Extend vs Mistral Document AI

Agent access

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

   weight 2 · round to Extend
   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

   Mistral Document AI~partialprobed6/10

   A probe confirms Mistral's docs site serves a working llms.txt (HTTP 200) listing agent-oriented doc links, so an AI agent could be pointed at it. However, the specific Document AI/document-processing page lacks a markdown variant (404 on overview.md), showing incomplete agent-oriented coverage for this particular product area. Missing for 10: confirmation that document-processing pages themselves are included in llms.txt or have .md equivalents, and any evidence of an agent successfully consuming these docs end-to-end.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.mistral.ai/llms.txt # MistralAI ## Docs [Agents & Conversations](https://docs.mistral.ai/docs/age…”
   • [probe] “PROBE docs-md: HTTP 404 at https://docs.mistral.ai/studio/document-processing/overview.md”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   Mistral Document AI~partialcommunity6/10

   Document AI is delivered purely as an API with official Python and TypeScript SDKs, and inputs can be passed via URL/Base64/file upload — all of which naturally support headless/CI use. However, there is no explicit documentation of CI/CD examples, GitHub Actions, async batch processing at scale, or automation-specific guidance (and community reports note timeouts on large batch jobs, suggesting scaling caveats for automated pipelines). Missing for 10: explicit CI/automation documentation or examples, guidance on rate limits/batching for large-scale headless runs, and independent confirmation of reliable large-batch automation.

   • [claimed-docs] “We provide official SDK clients in both Python and Typescript”
   • [claimed-docs] “For PDFs, pass a publicly available URL, pass a Base64-encoded PDF, or upload a PDF file.”
   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”

   proof ↗⚑ flag

3. ai-native userUse an official CLI

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   Evidence shows only official Python/TypeScript SDKs and a REST API for Document AI; there is no mention of an official CLI tool anywhere in the docs or community evidence.

   • [claimed-docs] “We provide official SDK clients in both Python and Typescript”

   proof ↗⚑ flag

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

   weight 3 · round to Extend
   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

   Mistral Document AI✓fullprobed8/10

   Mistral Document AI is delivered entirely as an API with extensively documented parameters (OCR extraction, table_format, extract_header/footer, include_blocks, confidence_scores_granularity, annotation endpoints, Document QnA) plus official Python and TypeScript SDKs, and community evidence confirms real-world API usage (pricing per page, PDF-to-markdown workflows). Missing for 10: a discoverable formal OpenAPI/swagger spec (probe found 404s at standard locations) and independent third-party API-integration write-ups beyond community OCR quality discussions.

   • [claimed-docs] “Use the Document AI OCR processor to extract text and structured content from PDF documents and images.”
   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”
   • [claimed-docs] “For PDFs, pass a publicly available URL, pass a Base64-encoded PDF, or upload a PDF file.”
   • [claimed-docs] “Document formats include: `image_url`: PNG, JPEG/JPG, AVIF, and other image formats. `document_url`: PDF, PPTX, DOCX, and other document for…”
   • [claimed-docs] “We provide official SDK clients in both Python and Typescript”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.mistral.ai/openapi.json, https://docs.mistral.ai/swagger.json, https://docs.mistral.ai/…”

   proof ↗⚑ flag

5. ai-native userBuild against official SDKs

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack covers Document AI's OCR/annotation/QnA features and API endpoint details but contains no mention of official SDKs (Python, JS/TS, etc.) for building against Document AI — this is an applicable axis for an API product but no supporting evidence exists in the pack.

   ⚑ flag

6. ai-native userSubscribe to events via webhooks

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   No evidence anywhere in the pack of webhook support or event subscription for Document AI; documentation covers OCR, annotations, and Q&A only, with no mention of webhooks or event-driven notifications.

   ⚑ flag

Agentic features

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

   weight 2 · round to Mistral Document AI
   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

   Mistral Document AI✓fullcommunity7/10

   Document AI's Document QnA and annotation features explicitly combine OCR with LLM capabilities to let users 'extract information and insights from documents by asking questions in natural language,' plus structured annotation for key insights like vendor/clause/amount extraction. Community evidence corroborates strong OCR/extraction quality, though no independent hands-on evidence specifically validates the QnA/insights feature's quality or proactive 'suggestions' beyond Q&A. Missing for 10: independent corroboration of Document QnA/insight quality, evidence of proactive (non-query-driven) suggestions.

