Be the brand AI recommends.
When a buyer asks ChatGPT, Gemini, Claude, Perplexity, Grok or Google AI Overviews which product to choose — a D2C label, a SaaS tool, an insurer, a fund, a loan or a card — be the answer, and be right in it. Most tools hand you a score; we find the one reason you're not recommended and fix it.
Free evaluation · no credit card.
Asked the way your audience asks
Diagnose
one root cause
Correct
from your facts
Prove
a crawler fetched it
The problem
AI is choosing for your buyers — before they ever reach your site.
Reputation used to live on page one of Google and in your reviews — you could see it, and SEO and ORM could move it. Now your buyer asks an AI assistant instead, and it answers the comparison, the ‘is it any good’, the ‘which is cheaper’ with one synthesized verdict you never see. AI search is the new ORM: if AI names a competitor instead of you — or repeats an out-of-date price, a wrong claim, a stale spec — that buyer is gone before they reach your site, and ranking a page won't change what the model already learned. A visibility score tells you it's happening. It doesn't tell you why, or what to do.
In the wild
What this looks like
A comparison that quotes a stale price
A buyer asks which option is better value and the AI assistant quotes a price you retired two quarters ago, making you look pricier than you are. We trace it to the source, publish the corrected figure, and confirm the next answer quotes it right.
The 'is it any good' answered by a rival
Someone asks if your product is worth it, and the AI assistant leans on a third-party summary that misses your strongest feature, then recommends a competitor. We find the one missing fact driving the pivot, supply it in your words, and watch share-of-answer swing back.
The recommendation you're absent from
A buyer asks for the top choices for their use case and your name never appears, even though you fit best. We find why the model has no reason to include you, add the evidence it needs, and track your entry into the recommended set.
A spec locked inside a PDF
A buyer asks whether you support a capability you clearly do, and the AI assistant answers 'unclear' because the proof lives in a datasheet PDF or a script-only table no crawler reads. We restructure it as plain text and structured data, then confirm the next answer says yes.
The launch the models haven't caught up to
You ship a new product or tier, but for weeks the AI assistants keep answering from last quarter's page and route buyers to the old option — or a rival filling the gap. We show which engines are behind, publish the new facts, and watch each one refresh.
A price you retired 8 months ago
A buyer asks what you cost and hears a figure you stopped charging months back — high enough to lose the deal, or low enough to break the conversation at checkout. We set the current price as a dated, machine-readable fact so the next answer is right.
A co-founder who left two years ago
A buyer asks who runs the company and the AI assistant confidently names a founder who moved on years ago. We set the current office-holder as the verified fact, publish it as dated, extractable text, and re-measure until the answer reflects who's actually in post.
The product you renamed, still on sale
An AI assistant recommends a product you renamed or retired, sending buyers after something they'll never find. We check whether your current line-up is even in your server-rendered HTML, flag any third-party like G2 outranking you, and ship the corrected product facts.
A fake review the AI keeps repeating
An AI assistant answers 'is it a scam' by echoing a defamatory forum post or a fake one-star review from a third-party site. We name the exact source, hand you both levers — an authoritative counter and a report-request draft — and re-measure until the accurate answer wins.
A rate, fee or eligibility AI quotes wrong
A borrower asks for your lowest loan rate, or a saver which account pays most, and the AI assistant answers with a figure you revised or an eligibility rule that no longer holds. We set the current number as a dated fact and publish a compliance-safe correction — built only from your approved facts and disclosures, never advice — so the next answer is right.
How it works for you
Find the one cause. Fix it from your facts. Prove it landed.
- 1
Measure
We ask the buyer questions that matter across all six engines and capture every answer verbatim, with its sources.
- 2
Diagnose
Every wrong or missing answer resolves to exactly one root cause — a comparison table that's JavaScript-only, a spec buried in a PDF, a competitor cited in your place.
- 3
Correct
A ready-to-publish correction built only from your own verified facts — paste it, or one-click publish to WordPress / Shopify.
- 4
Prove
We confirm a real AI crawler fetched it, then measure the lift against a matched control — the traffic and revenue AI drove, not a vanity number.
The substance
How it works for your brand
We start from the questions your buyers actually ask
We don't test vanity phrases — we build the real purchase-intent questions in your category: the comparisons, the 'is it worth it', the 'which is cheaper', the 'best for my use case'. Every answer we measure is one a real buyer sees, tuned to your products and market, and you add the questions your sales team hears every week.
A root cause, not another dashboard number
A score tells you AI describes you badly; it never tells you why — so it isn't actionable. For each wrong answer we trace the exact input the model leaned on — a stale spec, a competitor's framing, a missing claim — and name the one thing to change. That's the difference between a report you file and a correction you ship.
