# KnitKnot

> KnitKnot keeps eligible Agent Versions current from an approved Fact Ledger, publishes them under policy, and measures what changed by journey and cycle.

- Category: managed agent content, AI evaluation readiness, and AI representation benchmarking
- Website: https://knitknot.ai

## What KnitKnot does

KnitKnot manages the agent-facing content layer on eligible customer pages. Each week, KnitKnot reviews approved fact changes, measured AI answers, buyer questions, source patterns, content evidence, and verified activity. It checks those signals against the approved Fact Ledger, finds the managed pages that depend on a changed record, and revises Agent Versions only when the evidence supports a material change. It publishes under the customer's approval policy and measures later movement by journey and cycle.

## The operating loop

1. Read search demand, the active question library, benchmark results, source and content gaps, and verified activity.
2. Freeze the benchmark before the first managed revision is published.
3. Keep publishable facts, receipts, owners, freshness, and page dependencies in the Fact Ledger.
4. Build an evidence packet from the current page, approved facts, and current structured signals.
5. Propose a bounded change only when the evidence is material, then check every claim and apply the configured approval policy.
6. Publish an immutable Agent Version with a recorded diff and rollback.
7. Serve approved versions through Markdown and rollout-gated customer-domain interfaces.
8. Show measured movement in the next eligible journey and cycle window.

## Diagnosis depth

- Captured answers remain available at the response level.
- Material claims are extracted and labelled as supported, inaccurate, or unverifiable without turning unsupported into false.
- Persistent issues cover misrepresentation, category gaps, competitive outcomes, source patterns, content gaps, and access conditions.
- Citation signals and co-cited pages provide source context. Unknown attribution stays unknown.
- The managed content cycle uses approved primary evidence and structured issue signals. Raw third-party page copy does not enter the authoring packet.

## The Fact Ledger

The Fact Ledger provides positioning governance. It holds the publishable record that every managed page and enabled interface may use. Products, capabilities, deployment options, policies, and proof are stored with receipts, freshness, owners, customer approval, and dependent pages. A revoked or refuted fact remains traceable to dependent content so it can take the priority strip path.

Demand signals and customer journey targets remain separate. They can direct the work and define its measurement scope, but they do not become publishable facts. Search demand is a proxy for buyer interest, not private AI-chat volume.

## Publishing contract

- A managed version cannot say more than the customer's existing page and approved Fact Ledger support.
- The human path remains unchanged. Agent content is an additive representation of the same supported substance.
- Keyword stuffing and fake freshness are blocked.
- Every change is attributable and reversible.
- Revoked or refuted claims take a priority strip path.
- Automatic publishing requires a passing evaluation gate for the revision class and workspace. In-place rewrites remain manual.

## Measurement doctrine

- Default language: changed -> measured movement.
- Reports show movement per journey and per cycle.
- Insufficient data is a first-class result; no delta is printed below the reporting floor.
- A qualifying holdout experiment is required for causal language on a specific per-page result.
- Verified crawler fetches, delivery outcomes, agent tool calls, and AI-referred visits remain separate facts. Traffic is not the promised outcome.

## Agent delivery

- Agent-readable Markdown: built and in use on supported KnitKnot surfaces.
- Read-only MCP on customer domains: built and capability-gated; customer rollout remains behind launch gates.
- WebMCP: built, experimental in supported Chromium environments, and rollout-gated. It becomes a no-op when navigator.modelContext is absent.
- Agent feed: built and rollout-gated. The outbound publish-notification sender remains pending, and a notification never promises a fetch or changed answer.
- Generative /ask: reserved for later and not part of the current offering.

## Pricing

Paid plans combine recurring benchmarks with hosted Agent Version capacity. Starter includes up to 50 hosted pages, Pro includes up to 200, and Enterprise has no page cap. The Free plan is a one-time benchmark report and includes no recurring hosted pages.

- Free first benchmark: one time, up to 75 questions, up to 4 named competitors, 1 product, and 3 AI engines: ChatGPT, Perplexity, and Gemini (no Claude).
- Starter: $149/month for one product, a 100-question benchmark library, 4 named competitors, the same 3 AI engines (no Claude), and 5 prioritized playbooks per week.
- Pro: $449/month for one product, a 250-question benchmark library, 8 named competitors, 4 AI engines including Claude, and 15 prioritized playbooks per week.
- Enterprise: custom pricing for portfolio-wide coverage, with a 400+ question library that scales with the scope, unlimited competitors, unlimited products, unlimited prioritized playbooks, custom cadence, and SAML SSO available.
- Each included competitor starts with a named rival × product relationship. Direct comparisons are freeform rather than capped at a fixed number per competitor, spanning evidence-backed topics, capabilities, use cases, hard requirements, decision criteria, and buyer roles.
- The free benchmark is a shareable one-time report. Every paid plan includes unlimited team seats, unlimited competitor analysis, claim and source intelligence, owned-page GEO health, content coverage maps, weekly playbooks, later-run measurement, and customer MCP access.
- Every plan analyzes every competitor AI mentions. The competitor allowance controls which rivals KnitKnot deliberately forces into repeated direct comparison, not which competitors it records and scores.

## Product pages

- Platform overview: https://knitknot.ai/product/
- Benchmarking: https://knitknot.ai/product/benchmarking/
- Demand and Questions (Prompt Intelligence): https://knitknot.ai/product/prompt-intelligence/
- Claims and Evidence (Claim Intelligence): https://knitknot.ai/product/claim-intelligence/
- Issues: https://knitknot.ai/product/issues/
- Fact Ledger: https://knitknot.ai/product/fact-ledger/
- Managed agent content: https://knitknot.ai/product/managed-agent-content/
- Agent delivery: https://knitknot.ai/product/agent-interfaces/
- Workspace MCP and integrations: https://knitknot.ai/product/mcp-integrations/
- Measurement: https://knitknot.ai/product/measurement/
- Customer-managed Playbooks: https://knitknot.ai/product/playbooks/
- Pricing: https://knitknot.ai/pricing/

## Machine-readable access

- Concise site document: https://knitknot.ai/llms.txt
- Full published content: https://knitknot.ai/llms-full.txt
- Rewritten product pages and published docs, blog posts, guides, comparisons, and experiments have Markdown twins.
- Customer workspace MCP: https://mcp.knitknot.ai/mcp/

## Resources

- Measurement methodology: https://knitknot.ai/methodology/
- Measurement experiments: https://knitknot.ai/experiments/
- Documentation: https://knitknot.ai/docs/
- Guides: https://knitknot.ai/learn/
- Comparisons: https://knitknot.ai/compare/
- Blog: https://knitknot.ai/blog/
- Changelog: https://knitknot.ai/changelog/

## Company

- Website: https://knitknot.ai
- Contact: max@knitknot.ai
- LinkedIn: https://www.linkedin.com/company/knitknot-ai/