   • [claimed-docs] “The Document QnA capability combines OCR with large language model capabilities to enable natural language interaction with document content…”
   • [claimed-docs] “This allows you to extract information and insights from documents by asking questions in natural language.”
   • [claimed-docs] “Multi-document queries and comparisons”
   • [claimed-docs] “Extraction of key information like vendor details and amounts from invoices for automated accounting.”
   • [claimed-docs] “Extraction of key clauses and terms from contracts for easier review and management”
   • [community] “I used Abbyy Finereader for several years. I loved it... Modern VLMs put classic FineReader to shame for processing low-resolution/degraded/…”

   proof ↗⚑ flag

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

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack covers only synchronous OCR/document-extraction capabilities (text extraction, annotations, Q&A) with no mention of scheduling, triggers, webhooks, or any mechanism for autonomous background automation runs. As an AI-native document processing API, such automation features are plausible but entirely unevidenced here.

   ⚑ flag

3. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round to Extend
   Extend~partialclaimed5/10

   Extend markets built-in agentic features like "Composer & Review Agent," "Agentic OCR," and "Agent form filling," plus an "Agent Quickstart" guide, suggesting users can delegate document-processing tasks to built-in AI agents. However, there's no clear evidence of a general-purpose conversational AI assistant inside the product UI for delegating arbitrary tasks — the agentic features are narrowly scoped to document extraction/review workflows. Missing for 10: a documented chat/assistant interface, examples of delegating broader tasks beyond document processing, and independent hands-on confirmation of these agent features in use.

   • [claimed-docs] “Composer & Review Agent”
   • [claimed-docs] “Agentic OCR”
   • [claimed-docs] “Agent form filling”
   • [claimed-docs] “Agent Quickstart | Extend Documentation”
   • [claimed-docs] “Ship reliable document agents in minutes, not months.”

   proof ↗⚑ flag

   Mistral Document AI~partialclaimed4/10

   Document AI includes a 'Document QnA' capability that lets users interact with document content in natural language and run multi-document queries/comparisons, which functions as a narrow, document-scoped assistant rather than a general-purpose built-in agent for task delegation. missing for 10: evidence of a broader in-product assistant UI for delegating multi-step tasks beyond document Q&A, agentic workflow/tool-use capabilities, and independent hands-on validation of the QnA feature's reliability.

   • [claimed-docs] “The Document QnA capability combines OCR with large language model capabilities to enable natural language interaction with document content…”
   • [claimed-docs] “Multi-document queries and comparisons”
   • [claimed-docs] “Building document Q&A applications”
   • [claimed-docs] “This allows you to extract information and insights from documents by asking questions in natural language.”

   proof ↗⚑ flag

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

   weight 2 · round to Extend
   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

   Mistral Document AI~partialclaimed4/10

   Document AI's Document QnA feature lets users interact with document content via natural-language queries, combining OCR with LLM capabilities (docs-12, docs-13, docs-22), which is a real but narrow slice of 'natural-language commands.' However, this is limited to querying document content, not operating the broader product (setting OCR parameters, triggering annotation types, configuring table/header extraction) via natural language — those are all done through structured API parameters, not NL commands. Missing for 10: evidence of NL-driven configuration/control of the OCR pipeline itself, any chat/agent interface for invoking the API, and independent corroboration of the QnA feature's reliability.

   • [claimed-docs] “The Document QnA capability combines OCR with large language model capabilities to enable natural language interaction with document content…”
   • [claimed-docs] “Multi-document queries and comparisons”
   • [claimed-docs] “Building document Q&A applications”

   proof ↗⚑ flag

Api quality

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

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack shows extensive static documentation pages describing OCR/annotation parameters and SDKs, but no mention of an interactive API reference (e.g., Swagger/Redoc UI) with runnable/try-it examples. Probes for openapi.json/swagger.json all returned 404, and no evidence of an API playground or live code execution in docs.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.mistral.ai/openapi.json, https://docs.mistral.ai/swagger.json, https://docs.mistral.ai/…”
   • [probe] “PROBE docs-md: HTTP 404 at https://docs.mistral.ai/studio/document-processing/overview.md”
   • [claimed-docs] “We provide official SDK clients in both Python and Typescript”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   Mistral Document AI—none0/10

