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How Lorikeet’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score31/100

Agent-ready 39.8 × 0.30 = 11.94

API quality 6.0 × 0.20 = 1.20

Openness 10.3 × 0.20 = 2.06

Built-in AI 68.2 × 0.15 = 10.23

Automation 38.5 × 0.15 = 5.77

(11.94 + 1.20 + 2.06 + 10.23 + 5.77) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 31.20 ÷ 1.00 = 31.2

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready39.8/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 9 (quality) × 1.0 (full) = 18.0 of 20 max

  • [probe] https://docs.lorikeetcx.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.lorikeetcx.ai/llms.txt # Reference - [Lorikeet MCP Server](https://docs.lorikeetcx.ai/mcp/mcp-server.md): Connect to your Lorikeet account fro”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Connect to your Lorikeet account from ChatGPT, Claude, Claude Code, and MintMCP using the Model Context Protocol.”

Run the product headlessly / in CI for automationweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“The agent takes action through your APIs and MCP servers inside natural-language and deterministic workflows”
  • [claimed-docs] https://www.lorikeetcx.ai/integrations“Connect Lorikeet in seconds to your ticketing system, knowledge base, and internal tools to seamlessly ingest data and take action for your customers”

Connect an agent via an official MCP serverweight 3

3 (weight) × 7 (quality) × 1.0 (full) = 21.0 of 30 max

  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“The Lorikeet MCP server lets you interact with your Lorikeet account directly from Claude Code, Claude.ai, ChatGPT, and Codex.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Diagnose tickets - trace workflow execution and identify root causes”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Audit knowledge bases - analyze articles at scale, find gaps, and spot quality issues”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Build workflows - create and iterate on workflows using natural language”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Test tools - run and validate tool configurations directly from your AI assistant”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Connect to your Lorikeet account from ChatGPT, Claude, Claude Code, and MintMCP using the Model Context Protocol.”
  • [probe] https://docs.lorikeetcx.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.lorikeetcx.ai/llms.txt # Reference - [Lorikeet MCP Server](https://docs.lorikeetcx.ai/mcp/mcp-server.md): Connect to your Lorikeet account fro”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”

Use an official CLIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“The Lorikeet MCP server lets you interact with your Lorikeet account directly from Claude Code, Claude.ai, ChatGPT, and Codex.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/skills“/lorikeet:create-simulations build simulations for the refund workflow”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”

Drive the product through a documented public APIweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“The Lorikeet MCP server lets you interact with your Lorikeet account directly from Claude Code, Claude.ai, ChatGPT, and Codex.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Diagnose tickets - trace workflow execution and identify root causes”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Audit knowledge bases - analyze articles at scale, find gaps, and spot quality issues”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Build workflows - create and iterate on workflows using natural language”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Test tools - run and validate tool configurations directly from your AI assistant”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Test the 'get-order-status' tool with order ID 98765 and check if the response matches what we expect.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Explore your setup - inspect workflows, tools, and integrations”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Connect to your Lorikeet account from ChatGPT, Claude, Claude Code, and MintMCP using the Model Context Protocol.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Run simulations - test workflows against different customer scenarios”
  • [probe] https://docs.lorikeetcx.ai/llms.txt“PROBE llms.txt: HTTP 200 at https://docs.lorikeetcx.ai/llms.txt # Reference - [Lorikeet MCP Server](https://docs.lorikeetcx.ai/mcp/mcp-server.md): Connect to your Lorikeet account fro”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 3 (quality) × 0.6 (partial) = 3.6 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/guardrails“Customer isolation, server-side identity validation, workflow-scoped tool access and hard execution caps are enforced in code rather than prompts”

Build against official SDKsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Agent-ready = 75.6 ÷ 190 × 100 = 39.8

API quality6.0/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Test against a sandbox environment without touching production dataweight 1

