent verification of large-file handling, explicit multi-GB file size limits/support, evidence of no silent truncation on very large inputs.

   • [claimed-docs] “2,000+ page support”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “If an uploaded file is detected as a Word or PowerPoint document, it will be automatically converted to a PDF.”
   • [claimed-docs] “Runs made with a test API key never touch production data and never fire production webhooks”
   • [claimed-docs] “Async Processing | Extend Documentation”

   proof ↗⚑ flag

Languages

1. developerNon-English documents — including CJK and right-to-left scripts — parse with the same fidelity as English

   weight 2 · round drawn
   Reducto—none0/10

   No evidence in the pack addresses multilingual or CJK/RTL parsing fidelity at all — docs describe parse/extract/split/classify features and English-language sample workflows, but there's no mention of language support, script coverage, or benchmarks for non-English documents. Missing for 10: any mention of CJK/RTL script support, multilingual accuracy benchmarks, or language-specific documentation/testimonials.

   ⚑ flag

   Extend—none0/10

   No evidence in the pack addresses multilingual or CJK/RTL document parsing fidelity; all citations cover general parsing, extraction, SDKs, compliance, and pricing without mentioning language coverage.

   ⚑ flag

Ocr

1. developerHandwritten fields and annotations are recognized and extracted, flagged with confidence when uncertain

   weight 1 · round to Extend
   Reducto—none0/10

   Reducto's docs describe Parse/Extract/Split/Classify with tables, layout, citations (bounding boxes) for extracted values, but nowhere mention handwritten text/annotation recognition or confidence scores flagging uncertain extractions. The axis clearly applies to a document-parsing product, but no evidence supports this specific capability.

   ⚑ flag

   Extend~partialclaimed6/10

   Extend explicitly advertises handwriting detection ('Detect tables, checkboxes, images, and handwriting') and 'Agentic OCR' as pricing features, plus a documented Confidence Scores mechanism that lets low-confidence extractions route to human review. However, there is no direct documentation showing handwritten field values are specifically flagged with lower confidence or examples of handwriting-specific extraction accuracy/behavior. Missing for 10: dedicated documentation or examples of handwriting-specific extraction accuracy, confidence flagging tied specifically to handwritten content, and independent/hands-on verification of handwriting OCR quality.

   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “Agentic OCR”
   • [claimed-docs] “Confidence Scores | Extend Documentation”
   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”

   proof ↗⚑ flag

2. developerScanned and photographed documents OCR accurately — skewed pages, stamps, low quality scans included

   weight 3 · round to Extend
   Reducto—none0/10

   The evidence describes Reducto's general Parse/Extract capabilities (structured JSON, tables, layout) but contains no documentation or evidence addressing OCR performance specifically on scanned/photographed documents, skewed pages, stamps, or low-quality scans. Community mentions are limited to accuracy comparisons with Mathpix and struggles with long documents, not scan-quality robustness, so there's no positive evidence this specific capability is delivered.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Parse is Reducto's foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [community] “Mathpix founder: 'Reducto does cool stuff on post processing (and other input formats), but some people have told me Mathpix is better at ju…”
   • [community] “Ask HN post: 'Looking for high-quality alternatives to Reducto for document parsing. Need strong table extraction + structured JSON output. …”

   proof ↗⚑ flag

   Extend~partialcommunity6/10

   Extend markets itself as handling 'your hardest documents with unmatched accuracy' with agentic OCR, handwriting/table/checkbox detection, and confidence-based routing to human review, and a year-long HN user reports being happy with extraction accuracy. However no evidence specifically addresses skewed pages, stamps, or low-quality scan handling — these document conditions are never explicitly mentioned in docs or community feedback. Missing for 10: explicit documentation or benchmarks on skew correction, stamp/seal recognition, and degraded scan quality performance, plus independent hands-on testing on such documents.

   • [claimed-docs] “Parse, extract, and split your hardest documents with unmatched accuracy. Ship reliable document agents in minutes, not months.”
   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “Agentic OCR”
   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Reducto
   Reducto~partialprobed7/10

   Reducto's core document operations (parse, extract, split, classify, edit, route) are all exposed as API endpoints, CLI commands, and MCP tools, and Studio-built pipelines can be deployed as a Pipeline ID callable from code, showing strong UI/API parity for document workflows. However, there's no explicit confirmation that every UI feature (e.g., webhook dashboard management, account/billing settings) is fully API-accessible, so parity is inferred rather than exhaustively documented. Missing for 10: explicit 1:1 feature-parity statement, evidence that Studio-only features (webhook dashboard, billing) are API-manageable, independent verification of parity claims.

   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”
   • [claimed-docs] “The Reducto CLI gives you direct terminal access to Reducto’s document capabilities: parse, extract, split, classify, and edit.”
   • [claimed-docs] “Once installed, agents in Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf, or any other Model Context Protocol client can clas…”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”
   • [probe] “official CLI documented at https://docs.reducto.ai/cli”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend documents a broad API/SDK/CLI/MCP surface covering the same core objects seen in its UI (processors, workflows with versioning, evaluation sets, extraction/classification/splitting, file upload) suggesting close UI-API parity, but there is no explicit statement of full parity and UI-centric tools like Studio's interactive testing and the Composer & Review Agent (human-in-the-loop) are not shown to have a documented equivalent API path. missing for 10: explicit parity claim/documentation, API equivalents for Studio interactive iteration and Composer/Review Agent workflows, independent confirmation of full coverage.