   No evidence of a published OpenAPI/machine-readable spec; docs describe SDKs (Python/TypeScript) but a direct probe of common OpenAPI spec URLs (openapi.json, swagger.json, etc.) returned 404 across all candidates, indicating no downloadable spec is provided.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.mistral.ai/openapi.json, https://docs.mistral.ai/swagger.json, https://docs.mistral.ai/…”
   • [claimed-docs] “We provide official SDK clients in both Python and Typescript”

   proof ↗⚑ flag

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

   weight 1 · round to Extend
   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

   Mistral Document AI—none0/10

   No evidence of any sandbox/test environment separate from production for Document AI; docs cover OCR features, annotations, and QnA but nothing about non-production testing modes, free trial isolation, or sandbox API keys. Missing for 10: dedicated sandbox environment or test mode documentation, guidance on isolating test data from production, any mention of sandbox API keys or staging endpoints.

   ⚑ flag

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

   weight 2 · round drawn
   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

   Mistral Document AI—none0/10

   No evidence of API versioning scheme or a documented deprecation policy anywhere in the docs pack; OpenAPI spec probes returned 404 and no changelog/versioning page is cited. missing for 10: versioned API endpoints, deprecation policy documentation, changelog/migration guides.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.mistral.ai/openapi.json, https://docs.mistral.ai/swagger.json, https://docs.mistral.ai/…”
   • [probe] “PROBE docs-md: HTTP 404 at https://docs.mistral.ai/studio/document-processing/overview.md”

   proof ↗⚑ flag

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

   weight 2 · round to Extend
   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

   Mistral Document AI~partialcommunity4/10

   Docs mention multi-document querying/comparison in the QnA feature and OCR can be called repeatedly via API for many files, but there is no documented batch/bulk-upload endpoint, and real-world use shows scaling limits (a 900-page single request timed out and had to be chunked), while other users report successful large-scale digitization of forms with human-review overhead. Missing for 10: an explicit bulk/batch processing API or endpoint, documented rate/size limits for large jobs, and independent evidence of reliable bulk throughput at scale.

   • [claimed-docs] “Multi-document queries and comparisons”
   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”
   • [community] “Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have a low percentage that need human review, but ov…”

   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 to Extend
   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

   Mistral Document AI—none0/10

   Evidence only shows generic marketing language about 'compliance-first organizations' and a 'Trust Center' link, with no concrete mention of zero-retention data handling, SOC 2 certification, or HIPAA compliance options for the Document AI product specifically. No documentation confirms data retention policies or named compliance certifications applicable to document processing of contracts/medical records.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   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 Extend
   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

   Mistral Document AI~partialclaimed3/10

   A single marketing-oriented citation states Mistral positions Document AI for "compliance-first organizations requiring secure on-premises deployment" and offers "secure deployments," but there is no technical documentation, API/on-prem architecture, VPC networking details, or independent/hands-on confirmation that the OCR/annotation stack can actually run fully self-hosted or within a customer VPC. Missing for 10: concrete self-hosted deployment docs/architecture, VPC networking specifics, independent verification of on-prem operation, and confirmation that all document-processing features (annotation, QnA) work outside Mistral's cloud.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure 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 Mistral Document AI
   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

   Mistral Document AI~partialcommunity7/10

   Docs confirm a single OCR/Document AI API handles PDF, PPTX, DOCX, images (PNG/JPEG/AVIF) via document_url/image_url inputs, plus URL/base64/upload ingestion, and community reports corroborate strong PDF/image OCR performance. However, XLSX, HTML, and email formats are never mentioned in the evidence, and one community report notes a full-page misread edge case. Missing for 10: explicit support/evidence for XLSX, HTML, and email formats, and independent confirmation of accuracy across all listed formats.

   • [claimed-docs] “Document formats include: `image_url`: PNG, JPEG/JPG, AVIF, and other image formats. `document_url`: PDF, PPTX, DOCX, and other document for…”
   • [claimed-docs] “For PDFs, pass a publicly available URL, pass a Base64-encoded PDF, or upload a PDF file.”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”

   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
   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

   Mistral Document AI!disputedcontradicted4/10

   Docs describe PDF/image ingestion via URL, base64, or upload with no documented size or timeout limits (mistral-document-ai-docs-15, docs-23), implying large documents should be supported, but hands-on community testing found a 900-page document caused a timeout and only succeeded after manually chunking the file (mistral-document-ai-comm-4). This is a concrete contradiction of the 'thousand-page documents process reliably without timeouts' claim rather than mere skepticism. Missing for 10: vendor-documented size/page limits or guaranteed timeout thresholds, evidence of successful multi-gigabyte or thousand-page processing without manual chunking, and any official guidance on handling large-file reliability.

