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.

# Managed agent content with KnitKnot

KnitKnot keeps eligible Agent Versions current from an approved Fact Ledger, then measures what changed.

## The operating loop

  1. 1. Read demand, benchmark, content-gap, source, and verified activity signals.
  2. 2. Keep approved facts and their receipts in the Fact Ledger.
  3. 3. Revise eligible Agent Versions when the evidence supports a material change.
  4. 4. Publish approved revisions with checks, immutable history, and rollback.
  5. 5. Show measured movement by journey and cycle.

## Product surfaces

### Benchmarking

Run a stable library of buyer questions across supported AI engines. Preserve the answers, material claims, competitive outcomes, and reliable source evidence behind every result.

### Demand and Questions

Build the buyer-question library from search demand, products, competitors, capabilities, and buyer roles. Search demand is a proxy for buyer interest, not private AI-chat volume.

### Claims and Evidence

Break each captured answer into checkable claims. Store the response span, verdict, receipt, and source attribution when the answer exposes a reliable citation signal.

### Issues

Group repeated representation, competitive, source, content, and access problems into durable records with evidence, reach, activity, and linked work.

### Fact Ledger

Store approved company facts with receipts, freshness, owners, and tracked dependencies. Demand signals and customer journey targets remain separate from publishable facts.

### Managed agent content

Use the customer's page, approved facts with receipts, and current demand and benchmark signals to propose bounded changes. A weekly review can end with no change when the evidence is not material.

### Agent delivery

Serve the approved corpus through agent-readable Markdown and rollout-gated interfaces: read-only MCP on the customer domain, experimental WebMCP declarations, and a signed agent feed. Availability never promises provider consumption.

### MCP and integrations

Query the authorized workspace through KnitKnot MCP. Supported connections coordinate playbook work, documentation destinations, and observed AI-referred traffic without mixing those signals into benchmark scoring.

### Measurement

Key the next eligible benchmark window to the published revision. The default statement is changed -> measured movement. Insufficient data remains a visible result, and a qualifying holdout is required for causal language.

### Customer-managed Playbooks

Playbooks keep an issue, its evidence, the target page, recommended work, the ship record, and later measurement together when the customer team owns authoring and publishing.

## Publishing boundary

Managed content cannot say more than the customer's existing page and approved Fact Ledger support. The human path remains unchanged, every revision is attributable and reversible, and revoked claims take a priority strip path.

## Start with the baseline

Request a benchmark at https://knitknot.ai/ or sign in at https://app.knitknot.ai/signin.

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

Get Agent Ready

Keep your agent-facing content current.

KnitKnot reads the demand, benchmark, content-gap, and verified activity signals around your company. We manage eligible Agent Versions from an approved Fact Ledger and measure what changed.

The operating loop

One managed loop from demand to measured movement.

The weekly signal review, Fact Ledger, published revision, and later measurement stay connected.

  1. Sense

    Read demand, benchmark, source, content-gap, and verified activity signals.

    Open

  2. Govern

    Approve the facts and receipts that managed content may use.

    Open

  3. Manage

    Revise eligible Agent Versions when the evidence supports a material change.

    Open

  4. Deliver

    Serve approved versions through Markdown and gated customer-domain interfaces.

    Open

  5. Measure

    Compare the next eligible benchmark with the frozen baseline.

    Open
Product surfaces

Open the product at the job you need to do.

The product menu is organized by job. Playbooks remain available for pages your team manages outside the hosted program.

Hosted Agent Versions

The pages reopen when the market changes.

Every eligible benchmark gives KnitKnot a fresh decision packet. Search demand shows what buyers are asking, the benchmark reveals where AI is negative or wrong, co-cited pages reveal the proof environment, and the Fact Ledger limits what the page may say.

Always current does not mean always changing.

KnitKnot keeps the active version when the evidence is unchanged, too thin, or outside the page’s scope. When a revision is warranted, the diff carries its facts, receipts, approval, release history, and rollback path.

See the managed cycle

Next-page decision packet

Search-demand topic
Buyer interest changed
Review scope
Negative sentiment
Repeated objection surfaced
Candidate correction
Co-cited target
Proof page shapes the answer
Evidence target
Approved fact
Receipt still current
Allowed support
Fact Ledger

Demand directs the work. Approved facts set the boundary.

Search demand, benchmark gaps, and journey targets can explain why a page should change. Only the current page and approved facts with receipts can support what the revision says.

A target is not a fact.

Journey targets describe where the customer wants to improve. Demand signals describe what people appear to care about. Neither one becomes a publishable claim.

Approved facts retain their receipts and dependencies. A revoked fact takes a priority strip path through affected Agent Versions.

Questions

Get Agent Ready FAQ

Start with the benchmark

Find the gaps. Build a better answer.

See where AI represents your company accurately, gets it wrong, or leaves it out—then keep eligible Agent Versions current with approved facts and evidence.

Question → evidence → next evaluation