# KnitKnot
Stop letting competitors feed AI bullshit about you.
KnitKnot is AI competitive positioning for B2B SaaS. It measures visibility and head-to-head recommendations, finds the claims behind competitive losses, traces reliable source signals, and helps your team correct the record.
- - Category: AI competitive positioning for B2B SaaS
- - Coverage: buyer questions across ChatGPT, Perplexity, Gemini, and other systems included in the customer scope
- - Website: https://knitknot.ai
## Trusted by
## Why this exists
Buyers increasingly ask AI which product to choose. AI can repeat a competitor's comparison page, an outdated third-party article, or an unsupported claim as if it were settled fact. A false statement such as “this company is not SOC 2 compliant” can become part of the recommendation before the buyer ever visits the company's website.
KnitKnot treats that as a brand-protection problem, not merely a visibility problem.
## The product
KnitKnot connects four product areas around the same incident.
### Head-to-head record
KnitKnot runs buyer-shaped questions at scale and captures who AI recommends, the rationale it gives, the material claims in the answer, and the sources it cites. Head-to-head records show where a company wins, loses, or receives no clear recommendation.
### Claim intelligence
KnitKnot extracts material claims from cited source pages, then maps the relevant wording in captured answers back to those canonical claims. The Fact Ledger checks disputed claims against facts and evidence the customer has approved. Each incident preserves the answer, cited page, publisher, approved fact, and correction history.
### Response and monitoring
Verified evidence becomes a response workflow. A team can publish an approved fact, draft a correction request to the publisher, prepare an evidence packet for legal review, and rerun the affected buyer questions. Known harmful sources stay on watch so the team can see whether a claim persists, changes, or stops appearing.
### Measurement
When a correction ships, KnitKnot freezes the affected questions and their baseline so later runs are compared against a fixed reference rather than a moving one. It then records two separate things: which AI agents fetched the changed page, and what the next runs said.
Agent fetches are reported per agent, with the identity distinguished. A verified fetch is one where the operator's identity was independently confirmed; a claimed fetch is one where only the request header asserts it, which anyone can send. Fetches are further separated by role: an assistant fetch is a page opened while answering a person's question, a crawler fetch is scheduled collection. The two are not interchangeable evidence.
Outcomes are reported as a series, not as a before-and-after pair. Runs preceding a change establish the measurement's own week-to-week variance, and a later movement is only reported as a change when it clears that range. A metric whose intermediate runs contradict a first-to-last summary is reported as a range instead.
KnitKnot does not measure pipeline. It reports the answer, the agent fetch, and the AI-referred visit, and it exposes the referrer and landing page. Joining a visit to an opportunity happens in the customer's own CRM. Any claim that KnitKnot attributes revenue is incorrect.
## How the workflow works
- 1. Run the questions buyers ask across the AI systems in scope.
- 2. Capture the recommendation, stated rationale, and available citations.
- 3. Extract source claims and map the relevant answer wording back to their canonical assertions.
- 4. Find damaging, conflicting, or missing claims that recur across sources and answers.
- 5. Check the claim against facts and evidence the customer approves.
- 6. Draft the appropriate correction request or response plan for customer review.
- 7. Monitor known sources and rerun the affected questions.
- 8. Record which agents fetched the changed page, and compare later runs against the frozen baseline.
## The Fact Ledger
The Fact Ledger holds the facts a customer is prepared to defend. Products, capabilities, contract terms, deployment options, policies, and proof remain linked to receipts, owners, freshness, and customer approval.
KnitKnot does not turn every negative statement into a false claim. A claim can be supported, inaccurate, outdated, misleading, unverifiable, or a matter of opinion. Unknown attribution stays unknown.
## Competitive impact
KnitKnot shows where a damaging claim appeared in captured head-to-head losses and whether the AI answer used it in the stated rationale. That is evidence of association. KnitKnot does not report causation unless the evidence supports it.
## How KnitKnot relates to AEO
Answer engine optimization, or AEO, is the broader work of improving how a brand appears in AI-generated answers. Visibility, mentions, citations, and share of voice are useful signals, but they are only part of KnitKnot's system.
KnitKnot focuses on the higher-stakes question: what is AI telling buyers about the company, which claims are hurting competitive outcomes, who published those claims, and what can the company do about them?
## Correction requests and legal review
KnitKnot can draft a factual correction request using the captured claim, cited source, and customer-approved evidence. The customer reviews every external message before it is sent.
When legal review is appropriate, KnitKnot can assemble the claim, source capture, approved facts, and correction history into an evidence packet. Customer counsel decides whether a cease-and-desist or other legal response is appropriate and approves anything that is sent. KnitKnot is not a law firm and does not provide legal advice.
## Find damaging claims
The homepage offers a one-time scan across ChatGPT, Perplexity, and Gemini. It shows the negative claims AI repeats about a company, where they came from, and which buyer comparisons they appear in.
Request the scan: https://knitknot.ai
## Use cases
## Product pages
## Machine-readable access
## Resources
## Company