   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”
   • [claimed-docs] “For PDFs, pass a publicly available URL, pass a Base64-encoded PDF, or upload a PDF file.”
   • [claimed-docs] “Document formats include: `image_url`: PNG, JPEG/JPG, AVIF, and other image formats. `document_url`: PDF, PPTX, DOCX, and other document for…”

   proof ↗⚑ flag

Languages

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

   weight 2 · round to Mistral Document AI
   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

   Mistral Document AI!disputedcontradicted4/10

   Mistral's docs claim strong multilingual OCR across 40+ languages, but a hands-on community test found that on a bidirectional English/Hebrew (RTL) document, 'the Hebrew output had no correspondence to the text whatsoever,' directly contradicting the claim of equal fidelity for non-Latin/RTL scripts. No CJK-specific hands-on evidence (positive or negative) is present either. Missing for 10: CJK-specific accuracy evidence, independent benchmarks confirming RTL/CJK fidelity, and resolution of the reported RTL failure.

   • [claimed-docs] “Multilingual OCR performs strongly across more than 40 languages.”
   • [community] “Just tested with a multilingual (bidi) English/Hebrew document. The Hebrew output had no correspondence to the text whatsoever... Their benc…”

   proof ↗⚑ flag

Ocr

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

   weight 1 · round drawn
   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

   Mistral Document AI~partialcommunity6/10

   Docs confirm OCR works on handwritten sources and that confidence scores are returned at page/block/word granularity, and a community user reports successful handwritten-form digitization with a low percentage requiring human review — matching the 'flagged when uncertain' workflow. However, the product doesn't document an explicit automatic uncertainty-flagging mechanism (developers must threshold the raw confidence scores themselves), and there's no dedicated handwriting accuracy benchmark. Missing for 10: built-in automatic flagging/threshold logic for uncertain fields, and handwriting-specific accuracy/benchmark data beyond anecdotal community reports.

   • [claimed-docs] “OCR with image: even from low-quality or handwritten sources.”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the `confidence_scores_granularity` parame…”
   • [community] “Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have a low percentage that need human review, but ov…”

   proof ↗⚑ flag

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

   weight 3 · round to Extend
   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

   Mistral Document AI!disputedcontradicted6/10

   Docs claim OCR works even on low-quality or handwritten sources (docs-14) and community reports strongly corroborate accuracy on degraded, decades-old, and handwritten scans (comm-6, comm-7, comm-8) — but other hands-on users report concrete failures: one saw an entire page misdetected as a single image block (comm-3), and another found Hebrew/bidi text output had no correspondence to the source at all (comm-5), directly contradicting the 'accurate across quality/language conditions' claim. No evidence specifically addresses skew correction or stamp handling. Missing for 10: explicit skew/rotation handling evidence, stamp-detection evidence, and resolution of the conflicting accuracy reports.

   • [claimed-docs] “OCR with image: even from low-quality or handwritten sources.”
   • [community] “I used Abbyy Finereader for several years. I loved it... Modern VLMs put classic FineReader to shame for processing low-resolution/degraded/…”
   • [community] “I was processing 55 year old paper files, most of them severely degraded, with its predecessor model. I was very impressed! I also tried Abb…”
   • [community] “Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have a low percentage that need human review, but ov…”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”
   • [community] “Just tested with a multilingual (bidi) English/Hebrew document. The Hebrew output had no correspondence to the text whatsoever... Their benc…”

   proof ↗⚑ flag

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

   weight 3 · round to Mistral Document AI
   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

   Mistral Document AI~partialclaimed3/10

   Output can be requested in markdown/html table formats and structured JSON with bounding boxes, which is a form of open-format export of extracted data, and on-prem deployment options exist for those wanting full control. However, there is no evidence of a bulk/account-wide data export mechanism, no documentation of exporting configuration, annotations schemas, or usage history, and no explicit 'leave with your data' portability guarantee. missing for 10: bulk account data export tooling, documented data portability/export guarantees, evidence of exporting all user data (not just per-document OCR output) in open formats.