1 (weight) × 5 (quality) × 0.6 (partial) = 3.0 of 10 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Generate simulations straight from your actual tickets and run them in bulk batches, so every workflow change is tested against the conversations your customers actually have.”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Run simulation batches to test workflows against different customer scenarios”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“Replay historical tickets and synthetic scenarios in bulk to see projected resolution quality and knowledge gaps, then deploy on the topics the agent is trained for”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“Replay historical tickets and synthetic scenarios in bulk to see projected resolution quality and knowledge gaps”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Author adversarial scenarios such as false authority claims, mid-conversation goal switches and prompt injection attempts, then run them as guardrail test scenarios so you know how the agent responds before a real user tries the same tricks.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Run simulations - test workflows against different customer scenarios”

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

API quality = 3.0 ÷ 50 × 100 = 6.0

Openness10.3/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 6 (quality) × 0.6 (partial) = 7.2 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“The Lorikeet MCP server lets you interact with your Lorikeet account directly from Claude Code, Claude.ai, ChatGPT, and Codex.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Diagnose tickets - trace workflow execution and identify root causes”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Audit knowledge bases - analyze articles at scale, find gaps, and spot quality issues”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Build workflows - create and iterate on workflows using natural language”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Test tools - run and validate tool configurations directly from your AI assistant”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Explore your setup - inspect workflows, tools, and integrations”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Run simulations - test workflows against different customer scenarios”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“The agent takes action through your APIs and MCP servers inside natural-language and deterministic workflows”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”

Export all of my data in open formats and leaveweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Read the product's source under an open licenseweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Self-host the core productweight 3

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Openness = 7.2 ÷ 70 × 100 = 10.3

Built-in AI68.2/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/quality-assurance“Coach's Ticket Quality Score reviews 100% of conversations against your quality standards, AI and human alike, replacing manual spot checks with full coverage.”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Audit your entire knowledge base for gaps, outdated articles, and quality issues”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Diagnose tickets by tracing workflow execution and identifying root causes”
  • [claimed-docs] https://www.lorikeetcx.ai/competitors“Track resolution quality, customer satisfaction, revenue impact, and operational efficiency with industry-leading analytics.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/coach“Coach can implement improvements on your behalf, or make suggestions for you to action yourself.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/ai-agents“Coach reviews 100% of tickets against your quality standards and turns findings into fixes, so your AI agent improves every week instead of drifting.”
  • [claimed-docs] https://www.lorikeetcx.ai/product“Scale and optimize with conversational insights and analytics from Lorikeet Coach”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 7 (quality) × 0.6 (partial) = 8.4 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“one agent that resolves issues end-to-end across chat, email, voice and SMS”
  • [claimed-docs] https://www.lorikeetcx.ai/product/ai-agents“Lorikeet coordinates a team of specialist agents, with pockets of determinism for regulated steps, to handle multi-party, multi-system workflows end-to-end.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/outbound“campaign cadences with scheduling windows control when outreach happens”
  • [claimed-docs] https://www.lorikeetcx.ai/product/coach“Coach can implement improvements on your behalf, or make suggestions for you to action yourself.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“The agent takes action through your APIs and MCP servers inside natural-language and deterministic workflows”

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 7 (quality) × 1.0 (full) = 21.0 of 30 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/quality-assurance“Coach's Ticket Quality Score reviews 100% of conversations against your quality standards, AI and human alike, replacing manual spot checks with full coverage.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/coach“Talk to Coach wherever you work, whether in Lorikeet, Slack, Claude, ChatGPT, or via MCP.”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Diagnose tickets by tracing workflow execution and identifying root causes”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Audit your entire knowledge base for gaps, outdated articles, and quality issues”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Build, edit, and deploy workflows using natural language”
  • [claimed-docs] https://www.lorikeetcx.ai/product/ai-agents“Coach reviews 100% of tickets against your quality standards and turns findings into fixes, so your AI agent improves every week instead of drifting.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/coach“Coach can implement improvements on your behalf, or make suggestions for you to action yourself.”