Corrections are built only from facts you approve
We never invent claims or stuff keywords — every correction is assembled from your own verified facts, and nothing publishes until you sign off. That keeps you accurate and on-brand, and it holds up when a model checks it against other sources: truthful content the engines trust, not manipulation they'll discount.
One-click publish to where the answer is formed
Approve a correction and push it live to your site and connected channels in a click — no engineering ticket, no sprint. The fix lands where AI engines actually read, and because it's addressed to a specific question, we can tell precisely when an engine picks it up.
Proof against a matched control
We don't ask you to take the lift on faith. After a correction ships we hold the changed questions against a matched, untouched set, so any movement in recommendation and share-of-answer is measured, not assumed — the before and after, per engine, per question, control alongside.
Six engines that disagree, reconciled in one view
ChatGPT, Gemini, Claude, Perplexity, Grok and Google AI Overviews rarely tell the same story about you on the same day. Instead of averaging that into one number that hides the problem, we show every engine's answer verbatim side by side — which engine is wrong, on which question, against which source — and fixing the underlying record moves the laggards for you.
Corrections engines can actually read
A correction only works if a crawler can parse it — so we don't hand you a paragraph to bury in a blog post. Each fix is generated as JSON-LD, a clean HTML fact block and an updated llms.txt, the exact shape engines consume, so a model can lift the fact straight out and the answer holds instead of drifting back.
Recommendation is one problem; getting your facts right is the other
Being absent from the recommended set costs you buyers — but so does being present and described wrong. AI routinely states key facts incorrectly: a stale price, a former CEO named as current, a discontinued product. You define the facts that matter as a record you own, and we measure how correctly all six engines state each — in the same loop as share-of-answer.
A verified record you define, graded fact by fact across every engine
You write down the facts that must be right — price, founder, CEO in post, HQ, funding, live product names — as a record you approve. We ask each engine the buyer's question about each one and grade the answer correct, outdated or wrong, distinguishing a stale value from a fabricated one because the fixes differ. A per-fact, per-engine fidelity view, captured verbatim.
The root cause of a wrong fact, computed from your own page
When an engine states a fact wrong, we fetch your page the way a plain crawler does — no JavaScript — and check whether the current value is even there. From that we resolve one cause: it isn't machine-readable, it isn't indexed yet, a third-party like G2 or Wikipedia outranks your record, or your page is right and one engine is just stale. Each cause carries its own fix, so you correct the real reason, not a recurring symptom.
A correction pack you paste on your domain, then prove
For each wrong fact we generate a copy-paste pack built only from your approved record — JSON-LD, a plain-text HTML fact block, an llms.txt entry — each carrying the value and its effective date. You publish on your own domain, we confirm a real AI crawler fetched it, and we re-ask until the answers flip to correct — the same proven loop as a recommendation lift.
Capabilities
Built for how you work
Competitor share-of-answer
See exactly who AI recommends instead of you, on which questions, and why — then flip it.
Sentiment & citation intelligence
How AI frames you, and which sources it trusts to say it. Win the citations that move the answer.
Fixes that ship
Comparison pages, FAQ schema, llms.txt — generated from your facts and published in a few clicks.
Proof in real revenue
Forecast the lift before you spend, then re-measure the before/after against a control — in the open.
Machine-readable by default
Corrections ship as JSON-LD, HTML fact blocks and llms.txt — the structured formats engines actually read, not a blog post they might skim.
Six engines, reconciled daily
ChatGPT, Gemini, Claude, Perplexity, Grok and Google AI Overviews often disagree about you; see all six side by side and fix the one that's wrong.
AI fact-accuracy check
Define your key brand facts — current price, founder and CEO, HQ, funding, the real product names — and see how correctly all six engines state each one, graded correct, outdated or wrong against the record you approve. It's not just whether AI recommends you; it's whether it gets you right.
Computed root cause per wrong fact
Each misstated fact resolves to one cause: it isn't in your page's server-rendered HTML, it's not indexed yet, it's outranked by a third-party like G2 or Wikipedia, or it's simply stale in one model — computed by fetching your page the way a crawler does, not guessed.
Copy-paste correction pack
Every fact fix ships as JSON-LD, an HTML fact block and an llms.txt entry, each dated with an effective-from so the current value is unambiguous — publish it on your own domain, then re-measure to prove the answer flipped.
Reputation defense
When an AI assistant repeats a damaging claim or a fake review about you, we don't just flag it — we name the exact sources feeding it, the cited posts and domains poisoning the answer. Then you get two levers: counter it with authoritative content built from your verified record that the engine can cite instead, or generate a report/takedown-request draft aimed at the source. Not just what AI says, but what's driving it and how to push back.