   • [claimed-docs] “Table formatting supports null, markdown, and html values through the table_format parameter.”
   • [claimed-docs] “Table formatting supports `null`, `markdown`, and `html` values through the `table_format` parameter.”
   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   proof ↗⚑ flag

2. ai-native userSelf-host the core product

   weight 3 · round drawn
   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

   Mistral Document AI~partialclaimed3/10

   The only evidence is a marketing line on the solutions page claiming 'secure on-premises deployment' for compliance-first organizations, alongside a general mention that Mistral offers enterprise on-prem deployments elsewhere. There is no technical documentation, deployment guide, container/image, or licensing detail describing how to actually self-host the Document AI OCR/annotation/QnA capabilities. Missing for 10: concrete self-host deployment docs/instructions, technical requirements, hands-on confirmation that self-hosted Document AI works equivalently to the hosted API.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure 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
   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

   Mistral Document AI—none0/10

   No evidence pack items describe published, reproducible accuracy benchmarks (datasets, methodology, or eval scripts) that an ML engineer could rerun; docs focus on features/parameters, and community comments only reference qualitative testing or a vague mention that 'their benchmark results are impressive' without detail or reproducibility.

   ⚑ 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 Mistral Document AI
   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

   Mistral Document AI~partialcommunity6/10

   Docs show bbox_annotation for charts/figures, include_blocks with paragraph-level bounding boxes and structural labels in reading order, and document_annotation for full-document summaries — directly supporting figure/chart extraction with page-traceable positions. Community confirms real-world extraction of embedded images, but one hands-on report shows a failure mode where an entire page was misclassified as a single image rather than granular figure regions, indicating inconsistent reliability. Missing for 10: explicit example of VLM-generated descriptive summaries (vs. just bbox coordinates), independent benchmarking of figure/chart bounding-box accuracy across document types.

   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “bbox_annotation: gives you the annotation of the bboxes extracted by the OCR model (charts/ figures etc) based on user requirement and provi…”
   • [claimed-docs] “document_annotation: returns the annotation of the entire document based on the provided document annotation format.”
   • [claimed-docs] “Block extraction uses the `include_blocks` parameter. When enabled, each page contains a `blocks` array with paragraph-level bounding boxes,…”
   • [claimed-docs] “`bbox_annotation`: gives you the annotation of the bboxes extracted by the OCR model (charts/ figures etc) based on user requirement and pro…”
   • [community] “It outperforms the competition significantly AND can extract embedded images from the text. I really like LLMs for OCR more and more.”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”

   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
   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

   Mistral Document AI!disputedcontradicted6/10

   Docs describe robust structural parsing (headers/footers, block-level reading order, tables, confidence scores) and community feedback broadly praises accuracy versus competitors on degraded/handwritten text. However, concrete hands-on reports contradict clean complex-layout parsing: one user found the model misclassified an entire page as a single image block, and another found bidirectional (Hebrew) text output had 'no correspondence to the text whatsoever,' both indicating real-world layout/order failures despite vendor claims. Missing for 10: independent benchmark specifically on multi-column/footnote-heavy PDFs, and resolution of the bidi/layout failure reports.

   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Header and footer extraction uses the extract_header and extract_footer parameters.”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [community] “It outperforms the competition significantly AND can extract embedded images from the text. I really like LLMs for OCR more and more.”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”
   • [community] “Just tested with a multilingual (bidi) English/Hebrew document. The Hebrew output had no correspondence to the text whatsoever... Their benc…”

   proof ↗⚑ flag

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

   weight 2 · round to Mistral Document AI
   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

   Mistral Document AI✓fullcommunity8/10

   Docs explicitly describe block extraction with paragraph-level bounding boxes, structural block labels, and content in reading order, plus header/footer separation, table structure preservation (markdown/html), and confidence granularity — directly supporting hierarchy/reading-order preservation for downstream LLM consumption. Community feedback corroborates strong OCR/structure quality generally, though no independent test specifically validates heading/section hierarchy fidelity. Missing for 10: no third-party benchmark or hands-on example confirming heading-level hierarchy (vs. just block/paragraph order) is preserved, and one community report notes occasional structural misclassification (whole page as image).