   • [claimed-docs] “CLI | Extend Documentation”
   • [claimed-docs] “Using Extend via MCP | Extend Documentation”
   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “Agent Quickstart | Extend Documentation”
   • [claimed-docs] “you save that config once as a processor, give it a stable id, and reference it from then on”
   • [claimed-docs] “the thing you version and iterate on in Extend Studio, and the unit you evaluate and optimize”
   • [claimed-docs] “Evaluation sets let you test the accuracy of your document processors reliably and repeatedly.”
   • [claimed-docs] “A workflow has one editable draft and any number of immutable deployed versions.”
   • [claimed-docs] “Composer & Review Agent”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round to Reducto
   Reducto~partialclaimed5/10

   Reducto's core outputs (Parse, Extract, Split) are all structured JSON, an open format the customer fully owns, and enterprise/on-prem deployments keep data resident in the customer's own VPC — both reduce lock-in. However, there is no explicit account-level 'export all my data and leave' feature, no documented bulk-export or account-deletion/migration-out tool, and the only migration incentive documented is for switching TO Reducto, not away from it. missing for 10: an explicit data-export/account-portability feature, documentation of exporting historical job data/configs, and evidence of migrating away from Reducto.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Extract pulls specific fields from documents as structured JSON. You define a schema describing the data you need, and Reducto returns value…”
   • [claimed-docs] “Split identifies which pages contain which sections of a document. You describe sections in natural language, and Reducto returns the page n…”
   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “We have a Zero Data Retention policy (ZDR) for users on our “Growth” tier and above, meaning all data submitted via API is set to expire wit…”

   proof ↗⚑ flag

   Extend—none0/10

   Extend is a SaaS document-processing platform holding workflows, processors, evaluation sets and extracted data, so data-portability/export is a fair question, but the evidence pack contains no mention of a bulk data-export feature, open-format export of processed data/configs, or account-closure data dump — only retention/ZDR policies which describe deletion, not export.

   • [claimed-docs] “Data handling, Zero Data retention: "We maintain automatic data-retention policies that vary in default length by billing tier and can be co…”
   • [claimed-docs] “Data Handling | Extend Documentation”

   proof ↗⚑ flag

3. ai-native userSelf-host the core product

   weight 3 · round to Reducto
   Reducto~partialclaimed5/10

   Reducto offers VPC/on-prem deployment options (dedicated VPC, hybrid VPC, fully hosted SaaS) letting enterprise customers keep data and storage within their own VPC, but ephemeral processing still runs on Reducto's own GPU infrastructure rather than a fully self-hostable, open binary/container a user runs entirely on their own hardware. missing for 10: no evidence of a downloadable/open-source self-host package, no details on hybrid model's compute independence from Reducto's infra, and this option is gated behind enterprise tier rather than open to any AI-native user.

   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “Customers can choose between a dedicated VPC deployment, a hybrid VPC model, and a fully hosted SaaS model, balancing control, cost, and com…”

   proof ↗⚑ flag

   Extend~partialclaimed3/10

   Extend is a closed SaaS platform; the only evidence bearing on self-hosting is a pricing-page bullet mentioning 'BYOC (your VPC) and Hybrid deployments' as an enterprise add-on, with no documentation of installation, source availability, or a true self-managed deployment path. Missing for 10: open-source or downloadable core product, self-hosting setup docs, infrastructure requirements, and independent confirmation that customers can run it fully outside Extend's cloud.

   • [claimed-docs] “BYOC (your VPC) and Hybrid deployments”

   proof ↗⚑ flag

Evals

1. ml engineerThe vendor publishes reproducible accuracy benchmarks and I can run my own evals before committing

   weight 1 · round to Extend
   Reducto—none0/10

   No evidence of any published benchmark methodology, dataset, or reproducible accuracy numbers from Reducto; docs cover product features (parse, extract, split, etc.) but no benchmark suite or eval framework is mentioned. Community discussion even shows an open ask for third-party comparisons and accuracy complaints, but nothing about a vendor-published, reproducible eval an ML engineer could run themselves.

   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”
   • [community] “Reducto founder: 'Deep Extract is more accurate than any structured extraction product we've tried, but the approach comes with a very clear…”
   • [community] “Ask HN post: 'Looking for high-quality alternatives to Reducto for document parsing. Need strong table extraction + structured JSON output. …”

   proof ↗⚑ flag

   Extend~partialcommunity5/10

   Extend documents a full self-serve evaluation framework (evaluation sets, processors, versioning, aggregate metrics from evaluation-set runs) that lets an ML engineer run their own accuracy evals before committing, and community testimony corroborates strong extraction accuracy in practice. However there is no evidence of the vendor publishing its own reproducible benchmark numbers/datasets or accuracy reports for independent verification. Missing for 10: vendor-published benchmark results/methodology, third-party benchmark validation, public accuracy leaderboards or comparable datasets.

   • [claimed-docs] “Evaluation sets let you test the accuracy of your document processors reliably and repeatedly.”
   • [claimed-docs] “An evaluation set run scores one processor version against every item in an evaluation set and returns aggregate metrics”
   • [claimed-docs] “you save that config once as a processor, give it a stable id, and reference it from then on”
   • [claimed-docs] “the thing you version and iterate on in Extend Studio, and the unit you evaluate and optimize”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

