KnitKnot
You are reading the agent-optimized layer of this page: the literal markdown we serve to AI crawlers and assistants, shipped in the page source of every visit. Making sure AI reads the right facts about a company is literally what KnitKnot does.

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

## Machine-readable access

## Resources

## Company

Raw mirror of this content: https://knitknot.ai/index.md. Site-wide summary: /llms.txt · full content: /llms-full.txt

AI competitive positioning

One question goes in. Different versions of your company come back.

KnitKnot benchmarks how AI agents answer, traces the claims that matter to their sources, helps your team improve the approved inputs it controls, and measures the next evaluation.

Follow one answer
An evidence map connecting one buyer question to three AI answers, source receipts, approved facts, and a measured revision

Question 01 · evaluation

“Which vendor belongs on the shortlist—and why?”

The thread

Question → answer → claim → source → approved fact → action → measurement

01 / The answer

AI agents do not read from one company record.

The same buyer question can return a different shortlist, rationale, or product description from each evaluated system. The first job is not to declare a winner. It is to preserve exactly what each answer said.

Answer AIncluded, with current positioningObserved
Answer BIncluded, with an outdated capabilityReview
Answer CAbsent from the shortlistObserved

Claim fragment / 04

“The platform supports 173 integrations.”
Captured answer
Third-party page
Approved record: 200

02 / Pull one claim

Here is the answer. Here are its receipts.

KnitKnot breaks a captured answer into claims and traces the consequential ones to their source context. Unsupported does not automatically mean false. Unknown stays unknown.

Inspect claims and evidence

03 / Approve the record

A target is not a fact.

The Fact Ledger separates what the company can support from what it hopes to say. Products, capabilities, decision criteria, buyer roles, competitors, and positioning live on one approved record with evidence attached.

See the Fact Ledger
Approved fact
200 integrations
2 receipts
Positioning
Enterprise agent observability
Approved
Unverified target
Market-leading accuracy
Not a fact
Hosted Agent Version / currentPolicy passed

Search-demand topic

Enterprise agent observability

In scope

Negative sentiment

Outdated deployment limitation

Review

Co-cited target

Security comparison page

Promote

Approved fact

SAML SSO and role controls

Supported

Next reviewAfter the next eligible benchmark. No material evidence means no revision.

04 / Hosted Agent Versions

Always current. Never untethered.

After every eligible benchmark, KnitKnot reopens the evidence packet for each hosted page. Search-demand topics, repeated negative claims, co-cited source patterns, and approved facts determine what deserves review.

A bounded revision publishes only when the evidence is material, the facts support it, and your approval policy passes. Otherwise the current version stays put.

See hosted Agent Versions

05 / Publishing contract

Bounded by design.

No silent rewrite. No invented certainty. No changed human page.

01

Approved facts

A managed version cannot say more than the page and approved record support.

02

Visible sources

Consequential claims keep their receipts and their review state.

03

Human pages unchanged

The agent-readable version is additive. Your original page remains the human and crawler path.

04

Attributable and reversible

Every eligible release keeps its approval, publish history, evaluation window, and rollback.

06 / Ask again

Changed, then measured.

The next eligible benchmark can show measured movement, no material change, or insufficient evidence. Each is a result. AI referrals, crawler fetches, and agent tool calls remain separate observational facts.

Read the measurement doctrine
BaselineFrozen
RevisionPublished
Next windowObserved

Movement below the reporting floor is recorded as no material change, not dressed up as a delta.

07 / Week one

The evidence stands on its own.

The first thing KnitKnot produces is an unlisted benchmark of how evaluated AI systems represent you today. Nothing is managed until you approve the facts and choose the pages.

See pricing

Questions

Before you start.

Hosted pages, evidence boundaries, publishing ownership, and measurement—without the category fog.