   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Header and footer extraction uses the extract_header and extract_footer parameters.”
   • [claimed-docs] “Table formatting supports null, markdown, and html values through the table_format parameter.”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”

   proof ↗⚑ flag

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

   weight 2 · round to Mistral Document AI
   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

   Mistral Document AI~partialclaimed3/10

   The only relevant evidence is a solutions-page claim that Mistral offers secure on-premises deployment for 'compliance-first organizations' and maintains a Trust Center, which implies data residency control via self-hosting but does not document explicit region/data-residency selection within the hosted Document AI API itself. Missing for 10: documented region-selection options in the API/console, explicit EU/US or other data-residency guarantees, and independent confirmation of on-prem deployment working in practice.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   proof ↗⚑ flag

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

   weight 3 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack covers OCR/document-processing features and mentions on-premises deployment options and a Trust Center, but contains no explicit statement about data usage for AI training, opt-out policies, or data retention guarantees for La Plateforme/Document AI API usage. Missing for 10: explicit training-data opt-out policy, retention/data-use terms for API calls, independent confirmation of no-training defaults.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   proof ↗⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack covers OCR/extraction features and general enterprise positioning (on-prem deployment, Trust Center) but contains no specifics on data retention policies, deletion controls, or configurable data lifecycle for Document AI processing. missing for 10: documented retention windows, explicit deletion/opt-out controls, API parameters or admin settings for data handling, independent confirmation of retention practices.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   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

   Mistral Document AI—none0/10

   No evidence in the pack addresses telemetry or usage-tracking opt-out controls for Document AI; the only privacy-adjacent mention is a general note about on-prem/self-hosted deployment and a Trust Center link, with no specifics on telemetry settings.

   • [claimed-docs] “Document AI solution page: positioned for "Compliance-first organizations requiring secure on-premises deployment" with "Secure deployments"…”

   proof ↗⚑ 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 Extend
   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

   Mistral Document AI~partialclaimed5/10

   The OCR block-extraction feature returns paragraph-level bounding boxes, structural block labels, and content in reading order (docs-4/18/24), which provides some semantic segmentation and metadata that could feed a RAG pipeline, and markdown/table output aids readability. However, there is no explicit chunking API, no embedding-ready segment format, no chunk-size/overlap controls, and no mention of RAG-specific metadata (e.g., chunk IDs, semantic boundary detection) — the docs frame this as OCR/annotation output, not a purpose-built RAG chunker. missing for 10: explicit RAG-chunking API/parameters, embedding-ready segment format, semantic boundary detection beyond block/paragraph structure, and independent evidence of RAG use.

   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Block extraction uses the `include_blocks` parameter. When enabled, each page contains a `blocks` array with paragraph-level bounding boxes,…”
   • [claimed-docs] “When enabled, each page contains a blocks array with paragraph-level bounding boxes, structural block labels, and extracted content in readi…”
   • [claimed-docs] “Table formatting supports null, markdown, and html values through the table_format parameter.”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”

   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 Mistral Document AI
   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

   Mistral Document AI✓fullcommunity8/10

   Docs explicitly support clean, LLM-ready output: OCR returns markdown with configurable table_format, dedicated extract_header/extract_footer parameters that pull page furniture into separate header/footer fields rather than the body text, and include_blocks that returns structural, reading-order content — all directly aimed at producing clean structured output for downstream LLM/RAG use. Community feedback independently praises the 'pdf to markdown' quality as fast and highly accurate compared to competitors, corroborating the docs claims, though some testers report unrelated accuracy issues (image misdetection, multilingual garbling) rather than contradicting the header/footer-stripping mechanism itself. Missing for 10: independent benchmark or hands-on confirmation specifically validating repeated-header deduplication across multi-page documents, and no JSON-schema example showing final 'clean' output structure.

   • [claimed-docs] “Header and footer extraction uses the extract_header and extract_footer parameters.”
   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Header and footer extraction uses the `extract_header` and `extract_footer` parameters. When you use them, the response includes header and …”
   • [claimed-docs] “Block extraction uses the `include_blocks` parameter. When enabled, each page contains a `blocks` array with paragraph-level bounding boxes,…”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [community] “It outperforms the competition significantly AND can extract embedded images from the text. I really like LLMs for OCR more and more.”