Figures

1. ml engineerFigures and charts are extracted or described (VLM summaries, image crops) with positions traceable back to the source page

   weight 2 · round to Reducto
   Reducto~partialclaimed7/10

   Reducto's Parse endpoint extracts figures/charts as part of structured output (reducto-docs-1), agentic mode explicitly supports 'advanced chart extraction' via custom prompts (reducto-docs-13, reducto-docs-38), and Citations attach bounding-box coordinates pointing back to source text for extracted fields (reducto-docs-11, reducto-docs-22), giving page/position traceability. However there's no explicit documentation of VLM-generated figure summaries or image-crop output format, nor independent confirmation that figure/chart citations specifically resolve to accurate bounding boxes. Missing for 10: explicit VLM figure-summary output examples, documented image-crop artifacts, independent verification of figure-level citation accuracy.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “Add an entry to the `agentic` array for each scope that needs customized processing. Each entry must include a custom prompt or enable advan…”
   • [claimed-docs] “When enabled, every field includes bounding box coordinates pointing to the source text.”
   • [claimed-docs] “Use agentic processing with r-1 when your workflow needs a custom prompt for a specific content type or advanced chart extraction.”

   proof ↗⚑ flag

   Extend~partialclaimed5/10

   Extend documents citations/provenance pointing extracted field values back to exact page locations and detects tables/images/handwriting, but evidence never explicitly describes VLM-generated summaries of figures/charts or image crop extraction with bounding-box positions for visual elements specifically — citations are framed around extracted field values, not figure/chart description. missing for 10: explicit VLM-based figure/chart summarization, image crop extraction as an output artifact, and confirmation that citations/positions apply to figures/charts (not just text fields).

   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “Citations”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [claimed-docs] “Multifile extraction lets you run a single extraction over a collection of files with a shared context.”
   • [claimed-docs] “Confidence Scores | Extend Documentation”

   proof ↗⚑ flag

Layout

1. developerThe API parses complex real-world PDFs — multi-column layouts, headers, footers, footnotes — into clean, correctly ordered content

   weight 3 · round to Extend
   Reducto!disputedcontradicted5/10

   Reducto's Parse endpoint is documented to convert complex documents (multi-column, tables, figures, layout) into structured, ordered JSON, and agentic/chunking configs support handling nuanced layouts [reducto-docs-1, reducto-docs-35, reducto-docs-38]. However, hands-on community reports concretely contradict claims of universally clean, accurate parsing: one user reports Reducto 'struggled with long documents' on 300+ page financial PDFs versus a competing model, another prominent competitor's founder claims others are 'better at just getting data out of PDFs accurately,' and an Ask HN thread seeks alternatives specifically citing accuracy concerns on messy PDFs. missing for 10: independent benchmark data confirming column/header/footer/footnote ordering accuracy, and resolution of the long-document accuracy complaints.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Variable chunking splits at semantic boundaries like section headers, tables, and figures, keeping related content together while creating c…”
   • [claimed-docs] “Use agentic processing with r-1 when your workflow needs a custom prompt for a specific content type or advanced chart extraction.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”
   • [community] “Mathpix founder: 'Reducto does cool stuff on post processing (and other input formats), but some people have told me Mathpix is better at ju…”
   • [community] “Ask HN post: 'Looking for high-quality alternatives to Reducto for document parsing. Need strong table extraction + structured JSON output. …”

   proof ↗⚑ flag

   Extend~partialcommunity5/10

   Extend markets parsing as high-accuracy for 'hardest documents' with features like agentic OCR, table/checkbox/handwriting detection, and citations pointing to exact page locations, and one community user praises extraction accuracy over a year of use. However, no evidence specifically addresses multi-column layouts, headers/footers, or footnote handling/ordering. Missing for 10: explicit documentation or examples showing correct handling of multi-column reading order, header/footer exclusion, and footnote placement; independent benchmark or hands-on test of these specific layout challenges.

   • [claimed-docs] “Parse, extract, and split your hardest documents with unmatched accuracy. Ship reliable document agents in minutes, not months.”
   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “Agentic OCR”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

2. ml engineerParsed output preserves document hierarchy — headings, sections, reading order — so downstream LLMs see structure, not soup

   weight 2 · round to Reducto
   Reducto~partialcommunity7/10

   Reducto's Parse endpoint documents structured JSON output preserving text, tables, figures, layout, and formatting, and variable chunking explicitly splits at semantic boundaries like section headers to keep related content together, which speaks directly to preserving hierarchy/reading order. However, there's no explicit documentation of heading-level detection or a hierarchical/nested structure representation (e.g., section-tree output), and one community report notes struggles with long documents (300+ pages), suggesting structure fidelity may degrade at scale. Missing for 10: explicit heading/section hierarchy schema in output, independent benchmark on structure/order fidelity, and resolution of the long-document accuracy concern.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Variable chunking splits at semantic boundaries like section headers, tables, and figures, keeping related content together while creating c…”
   • [claimed-docs] “Chunking lets you control this tradeoff without post-processing the response yourself.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”

   proof ↗⚑ flag

   Extend—none0/10

   The evidence pack confirms Extend parses documents and extracts structured data (chunks, metadata) but contains no specific claim or demonstration that parsed output preserves heading hierarchy, section structure, or reading order for downstream LLM consumption.

   • [claimed-docs] “Parse runs now include a **`metadata`** object on `output`, alongside `chunks`”
   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round to Reducto
   Reducto~partialcommunity5/10

   Reducto's enterprise docs describe deployment options (dedicated VPC, hybrid VPC, fully hosted SaaS) where 'all data and storage reside in the customer's VPC,' giving some control over data location, but this is scoped to Enterprise-tier deployments rather than a self-service region-selection setting for standard AI-native users. Community feedback also notes a DPA (needed for EU residency compliance) is only available on the enterprise plan, reinforcing that residency control is gated behind higher tiers. Missing for 10: explicit region/residency selection UI or API parameter for standard/Growth tiers, documented list of supported regions/data centers, and independent verification of VPC deployment residency guarantees.

