Monitor the answers
Continuous reads of what every major engine says about your brand across the questions that matter — claims, facts, sentiment, and which sources each answer cites. Every error logged with its evidence.
The Growth Engine System 03 Design Partner — 3 seats
Trust & Citations.
Be worth citing.
AI systems describe your brand to buyers every day, whether you look or not. Trust is the system that keeps that description accurate — and worth repeating.
Accountable to one number
96% Answer accuracy
Why this system exists
The answer about you is being written from sources you never audited.
ORL Trust & Citations is an answer-accuracy and corroboration system for any company AI talks about. Engines repeat what independent sources let them verify: when your facts are stale, contradictory or absent, answers hedge or recommend someone else. Trust monitors what every major engine says about you, traces each error to its source, corrects the record, and builds the third-party citation surface that makes engines confident naming you.
74%
of buyers consult independent reviews and third-party sources — and so do the engines answering them.
Engines assemble your brand from whatever they can verify: old listings, third-party profiles, forum threads, press from three years ago. When those sources disagree with you — or with each other — the answer hedges, goes stale, or names a competitor whose record is cleaner. The least-trusted source about you is the one you wrote yourself; everything else is currently unmanaged.
- Dead lastwhere a brand’s own collateral ranks among sources buyers trust
- 2–3independent sources a typical answer leans on per claim
- Weekshow long a wrong fact keeps repeating once it enters the answer layer
How the system runs
Not a campaign. A loop.
Trace and correct
Each inaccuracy is traced to the source feeding it — a stale listing, a conflicting page, an old article — and corrected there. Fix the source and the answers follow; the dashboard proves when they do.
Reconcile the record
One canonical fact sheet — names, numbers, claims, dates — reconciled across every surface you control and every major surface you don’t. Divergence reads as uncertainty, and uncertainty gets you omitted.
Build the citations
The independent surface engines verify against, built deliberately: reviews cultivated where your category’s answers actually look, press with checkable facts in it, data other publications want to cite.
↻ Step four feeds step one. The system compounds; a campaign would end here.
Installed, not rented
Fire us. Keep the system.
The difference between a service and an installation is what exists the day after the engagement ends. Here is what stays:
- The answer-accuracy monitor and its history
- The canonical fact sheet, reconciled everywhere
- The citation map — who says what about you, where
- The correction playbook, tuned by outcome
What gets installed
The build sheet.
- Continuous accuracy monitoring across four engines
- Error log with source-level trace and fix status
- Cross-surface fact reconciliation
- Citation-surface program — reviews, press, data
- Quarterly sentiment and accuracy report
- Correction sprints when the record breaks
The only report that matters
96% Answer accuracy
Across the questions where engines describe your brand, the percentage of answers that are factually right about you — prices, claims, capabilities, availability. Monitored continuously, corrected at the source.
Before you ask
Trust & Citations questions.
AI is saying something wrong about us. Can it be fixed?
Usually, yes — but not by complaining to the engine. Wrong answers almost always trace to a source: a stale listing, an outdated article, conflicting facts across your own surfaces. Fix the source, corroborate the correction somewhere independent, and the answers follow. Trust exists to run that loop continuously instead of once.
Why do engines hedge instead of recommending us?
Because a specific claim carries risk for the model, and the bar for stating one is corroboration. A fact that appears only on your own site is a marketing claim; the same fact reflected in a registry, a review platform, press coverage or a market report becomes safe to repeat. Most under-recommended brands are not under-published — they are under-corroborated.
What counts as a citation surface?
The set of independent sources engines lean on when answering questions in your category: review platforms, industry press, registries and official bodies, market reports, expert commentary. It differs by category — which is why the first step is reading which sources your category’s answers actually cite, not guessing.
Is this PR?
It overlaps where PR produces checkable facts. A feature story builds awareness; a story containing your verifiable numbers builds answers. Trust prioritizes coverage engines can quote — specifics, data, named outcomes — over volume of mentions, and it measures success in citations, not clippings.
How fast do corrections show up?
Source fixes propagate on the engines’ own refresh cycles — typically weeks, not days. Accuracy corrections tend to land faster than new citations, because removing a contradiction is easier than establishing new trust. The dashboard tracks both separately so you always know which lever is moving.
What do we keep if the engagement ends?
The accuracy monitor and its full history, the canonical fact sheet every surface was reconciled against, the citation map of who says what about you where, and the correction playbook. The record stays clean because the system that keeps it clean stays installed.
It starts with the Accuracy Read.
We pull what every major engine currently says about your brand, mark every claim right or wrong with the citation behind it, and hand you the log. Most brands have never seen their own record — it is usually a persuasive document.
Get the Accuracy ReadDesign partner program · 3 seats · read credits toward month one