   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 Extend
   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

   Mistral Document AI—none0/10

   No evidence anywhere in the docs pack of async job submission, status polling endpoints, or completion webhooks for Document AI OCR/annotation calls; the API appears to be synchronous (request/response), and one community report notes 900-page documents caused a timeout rather than being handled as a background job.

   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”
   • [claimed-docs] “Use the Document AI OCR processor to extract text and structured content from PDF documents and images.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.mistral.ai/openapi.json, https://docs.mistral.ai/swagger.json, https://docs.mistral.ai/…”

   proof ↗⚑ flag

Latency

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

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   The evidence pack documents OCR/annotation/QnA features and parameters but contains no mention of a synchronous vs asynchronous processing mode, nor any documented latency figures per mode; one community note even mentions timeouts on large batches, suggesting no such fast sync mode is called out. missing for 10: any documentation of sync/async modes, any latency benchmarks or SLAs per mode.

   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”

   proof ↗⚑ flag

Scale

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

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   No evidence in the pack documents rate limits, batch/async processing endpoints, or throughput guarantees for high-volume (millions of pages) workloads; the only relevant community data point reports a timeout at 900 pages requiring manual chunking, which is the opposite of predictable large-scale throughput.

   • [community] “This worked indeed. Although I had to cut my document into smaller chunks. 900 pages at once ended with a timeout.”

   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 Extend
   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

   Mistral Document AI—none0/10

   Evidence covers API parameters, SDKs, and OCR quality discussions, but nothing describes a drag-and-drop web playground for trying document parsing/extraction without writing code; all usage shown is via API/SDK calls.

   ⚑ 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
   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

   Mistral Document AI—none0/10

   The evidence pack contains only API/feature documentation (OCR parameters, annotations, Q&A) and community feedback on OCR accuracy, but nothing about official Python or TypeScript SDKs, their typed interfaces, or coverage of parse/extract/jobs endpoints — missing for 10: any mention of SDK packages, typed client libraries, installation/import examples, or SDK-specific defaults.

   ⚑ 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 to Extend
   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

   Mistral Document AI~partialcommunity7/10

   Docs show strong provenance primitives for raw OCR: per-page blocks array with paragraph-level bounding boxes, structural labels, reading-order content, and confidence scores at page/block/word granularity (docs-4/5/18/19). However, the structured 'document_annotation' (key-field extraction like invoice/receipt fields) is described as returning annotations for the whole document/format without explicit per-field bounding boxes or source snippets tied to those extracted key-value fields—only 'bbox_annotation' ties boxes to charts/figures, not to structured field values, and no evidence confirms every extracted field (e.g., vendor name, amount) carries its own page/bbox/snippet link. Community evidence corroborates OCR/bbox usage exists but doesn't validate field-level citation provenance for structured extraction. Missing for 10: explicit documentation or example showing that fields extracted via document_annotation/JSON schema outputs individually include page number, bounding box, and source text snippet; independent verification of citation accuracy for structured (non-OCR-block) fields.

   • [claimed-docs] “Block extraction uses the include_blocks parameter. When enabled, each page contains a blocks array with paragraph-level bounding boxes, str…”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”
   • [claimed-docs] “bbox_annotation: gives you the annotation of the bboxes extracted by the OCR model (charts/ figures etc) based on user requirement and provi…”
   • [claimed-docs] “document_annotation: returns the annotation of the entire document based on the provided document annotation format.”
   • [claimed-docs] “Extraction of key information like vendor details and amounts from invoices for automated accounting.”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”

   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
   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

   Mistral Document AI~partialcommunity4/10

   Mistral Document AI explicitly documents confidence scores at page, block, or word granularity via the confidence_scores_granularity parameter, satisfying the 'calibrated confidence' half of the story. However, there is no evidence of any built-in human-in-the-loop review workflow, UI, or API path for routing low-confidence fields to reviewers — the only related evidence is a community comment noting users manually review 'a low percentage' of outputs themselves, which is not a product capability. Missing for 10: documented review-queue/workflow feature, API/UI for flagging or routing low-confidence extractions, and evidence of calibration methodology.