   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “Customers can choose between a dedicated VPC deployment, a hybrid VPC model, and a fully hosted SaaS model, balancing control, cost, and com…”
   • [community] “Why do you only get a data processing agreement when on the enterprise plan? It's a legal requirement for any European company.”

   proof ↗⚑ flag

   Extend—none0/10

   Evidence covers compliance (SOC2, HIPAA, GDPR), data retention/ZDR, and BYOC/Hybrid deployment options, but nowhere mentions region selection or data residency choice for storage location. missing for 10: explicit region/residency selection controls, documentation naming specific data center regions, evidence of a residency configuration option.

   • [claimed-docs] “Compliance: "We maintain controls aligned with SOC 2 Type II requirements. Reports and security documentation are available in the Trust Cen…”
   • [claimed-docs] “Data handling, Zero Data retention: "We maintain automatic data-retention policies that vary in default length by billing tier and can be co…”
   • [claimed-docs] “BYOC (your VPC) and Hybrid deployments”

   proof ↗⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Reducto~partialcommunity6/10

   Reducto documents a Zero Data Retention policy for Growth+ tiers (API data auto-deleted within 24 hours) and SOC2/HIPAA compliance, and offers VPC/on-prem deployment options that keep data within customer control — all of which support preventing data use for training. However, no evidence explicitly states a commitment not to train models on customer data, ZDR is gated behind paid tiers (not default/free), and a community comment notes DPAs (often tied to no-training commitments) are also enterprise-only. Missing for 10: explicit no-training-on-customer-data clause, ZDR/DPA availability for lower/free tiers, independent audit confirming policy adherence.

   • [claimed-docs] “Security policies, data retention: "We have a Zero Data Retention policy (ZDR) for users on our 'Growth' tier and above, meaning all data su…”
   • [claimed-docs] “Security policies, compliance: "SOC 2 Type 2: We have completed our SOC 2 Type I and Type II process." and "HIPAA Compliance: We currently o…”
   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [claimed-docs] “Customers can choose between a dedicated VPC deployment, a hybrid VPC model, and a fully hosted SaaS model, balancing control, cost, and com…”
   • [community] “Why do you only get a data processing agreement when on the enterprise plan? It's a legal requirement for any European company.”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend documents zero data retention (ZDR) options including for AI subprocessors, configurable at workspace level, plus SOC2/HIPAA/GDPR compliance controls, which addresses training-data concerns for enterprise customers. However, there's no explicit statement that customer data is never used to train Extend's own models by default, nor independent verification of this policy. Missing for 10: explicit default no-training-on-customer-data policy statement, independent audit/confirmation, and clarity on whether ZDR is opt-in/paid-tier only.

   • [claimed-docs] “Zero data retention”
   • [claimed-docs] “Data handling, Zero Data retention: "We maintain automatic data-retention policies that vary in default length by billing tier and can be co…”
   • [claimed-docs] “Compliance | Extend Documentation”
   • [claimed-docs] “Compliance: "We maintain controls aligned with SOC 2 Type II requirements. Reports and security documentation are available in the Trust Cen…”

   proof ↗⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Extend
   Reducto~partialcommunity6/10

   Reducto documents a concrete data retention/deletion policy: Zero Data Retention for Growth+ tier customers with API-submitted data auto-expiring/deleted within 24 hours (except persisted results), plus VPC/on-prem deployment options for full data control. However, ZDR is gated behind a paid tier (not default/free), there's no documented self-service deletion API or granular per-record deletion control, and a community comment flags DPA/legal-requirement gaps for lower tiers. Missing for 10: default-tier retention controls, explicit user-triggered deletion API, and independent verification of retention enforcement.

   • [claimed-docs] “We have a Zero Data Retention policy (ZDR) for users on our “Growth” tier and above, meaning all data submitted via API is set to expire wit…”
   • [claimed-docs] “Security policies, data retention: "We have a Zero Data Retention policy (ZDR) for users on our 'Growth' tier and above, meaning all data su…”
   • [claimed-docs] “All data and storage reside in the customer’s VPC, while ephemeral processing is handled by Reducto’s dedicated GPU infrastructure.”
   • [community] “Why do you only get a data processing agreement when on the enterprise plan? It's a legal requirement for any European company.”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Extend's official docs explicitly describe configurable automatic data-retention policies and zero data retention (ZDR) options, settable at the workspace level, plus GDPR-related compliance documentation; the pricing page also advertises 'Zero data retention' as a feature tier. This directly addresses retention and deletion control for AI-native users. Missing for 10: no independent/hands-on confirmation that deletion requests are actually honored, and no explicit self-service deletion API/UI documented beyond retention policy configuration.

   • [claimed-docs] “Data handling, Zero Data retention: "We maintain automatic data-retention policies that vary in default length by billing tier and can be co…”
   • [claimed-docs] “Zero data retention”
   • [claimed-docs] “Compliance | Extend Documentation”
   • [claimed-docs] “Data Handling | Extend Documentation”

   proof ↗⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Reducto—none0/10

   Evidence covers data retention (ZDR), SOC2/HIPAA compliance, and deployment options, but there is no mention of a telemetry/usage-tracking opt-out mechanism for the product itself (e.g., CLI/SDK/MCP analytics collection or an opt-out flag).

   ⚑ flag

   Extend—none0/10

   Extend documents data retention/ZDR and compliance policies, but there is no mention of telemetry or usage-tracking opt-out settings for the product itself (e.g., CLI/SDK analytics telemetry). missing for 10: any documentation of telemetry collection or an opt-out/disable-tracking mechanism.

