How Claim Engine works
Four phases: Audit, Architect, Deploy and Maintain. We find the claims AI makes about your category, pick the ones to own, and help your team change the sources behind them. A Reddit thread, a LinkedIn post, a review and a product page each have their own rules, and all of them can shape an AI answer.
Audit
The Category Claim Map: every claim AI makes about your category
Architect
Three target claims and the sources behind them
Deploy
Your people post; we brief, draft and review
Maintain
Rerun the questions and report the claim metrics
01 / Audit
First, the Category Claim Map records what AI says about your category.
- 100 buyer questions across 5 AI engines, 3 runs each
- Record every claim each answer makes about you and 3 to 5 competitors, and whether your own claims survive, get paraphrased or get dropped
- Trace the most likely sources behind each claim, name the buying criteria and who owns each one, and score how fragile each claim is
02 / Architect
Then we choose three target claims and the sources behind them.
- Pick three target claims: one to defend, one to shift, one to establish
- Choose the relevant thread types for each claim instead of assuming Reddit is always the answer
- Set the evidence, disclosure requirements and review owners for each source, and lay out a 90-day plan
03 / Deploy
Your people publish. We research, brief, draft and review.
- Build evidence assets for your site: a data point, benchmark or named result behind each claim
- Brief your employees and advisors on the threads that matter; they post from their own accounts with their affiliation disclosed
- Draft LinkedIn posts for named executives and review every draft against community rules and evidence before it goes out
04 / Maintain
We rerun the same questions and report the four claim metrics.
- Report claim survival, claim adoption, criteria ownership, fragility on the same buyer-question panel
- Flag new claims about you as they start forming and sources that changed or went stale
- Swap a target claim when one is won or the category shifts
The thread changes by source
The same process applies to every source type, but the standards for a review, a product page or a community discussion are different.
Community
Reddit, forums, Discord, Stack Exchange, Quora
Professional
Editorial
Publications, best-of lists, analyst notes, podcasts, video
Review
G2, Capterra, Trustpilot, app stores
Owned
Your site, docs, research, help center
Entity
Wikipedia, Wikidata, directories, company profiles, schema
One question, different sources
What does a useful answer look like in practice?
Suppose a buyer asks: “How should a 200-person company compare customer support platforms?” This is an illustrative example, not a client result. The question stays the same; the contribution changes to fit the place where someone encounters it.
Start with the gap
Run this question and close variants through the AI engines in scope. Note which vendors appear, whether the answer discusses migration, coverage and cost, and which sources it cites. Check where buyers already ask the question before deciding whether to contribute at all.
Reddit / community
The contribution: One of your employees, briefed by us, joins an existing discussion only if the rules allow it, identifies their connection, and shares a vendor-neutral checklist: migration effort, reporting needs, support coverage and the cost of switching. No planted question, pitch or link drop.
The check: Did the reply help the discussion and remain within community rules? Later, does that discussion appear in relevant AI answers?
LinkedIn / professional
The contribution: A named support leader on your team, working from a draft we prepared, explains the tradeoffs they have seen during migrations, with one concrete example and its limits. They answer follow-up questions in their own voice rather than reposting the Reddit reply.
The check: Do peers engage with the substance? Is the perspective reflected accurately when AI answers similar questions?
Owned / site
The contribution: Publish a comparison worksheet with evaluation criteria, sample questions to ask vendors, clear product fit and a date. Make any claim about your own product verifiable.
The check: Can buyers find and use it? Is the page accessible and cited or summarized accurately?
Editorial / review / entity
The contribution: Offer original migration data to a relevant editor if it stands on its own. Invite actual customers to leave honest reviews without scripting them. Correct inconsistent company and product facts across profiles.
The check: Are independent sources accurate and current? Do citations and product descriptions change without mistaking a placement for a result?
A month later, ask the same buyer question again and compare the claims and citations with the baseline. Keep the useful sources current; do not treat a new mention as proof that any single contribution caused it. See how we measure progress.
The Index–Thread Model in Practice
Every step of our process maps to a layer of the Index–Thread Model. The model tells us which activities matter and why they build on each other.
- Community discussions
- Professional posts
- Editorial and reviews
- Owned and entity sources
A claim that holds up on its own source
- Evidence and accuracy
- Disclosure and source rules
- Clear answers
- Accessible source pages
Information people can trust and AI can retrieve
- Buyer question panel
- Claims about you and competitors
- Citation source tracking
- Claim survival and adoption
Measured changes in the claims AI answers make
The compounding loop
See which of your claims survive an AI summary
Paste your positioning and a buyer question. The free Compression Test shows which claims an AI assistant keeps, which it drops, and which competitor sets each buying criterion.