Operate the product with natural-language commandsweight 2

2 (weight) × 8 (quality) × 1.0 (full) = 16.0 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“The Lorikeet MCP server lets you interact with your Lorikeet account directly from Claude Code, Claude.ai, ChatGPT, and Codex.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Build workflows - create and iterate on workflows using natural language”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Build, edit, and deploy workflows using natural language”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Test the 'get-order-status' tool with order ID 98765 and check if the response matches what we expect.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/skills“/lorikeet:create-simulations build simulations for the refund workflow”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Connect to your Lorikeet account from ChatGPT, Claude, Claude Code, and MintMCP using the Model Context Protocol.”
  • [probe] https://docs.lorikeetcx.ai/mcp/mcp-server“official MCP server documented at https://docs.lorikeetcx.ai/mcp/mcp-server”

Built-in AI = 61.4 ÷ 90 × 100 = 68.2

Automation38.5/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 7 (quality) × 1.0 (full) = 14.0 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Generate simulations straight from your actual tickets and run them in bulk batches, so every workflow change is tested against the conversations your customers actually have.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Side-by-side batch comparisons show how a workflow edit changed outcomes across hundreds of scenarios, with per-conversation drill-downs when you need to understand a single result.”
  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Audit knowledge bases - analyze articles at scale, find gaps, and spot quality issues”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Audit your entire knowledge base for gaps, outdated articles, and quality issues”
  • [claimed-docs] https://www.lorikeetcx.ai/product/quality-assurance“Coach's Ticket Quality Score reviews 100% of conversations against your quality standards, AI and human alike, replacing manual spot checks with full coverage.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/quality-assurance“Coach’s Ticket Quality Score reviews 100% of conversations against your quality standards, AI and human alike, replacing manual spot checks with full coverage.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“Replay historical tickets and synthetic scenarios in bulk to see projected resolution quality and knowledge gaps, then deploy on the topics the agent is trained for”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“Replay historical tickets and synthetic scenarios in bulk to see projected resolution quality and knowledge gaps”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Side-by-side batch comparisons show how a workflow edit changed outcomes across hundreds of scenarios, with per-conversation drill-downs”

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/guardrails“Customer isolation, server-side identity validation, workflow-scoped tool access and hard execution caps are enforced in code rather than prompts”
  • [claimed-docs] https://www.lorikeetcx.ai/product/guardrails“Incoming messages pass a prompt-injection classifier and bad-actor checks, while response guardrails screen what the agent says for grounding and policy, with the ability to block, rewrite or escalate.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/quality-assurance“When Coach gives a conversation a bad score, we refund the AI portion of that interaction.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/outbound“campaign cadences with scheduling windows control when outreach happens”
  • [claimed-docs] https://www.lorikeetcx.ai/industry/financial-services“When a case requires a specialist or regulatory review, it escalates with full interaction history so your team picks up mid-conversation.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/ai-agents“Lorikeet coordinates a team of specialist agents, with pockets of determinism for regulated steps, to handle multi-party, multi-system workflows end-to-end.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/how-it-works“The agent takes action through your APIs and MCP servers inside natural-language and deterministic workflows”

Schedule recurring jobs or workflowsweight 2

2 (weight) × 3 (quality) × 0.6 (partial) = 3.6 of 20 max

  • [claimed-docs] https://www.lorikeetcx.ai/product/outbound“campaign cadences with scheduling windows control when outreach happens”

Version, review, and roll back my automationsweight 1

1 (weight) × 4 (quality) × 0.6 (partial) = 2.4 of 10 max

  • [claimed-docs] https://docs.lorikeetcx.ai/mcp/mcp-server“Build workflows - create and iterate on workflows using natural language”
  • [claimed-docs] https://www.lorikeetcx.ai/release-notes“Build, edit, and deploy workflows using natural language”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Side-by-side batch comparisons show how a workflow edit changed outcomes across hundreds of scenarios, with per-conversation drill-downs when you need to understand a single result.”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Side-by-side batch comparisons show how a workflow edit changed outcomes across hundreds of scenarios, with per-conversation drill-downs”
  • [claimed-docs] https://www.lorikeetcx.ai/product/simulations“Generate simulations straight from your actual tickets and run them in bulk batches, so every workflow change is tested against the conversations your customers actually have.”

Automation = 30.8 ÷ 80 × 100 = 38.5