Figure-level accuracy for regulated products
For a financial brand — a bank, insurer, asset manager, lender, broker or neobank — the numbers are the product: a rate, an APR, an expense ratio, a fee, coverage, eligibility. Define them as a verified record and see how correctly all six engines state each one, and every correction ships compliance-safe — assembled only from your approved product facts and mandated disclosures, dated with an effective-from, never a claim we invented and never advice.
Inside the team
Who uses it inside a brand team
CMO
Watches share-of-answer and sentiment across engines the way they watch share of voice, and gets a defensible line from correction to recommendation lift to revenue.
Brand manager
Owns how the brand is described in AI answers day to day, approves corrections, and catches a wrong claim or stale price before it costs pipeline.
SEO and content lead
Sees which sources engines cite, fixes the ones dragging answers down, and publishes corrections to the pages that feed the models, all in one loop.
Product marketer
Owns the verified record of prices, specs and product names, and makes sure a new launch, renamed line or price change shows up correctly across every engine instead of being answered from outdated material — the facts, not just the comparisons.
Founder or CEO
At a smaller company, opens one view to see whether AI recommends the product and how it frames the pitch, catches an AI assistant naming a co-founder who left or quoting a retired price, and approves the handful of corrections that matter without needing a marketing team to interpret a dashboard.
PR and communications lead
Tracks how AI assistants characterise the brand and which facts they state — leadership, HQ, funding — and catches a wrong or damaging claim at the source before it hardens across every engine.
RevOps or revenue lead
Watches the facts that break deals — the price a buyer hears, the plan they think exists — treats a misstated fact as a pipeline leak, and ties each shipped correction to the recommendation and revenue movement it produced against a control.
The takeaway
What you walk away with
FAQ
Questions, answered
How is this different from an AI-visibility score?
A score tells you that AI represents you poorly; it stops there. We name the one input causing each wrong answer and ship a verified correction, then prove the answer changed. You get an action and a result, not a number to worry about.
Do you only tell me whether AI recommends me, or also whether it gets my facts right?
Both, in the same loop. Alongside competitor share-of-answer we run a fact-accuracy check: you define your key facts — current price, founder and CEO, HQ, funding, live product names — as a verified record, and we grade how correctly each of the six engines states every one, marking it correct, outdated or wrong. Being recommended but described with a stale price or a former CEO still loses the deal, so we track visibility and accuracy together.
How do you work out why AI is stating a fact wrong?
We fetch your official page the way a crawler does, with no JavaScript, and check whether the current value is even in that server-rendered HTML. From that we compute one cause: the fact isn't machine-readable (it's in a script-only widget or a PDF), it's on your page but not indexed yet, a third-party like G2 or Wikipedia is outranking your own record, or your page is right and one engine is just stale. Each cause has its own fix, so you correct the reason rather than the symptom.
What do I actually publish to fix a wrong fact?
A copy-paste correction pack built only from your approved record — JSON-LD structured data, a plain-text HTML fact block, and an llms.txt entry, each stamped with the date the value became effective so the current version is unambiguous. You publish it on your own domain, we confirm an AI crawler fetched it, and we re-ask the same fact questions until the answers flip to correct.
Will you publish anything that is not true about us?
No. Every correction is built only from facts you supply and approve, and nothing goes live without your sign-off. That is deliberate, accurate and on-brand content is what the engines trust and keep, so it is also what actually holds.
Which AI engines do you track?
We track the major AI assistants and AI answer surfaces buyers use to research and compare before they buy, and we refresh them daily. You see how each one describes and recommends you, so you are not guessing which engine is telling which story.
How do you prove the change drove the result?
After a correction ships we compare the changed questions against a matched control set we left untouched, so the movement is measured, not assumed. You get before-and-after recommendation and share-of-answer per engine and per question.
Do we need engineering to publish the fixes?
No. Approved corrections publish to your site and connected channels in a click, without a code change or a sprint. Your marketing team can run the whole measure-diagnose-correct-prove loop on its own.
How quickly do the AI answers actually change after we publish?
It depends on how often each engine re-crawls the source, which is why we do not stop at publishing. We confirm a real AI crawler fetched the corrected page, then keep re-measuring the same questions until the answer flips, so you see the change land per engine rather than assuming it did. Some engines refresh within days; others lag, and the view shows you which is which.
Do you cover markets and questions that aren't in English?
Yes. We measure the questions your buyers actually ask in the languages of the markets you sell into, and corrections are published natively in those languages rather than machine-translated. A capability that is only documented in English is a common reason an AI assistant answers a non-English buyer wrong, and we surface that as its own cause.
Why not just average the six engines into one visibility score?
Because the average hides the problem you need to fix. If one engine recommends you and another quotes a stale price, a blended score buries both. We keep every engine's answer separate and verbatim so you can act on the specific engine and question that is costing you, then confirm each one moved.
Be the brand AI recommends.
Free evaluation · no credit card.
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