   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the confidence_scores_granularity paramete…”
   • [claimed-docs] “Confidence scores are available for extracted content at page, block, or word granularity through the `confidence_scores_granularity` parame…”
   • [community] “Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have a low percentage that need human review, but ov…”

   proof ↗⚑ flag

Schemas

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

   weight 3 · round to Extend
   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

   Mistral Document AI~partialclaimed6/10

   Docs describe `document_annotation`/`bbox_annotation` that return structured output based on a user-provided 'annotation format', and give concrete extraction examples (invoices, receipts, contracts) implying schema-guided field extraction. However, the pack never explicitly names 'JSON schema' as the input format nor describes validation/error handling if extraction doesn't conform to the schema, and there is no independent/hands-on evidence testing schema-based structured extraction specifically. missing for 10: explicit JSON Schema input format documentation, validation/error behavior on mismatched schema, independent corroboration of structured-extraction accuracy.

   • [claimed-docs] “document_annotation: returns the annotation of the entire document based on the provided document annotation format.”
   • [claimed-docs] “`document_annotation`: returns the annotation of the entire document based on the provided document annotation format.”
   • [claimed-docs] “Extraction of key information like vendor details and amounts from invoices for automated accounting.”
   • [claimed-docs] “Capture of receipt data, including merchant names and transaction amounts, for expense management.”
   • [claimed-docs] “Extraction of key clauses and terms from contracts for easier review and management”
   • [claimed-docs] “bbox_annotation: gives you the annotation of the bboxes extracted by the OCR model (charts/ figures etc) based on user requirement and provi…”

   proof ↗⚑ flag

Splitting

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

   weight 2 · round to Extend
   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

   Mistral Document AI—none0/10

   Evidence covers OCR extraction, annotations, and Document QnA (including multi-document queries/comparisons across separate docs), but there is no mention of automatically classifying and splitting a single upload containing multiple distinct documents into per-document results — a distinct IDP capability that is not addressed anywhere in the pack.

   ⚑ flag

Tables

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

   weight 3 · round to Mistral Document AI
   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

   Mistral Document AI~partialcommunity5/10

   Docs confirm a table_format parameter that outputs markdown or HTML for extracted tables, and community feedback broadly praises OCR accuracy, but no evidence specifically addresses fidelity for merged cells, nested/complex headers, or tables spanning multiple pages. Missing for 10: documented handling of merged cells, nested header structures, multi-page table stitching, and independent verification of structural fidelity beyond simple format conversion.

   • [claimed-docs] “Table formatting supports null, markdown, and html values through the table_format parameter.”
   • [claimed-docs] “Table formatting supports `null`, `markdown`, and `html` values through the `table_format` parameter.”
   • [community] “Dang. Super fast and significantly more accurate than google, Claude and others. Pricing: $1/1000 pages... this looks great at pdf to markdo…”
   • [community] “It outperforms the competition significantly AND can extract embedded images from the text. I really like LLMs for OCR more and more.”

   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 Extend
   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

   Mistral Document AI~partialcommunity5/10

   Docs show table_format outputs (null/markdown/html) and a document_annotation feature that can return structured JSON based on a user-provided schema (useful for invoices, receipts, contracts), which could feed typed rows into a DB, but there's no explicit table-to-typed-JSON/row schema example, and community reports note accuracy issues (whole-page-as-image, multilingual mismatches) and the need for human review of a percentage of extractions, meaning manual cleanup is often still required. Missing for 10: a documented end-to-end example of table→typed JSON/row schema output, and independent evidence that output requires no manual correction.

   • [claimed-docs] “Table formatting supports null, markdown, and html values through the table_format parameter.”
   • [claimed-docs] “document_annotation: returns the annotation of the entire document based on the provided document annotation format.”
   • [claimed-docs] “Extraction of key information like vendor details and amounts from invoices for automated accounting.”
   • [claimed-docs] “`document_annotation`: returns the annotation of the entire document based on the provided document annotation format.”
   • [community] “From my testing... it decided that the entire page is an image and returned ![img-0.jpeg] with coordinates for the entire page. Our tool, do…”
   • [community] “Just tested with a multilingual (bidi) English/Hebrew document. The Hebrew output had no correspondence to the text whatsoever... Their benc…”
   • [community] “Yes, we have successfully used Mistral OCR for digitizing handwritten forms. You always have a low percentage that need human review, but ov…”

   proof ↗⚑ flag