   ⚑ flag

Chunking

1. ai-native userOutput comes pre-chunked for RAG — semantic boundaries, metadata, embedding-ready segments — not a wall of text

   weight 3 · round to Reducto
   Reducto✓fullclaimed9/10

   Reducto's Parse endpoint offers explicit variable chunking configured for embedding models, splitting at semantic boundaries (headers, tables, figures) rather than arbitrary text walls, with chunk_size controls and metadata like bounding boxes/citations attached to output — directly matching the RAG-ready chunking story. Docs show concrete API usage (chunk_mode, chunk_size) and best-practices guidance confirming semantic-boundary-aware chunking designed for embedding pipelines. Missing for 10: independent/hands-on benchmark confirming chunk quality in practice beyond vendor docs.

   • [claimed-docs] “Chunking lets you control this tradeoff without post-processing the response yourself.”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “Variable chunking splits at semantic boundaries like section headers, tables, and figures, keeping related content together while creating c…”
   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats.”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend's Parse API outputs both `chunks` and a `metadata` object, and there's a dedicated Splitting feature for breaking documents into semantic segments, which aligns with the RAG-chunking need. However, there's no explicit documentation framing this as embedding-ready or optimized for RAG pipelines, nor independent corroboration of chunk quality for downstream embedding use. Missing for 10: explicit embedding-ready format specs, RAG-specific use-case documentation, and independent/hands-on validation of chunk semantic quality.

   • [claimed-docs] “Parse runs now include a **`metadata`** object on `output`, alongside `chunks`”
   • [claimed-docs] “Splitting Overview | Extend Documentation”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”

   proof ↗⚑ flag

Output

1. ai-native userI get clean markdown/JSON designed for LLM consumption, with noise like repeated headers and page furniture stripped

   weight 2 · round to Reducto
   Reducto~partialclaimed6/10

   Reducto's Parse endpoint produces structured JSON with text, tables, figures, and layout, and offers chunking configs (variable chunking splits at semantic boundaries like headers/tables/figures, sized for embedding models), which directly targets LLM/RAG consumption. However, there is no explicit documentation of markdown output format or specific claims about stripping repeated headers/footers/page furniture noise — the evidence emphasizes structure preservation and chunking rather than noise removal. Missing for 10: explicit markdown output evidence, explicit documentation of header/footer/page-furniture deduplication, and independent/hands-on confirmation of clean LLM-ready output quality.

   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Chunking lets you control this tradeoff without post-processing the response yourself.”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “Variable chunking splits at semantic boundaries like section headers, tables, and figures, keeping related content together while creating c…”
   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats.”

   proof ↗⚑ flag

   Extend~partialclaimed3/10

   Extend's Parse feature outputs structured chunks plus a metadata object (extend-docs-3, extend-docs-9), suggesting some design toward LLM/RAG-ready output, but there is no explicit documentation claiming markdown/JSON output has repeated headers or page furniture stripped for clean LLM consumption. missing for 10: explicit claim/documentation of noise removal (headers, footers, page furniture) in parse output, and independent verification of clean output quality.

   • [claimed-docs] “Parse runs now include a **`metadata`** object on `output`, alongside `chunks`”
   • [claimed-docs] “Splitting Overview | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”

   proof ↗⚑ flag

Async

1. developerLong parses run as async jobs with status polling and completion webhooks, so my pipeline never blocks

   weight 3 · round to Reducto
   Reducto✓fullclaimed8/10

   Docs explicitly describe async job submission via run_job() returning a job ID immediately (docs-9, docs-23, docs-36), plus a production-grade Svix webhook system with signed requests, retries, and delivery dashboard (docs-10, docs-39, docs-28) for completion notifications, directly matching the non-blocking async pipeline story. Missing for 10: no explicit documentation of a status-polling endpoint/schema alongside webhooks, and no independent/hands-on confirmation of async+webhook reliability in production.

   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “client.parse.run_job() ... POST /parse_async ... Job ID (returns immediately)”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”

   proof ↗⚑ flag

   Extend✓fullclaimed7/10

   Extend's docs explicitly state every processing endpoint including parse supports both sync and async modes, and separate docs cover webhook configuration for completion notifications, matching the async-job + webhook pipeline pattern. Missing for 10: explicit mention of a status-polling endpoint/job-id retrieval flow and independent/hands-on confirmation that async+webhook flow works reliably in production.

   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “Async Processing | Extend Documentation”
   • [claimed-docs] “Webhook Configuration | Extend Documentation”

   proof ↗⚑ flag

Latency

1. developerA fast synchronous mode returns results in seconds for interactive apps, with latency documented per mode

   weight 2 · round drawn
   Reducto~partialcommunity4/10

   Docs show a default synchronous call (client.parse.run) alongside an explicit async endpoint (run_job() returning a job ID immediately) implying dual modes, and a founder comment notes a cost/latency tradeoff for agentic 'Deep Extract' vs single-pass extraction, but there is no documented per-mode latency benchmark (e.g., 'seconds') for the synchronous path. Missing for 10: explicit sync-mode latency numbers/SLA, a side-by-side latency comparison table for sync vs async, and independent hands-on confirmation of sub-second/seconds response times.

   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [community] “Reducto founder: 'Deep Extract is more accurate than any structured extraction product we've tried, but the approach comes with a very clear…”

   proof ↗⚑ flag

   Extend~partialclaimed4/10

   Docs confirm every processing endpoint supports both sync and async modes (extend-docs-37) and pricing page lists a 'Fast mode' option (extend-docs-25), implying a fast synchronous path exists, but no evidence quantifies actual latency (e.g., 'returns in X seconds') for either mode. Missing for 10: documented latency benchmarks per mode, explicit performance numbers for sync/fast mode, independent confirmation of response times.

   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “Async Processing | Extend Documentation”
   • [claimed-docs] “Fast mode”

   proof ↗⚑ flag

Scale

1. data engineerI push high-volume batches — millions of pages — with documented rate limits and predictable throughput

   weight 2 · round to Reducto
   Reducto~partialcommunity6/10

   Reducto documents async job APIs (run_job/parse_async), a discounted batch queue, Svix webhooks with retries, and presigned uploads up to 5GB, plus enterprise SLAs up to 99.99% and dedicated GPU/VPC infra — all supporting high-volume, async processing. However, no explicit documented per-minute/per-day rate limits or throughput numbers are cited, and community feedback notes struggles with very long documents (300+ pages) needing alternatives for speed/accuracy at scale. missing for 10: explicit published rate-limit tiers/throughput benchmarks, independent verification of million-page batch performance.

   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “Parse and Extract jobs submitted through the batch queue receive a 20% usage discount.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging.”
   • [claimed-docs] “Svix webhooks provide cryptographic request signing, automatic retries with exponential backoff, and a delivery dashboard for debugging. Use…”
   • [claimed-docs] “Files over 100MB? Use the presigned URL method which supports files up to 5GB.”
   • [claimed-docs] “For files larger than 100MB, use the presigned URL method. This uploads directly to cloud storage, bypassing the 100MB limit of the standard…”
   • [claimed-docs] “Enterprise customers receive contractual uptime SLAs of up to 99.99%.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”

   proof ↗⚑ flag

   Extend~partialclaimed4/10

   Extend documents async processing for all endpoints and a credit-based pricing model, which supports high-volume batch workflows, but there is no evidence of documented rate limits, throughput benchmarks, or guidance for processing millions of pages at scale. Missing for 10: explicit rate-limit documentation, throughput/SLA numbers, and batch-scale case studies or guarantees.

   • [claimed-docs] “Async Processing | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “How Credits Work | Extend Documentation”
   • [claimed-docs] “2,000+ page support”

   proof ↗⚑ flag

Playground

1. developerI drag a document into a web playground and see parse/extract results before writing any code

   weight 1 · round to Reducto
   Reducto~partialcommunity6/10

   Reducto Studio is documented as a web interface for testing parse/extract/classification workflows on real documents with a citation viewer before deploying as callable pipelines, which matches the spirit of a no-code playground. However, no evidence explicitly describes a drag-and-drop upload flow, and a community report from launch noted the Studio link (links.reducto.ai/studio) was broken with redirect errors, raising some doubt about polish/accessibility. Missing for 10: explicit drag-and-drop UI description, independent hands-on confirmation the playground works reliably today.

   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”
   • [claimed-docs] “In Reducto Studio, go to Webhooks in the sidebar. This opens your Svix dashboard directly.”
   • [community] “FYI - links.reducto.ai/studio doesn't seem to be working... ERR_TOO_MANY_REDIRECTS”

   proof ↗⚑ flag

   Extend~partialclaimed4/10

   Extend references a web-based "Studio" for iterating on processors (extend-docs-18, extend-docs-40) which implies a no-code interface for testing parse/extract, but no evidence describes a drag-and-drop upload flow or explicitly shows results appearing before any code is written. Missing for 10: explicit description or screenshot of drag-and-drop file upload in Studio, confirmation that parse/extract results appear instantly without any coding, and independent/hands-on confirmation of this playground UX.

   • [claimed-docs] “Studio and Evals”
   • [claimed-docs] “the thing you version and iterate on in Extend Studio, and the unit you evaluate and optimize”
   • [claimed-docs] “you save that config once as a processor, give it a stable id, and reference it from then on”

   proof ↗⚑ flag

Sdks

1. developerOfficial typed SDKs for Python and TypeScript cover the full API — parse, extract, jobs — with sensible defaults

   weight 3 · round to Extend
   Reducto~partialprobed4/10

   Docs and quickstart show a Python client (client.parse.run, client.parse.run_job(), extract usage) covering parse, extract, and async jobs, suggesting a functional Python SDK with reasonable defaults. However, there is no evidence at all of a TypeScript/JS SDK — all code samples are Python only — so the 'Python and TypeScript' claim is only half-supported. Missing for 10: TypeScript SDK existence/usage examples, explicit documentation of SDK method signatures/defaults across all endpoints (split, classify, edit), and independent developer corroboration of DX quality.

   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [claimed-docs] “result = client.parse.run( input=upload.file_id, retrieval={ "chunking": { "chunk_mode": "variable", …”
   • [claimed-docs] “`run_job()` Calls async endpoint, returns job ID”
   • [claimed-docs] “`run_job()` | Calls async endpoint, returns job ID”
   • [claimed-docs] “client.parse.run_job() ... POST /parse_async ... Job ID (returns immediately)”
   • [probe] “PROBE openapi: HTTP 200 at https://docs.reducto.ai/openapi.json — contains "openapi" key”

   proof ↗⚑ flag

   Extend~partialclaimed6/10

   Extend has a dedicated SDKs docs page and a Python package (pip install extend-ai) referenced, plus full API coverage across parse/extract/split/classify/jobs implied by endpoint docs, suggesting SDKs wrap the full API. However, there's no explicit confirmation of a TypeScript SDK, no code samples showing typed usage or sensible defaults, and no independent/community corroboration of SDK quality or completeness. Missing for 10: explicit TypeScript SDK evidence, typed code examples demonstrating parse/extract/jobs coverage, and independent developer confirmation of SDK ergonomics/defaults.

   • [claimed-docs] “SDKs | Extend Documentation”
   • [claimed-docs] “pip install extend-ai”
   • [claimed-docs] “API Quickstart | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”

   proof ↗⚑ flag

Grounding

1. ai-native userEvery extracted field carries provenance — page number, bounding box, source snippet — so agents can cite and humans can verify

   weight 2 · round drawn
   Reducto✓fullclaimed8/10

   Reducto's Extract citations feature documents bounding box coordinates pointing to source text for every extracted field, directly matching the provenance story; page-level references also come from Split. missing for 10: no explicit mention of a 'source snippet' text field alongside bounding box/page, and no independent/hands-on confirmation of citation accuracy in the community evidence.

   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “When enabled, every field includes bounding box coordinates pointing to the source text.”
   • [claimed-docs] “Extract pulls specific fields from documents as structured JSON. You define a schema describing the data you need, and Reducto returns value…”
   • [claimed-docs] “You describe sections in natural language, and Reducto returns the page numbers where each section lives.”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Docs explicitly describe per-field citations pointing back to the exact spot on the page, confidence scores for trust/routing decisions, and multifile citations/file provenance, directly matching the provenance story (page/location + source snippet). missing for 10: no explicit mention of bounding-box coordinates format, and no independent/hands-on verification of citation accuracy beyond vendor docs.

   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “Multifile extraction lets you run a single extraction over a collection of files with a shared context.”
   • [claimed-docs] “Confidence Scores | Extend Documentation”

   proof ↗⚑ flag

Review

1. data engineerExtractions carry calibrated confidence scores with a human-in-the-loop review path for low-confidence fields

   weight 2 · round to Extend
   Reducto—none0/10

   Evidence shows citations/bounding boxes for traceability and a Studio citation viewer for testing, but no mention anywhere of calibrated confidence scores per field or a structured human-in-the-loop review workflow for low-confidence extractions. Missing for 10: confidence score output, review/approval UI or workflow triggered by confidence thresholds, any documentation of HITL review path.

   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “When enabled, every field includes bounding box coordinates pointing to the source text.”
   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”

   proof ↗⚑ flag

   Extend✓fullclaimed8/10

   Extend has a dedicated Confidence Scores doc explicitly stating per-field confidence values let users 'trust high-confidence values automatically and route the rest to human review,' plus a Composer & Review Agent product feature and citations pointing to exact source locations for verification. This directly matches the calibrated confidence + human-in-the-loop review story. Missing for 10: independent/hands-on evidence validating that confidence scores are well-calibrated in practice and more detail on the review workflow UI itself.

   • [claimed-docs] “Confidence Scores | Extend Documentation”
   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [claimed-docs] “Composer & Review Agent”

   proof ↗⚑ flag

Schemas

1. developerI supply a JSON schema and get back validated structured fields extracted from the document

   weight 3 · round to Extend
   Reducto✓fullcommunity8/10

   Reducto's Extract endpoint explicitly takes a user-defined JSON schema and returns values matching that schema, with citations/bounding boxes for validation of source location, documented via docs and quickstart-style API calls. Some community feedback notes accuracy struggles on very long/messy documents, but this doesn't contradict the core schema-driven extraction capability itself. Missing for 10: independent hands-on validation of extracted field accuracy/schema conformance beyond vendor docs, and explicit mention of validation/error-handling for malformed schema responses.

   • [claimed-docs] “Extract pulls specific fields from documents as structured JSON. You define a schema describing the data you need, and Reducto returns value…”
   • [claimed-docs] “You define a schema describing the data you need, and Reducto returns values matching that schema”
   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “When enabled, every field includes bounding box coordinates pointing to the source text.”
   • [claimed-docs] “result = client.parse.run(input="https://cdn.reducto.ai/samples/fidelity-example.pdf")”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”
   • [community] “Reducto founder: 'Deep Extract is more accurate than any structured extraction product we've tried, but the approach comes with a very clear…”

   proof ↗⚑ flag

   Extend✓fullcommunity9/10

   Extend has dedicated docs for defining an Extraction Schema, returns structured fields with per-field confidence scores and citations pointing to source location, and supports sync/async extraction endpoints; community feedback corroborates strong extraction accuracy. Missing for 10: independent third-party benchmarking of schema-conformance accuracy beyond a single HN endorsement.

   • [claimed-docs] “Extraction Schema | Extend Documentation”
   • [claimed-docs] “Confidence Scores | Extend Documentation”
   • [claimed-docs] “Use them to trust high-confidence values automatically and route the rest to human review.”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

Splitting

1. data engineerMulti-document packets are classified and split automatically — one upload, per-document results

   weight 2 · round to Extend
   Reducto~partialclaimed6/10

   Reducto documents the building blocks needed for this story — Classify to route document types (reducto-docs-4, reducto-docs-30), Split to locate document/section boundaries by natural-language description (reducto-docs-3, reducto-docs-20), and multi-step Workflows/Studio pipelines that chain classification, parsing and extraction into a single API call or Pipeline ID (reducto-docs-6, reducto-docs-34). However, there is no explicit end-to-end example or case study showing a single multi-document packet upload being auto-split into distinct documents with separate per-document extraction results returned — the pieces exist but the composed 'one upload, per-document output' workflow is not directly demonstrated. Missing for 10: a concrete documented example/tutorial of packet splitting into per-document results, and any independent/community corroboration of this specific workflow.

   • [claimed-docs] “Route documents by type before processing. Define categories in natural language.”
   • [claimed-docs] “Classify determines what kind of document you are looking at before any downstream processing begins. You specify categories with natural la…”
   • [claimed-docs] “Split identifies which pages contain which sections of a document. You describe sections in natural language, and Reducto returns the page n…”
   • [claimed-docs] “You describe sections in natural language, and Reducto returns the page numbers where each section lives.”
   • [claimed-docs] “Multi-step workflows that chain classification, parsing, extraction, and editing into single API calls”
   • [claimed-docs] “Build document workflows by configuring classification, parsing, extraction, and editing steps, test on real documents with the citation vie…”

   proof ↗⚑ flag

   Extend✓fullcommunity8/10

   Extend has dedicated Classification and Splitting overview docs plus async processing that applies to every processing endpoint (extract, classify, split, parse), and supports file upload with automatic conversion, indicating a workflow where a single multi-document upload can be classified/split into per-document outputs. Community feedback confirms satisfaction with extraction accuracy over a year of production use. Missing for 10: an explicit end-to-end worked example combining classification+splitting on one upload with per-document result output shown in a single doc, and independent hands-on verification of this exact multi-doc splitting workflow.

   • [claimed-docs] “Classification Overview | Extend Documentation”
   • [claimed-docs] “Splitting Overview | Extend Documentation”
   • [claimed-docs] “Every processing endpoint in Extend (extract, classify, split, parse, and edit) supports both a synchronous and asynchronous mode.”
   • [claimed-docs] “This endpoint accepts file contents and registers them as a File in Extend, which can be used for running workflows, creating evaluation set…”
   • [claimed-docs] “If an uploaded file is detected as a Word or PowerPoint document, it will be automatically converted to a PDF.”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag

Tables

1. data engineerComplex tables — merged cells, nested headers, multi-page spans — come out as faithful HTML/markdown structure

   weight 3 · round to Reducto
   Reducto~partialcommunity6/10

   Reducto's docs explicitly address table extraction with configurable output formats that represent merged cells and headers, and Parse converts docs into structured JSON with tables/layout preserved; agentic mode also supports custom prompts for complex content. However, there's no explicit mention of nested headers or multi-page table spans being handled, and community feedback flags accuracy struggles on long documents, suggesting real-world caveats. missing for 10: explicit documentation/testing of nested headers and multi-page table spans, independent benchmark confirming fidelity on such complex tables.

   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats.”
   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats. The format you choose affects how merged cells, headers, and …”
   • [claimed-docs] “Parse is Reducto’s foundational endpoint. It converts documents into structured JSON with text, tables, figures, layout, and formatting”
   • [claimed-docs] “Use agentic processing with r-1 when your workflow needs a custom prompt for a specific content type or advanced chart extraction.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”

   proof ↗⚑ flag

   Extend~partialclaimed3/10

   Extend's pricing page claims it can 'Detect tables, checkboxes, images, and handwriting' and offers Agentic OCR/parsing, suggesting table-aware extraction, but there is no documentation addressing merged cells, nested headers, or multi-page table spans, nor evidence of faithful HTML/markdown structural output. Missing for 10: explicit handling of merged cells, nested headers, multi-page table continuity, and documented HTML/markdown fidelity examples.

   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “Agentic OCR”

   proof ↗⚑ flag

2. data engineerI turn extracted tables into typed rows/JSON I can load into a database without manual cleanup

   weight 2 · round to Reducto
   Reducto✓fullcommunity8/10

   Reducto's Extract endpoint lets engineers define a schema and returns typed structured JSON matching it, and Parse extracts tables in multiple configurable output formats (with citations/bounding boxes for traceability), directly enabling load-ready structured data without manual cleanup. Community feedback corroborates the core table/structured-extraction use case though notes accuracy tradeoffs on long documents, which is a quality caveat rather than a capability failure. Missing for 10: independent benchmark evidence of clean, zero-cleanup DB-ready output at scale, and explicit database-loading integration/examples.

   • [claimed-docs] “Extract pulls specific fields from documents as structured JSON. You define a schema describing the data you need, and Reducto returns value…”
   • [claimed-docs] “You define a schema describing the data you need, and Reducto returns values matching that schema”
   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats.”
   • [claimed-docs] “Reducto extracts tables from documents and can return them in several formats. The format you choose affects how merged cells, headers, and …”
   • [claimed-docs] “Citations tell you exactly where each extracted value came from in the document. When enabled, every field includes bounding box coordinates…”
   • [claimed-docs] “When enabled, every field includes bounding box coordinates pointing to the source text.”
   • [community] “We used Reducto and it did struggle with long documents. As we process financial documents going over 300+ pages using Gemini 3 Flash is pro…”
   • [community] “Reducto founder: 'Deep Extract is more accurate than any structured extraction product we've tried, but the approach comes with a very clear…”

   proof ↗⚑ flag

   Extend~partialcommunity6/10

   Extend documents schema-based extraction that outputs structured JSON with per-field confidence scores and citations, and explicitly advertises table detection, which supports turning extracted tables into typed data with less manual cleanup; community feedback also praises extraction accuracy. However, there is no explicit documentation of table-specific row/column typing, CSV/DB-ready output formats, or direct database load integrations. Missing for 10: table-to-row schema examples, explicit typed-row/CSV export, and direct DB-loading integration guidance.

   • [claimed-docs] “Extraction Schema | Extend Documentation”
   • [claimed-docs] “Confidence Scores | Extend Documentation”
   • [claimed-docs] “Detect tables, checkboxes, images, and handwriting”
   • [claimed-docs] “plus per-field confidence scores and citations that point back to the exact spot on the page”
   • [community] “We've been using Extend for over a year and have been super happy with the product and accuracy of the data extraction.”

   proof ↗⚑ flag