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Answer integrity

Quality control for what AI says about your company.

Your buyers stopped opening ten links. They ask an assistant, get one answer, and act on it. When that answer is out of date, you lose the deal before anyone reaches your site.

We find the wrong answers across ChatGPT, Claude, Gemini and Perplexity, fix the page they came from, then ask again to check the answer changed.

We ask
OpenAI · Anthropic · Google · Perplexity
Every number shows
the rate, the error bar, the run count
Under 5 runs
no number at all
Exhibit Worked example, not a measurement of Slack

Slack's free plan works well for a small team. It keeps your 10,000 most recent messages, so nothing is lost while you stay under that. Paid plans add unlimited history and Slack Connect.

surface assistant · api grounding grounded_search geo US/en temp 0.7
Sources the answer cited
top10teamchat.example.com/best-slack-alternatives does not support it
slack.com/pricing supports it
Your approved fact says otherwise Contradicted

The Slack free plan keeps 90 days of message history. The 10,000-message limit ended on 1 September 2022.

in force 2022-09-01 → current · sensitivity material · approved by pricing-ops

9%95% CI 5%–15%n=116 Critical

Why counting mentions misses this

The answer above is sourced, and wrong.

The brand is named, the tone is positive, and one of the two citations is Slack's own pricing page. Every share-of-voice tool scores that as a win. The limit it quotes ended in 2022.

Check it yourself: ask any assistant what Slack's free plan keeps, then open slack.com/pricing.

Slack's free plan works well for a small team. It keeps your 10,000 most recent messages. Paid plans add unlimited history and Slack Connect.

Approved record · in force 2022-09-01 → current

The Slack free plan keeps 90 days of message history.

01

One answer, no second opinion

Search handed your buyer ten links to compare. An assistant hands them one answer. If they act on the wrong one they never reach your site, so nothing in your analytics records it.

02

It used to be true

Slack really did keep 10,000 messages until September 2022, and thousands of comparison pages still say so. Every fact you give us carries a start date and an expiry, because an answer can be correctly sourced and still wrong.

03

The fix is upstream

A stale comparison page carried the old limit and the pricing page did not outrank it. Publish a dated correction, fix the doc, ask again, and see whether the answer moved.

How it works

Five steps, run on a schedule.

Mention counts are an input here, never the deliverable. The loop turns a wrong answer into a corrected one, then into evidence that the fix worked.

01

Write down what is true

Each fact gets a source, an owner, a start date and an expiry.

Truth registry
02

Ask what buyers ask

Real buyer questions, asked repeatedly across four assistants. We save the exact setup: model, version, grounding, country.

Observatory
03

Check every claim

Every statement is checked against your facts. Every citation is checked for whether it backs the claim.

Answer desk
04

Fix the record

A fixed list of moves: publish a dated correction, fix the doc, add the comparison page. Each carries its evidence.

Actions
05

Prove it moved

Ask again, compare against an untouched baseline, report the difference with a p-value and what we cannot rule out.

Experiments

Six refusals, enforced in code

Six things this product will not do.

Each one is a failing test. If a release ships a blended score or a bare percentage, the build goes red before you see it.

One score you can screenshot

Ask about you by name and you get mentioned. Ask for a vendor recommendation and you might not. Averaging the two makes a flattering number that means nothing. We keep them apart.

assertNoBlending() throws · tests/unit/intent.test.ts

A percentage with no sample size

Every rate ships with its error bar and its run count. Under five runs you get "insufficient data".

domain/stats.ts · tests/unit/stats.test.ts

An alert because a number wobbled

A change is reported only if it clears a significance test, moves at least ten points, and survives a correction for everything else tested that round.

two-proportion z-test, BH at q=0.1 · services/dashboard.ts

A made-up impact estimate

A fix gets a predicted range only if your workspace already holds comparable experiments. Otherwise it ships as an experiment.

deriveExpectedRange() · tests/unit/priority.test.ts

Reviews and posts we manufacture

There is no connector for posting anywhere, and none for generating reviews. The one review action asks your real customers.

ACTION_TYPES closed enum · tests/unit/product-copy.test.ts

A promise to make the models obey

Nobody outside a lab decides what a model says. We measure it, fix what it reads, and test whether the answers moved. A lint fails the build if this page says otherwise.

banned-claims lint over src/ · tests/unit/product-copy.test.ts

Measurement design

The rules behind every number.

These settings decide what the console shows you. The same page ships inside the product. If a line on it stops being true, that is a bug.

Sampling and inference settings, current release.
Fewest runs 5 per question cluster per window before any rate is shown
Most runs 20, spent where demand, value, volatility and defect risk are highest
Error bar 95% Wilson score interval, still correct at 0 hits and at all hits
Change detection two-proportion z-test, p < 0.05, at least a 10-point move
Multiple testing Benjamini-Hochberg at q = 0.1
Under the floor no number, just "insufficient data"
Saved with each run provider, model, version, access mode, grounding, search mode, geo, language, personalisation, system config hash, temperature, seed
Assistants covered OpenAI, Anthropic, Google, Perplexity

Fifty clusters across four assistants for a month costs $400 to $1,000 in model and search spend before anything else. That is why this is not a $49 tool. At $49 nobody can ask often enough to know whether the answer is right.

Pricing

Priced by how many buyer questions we watch.

Answer Risk Audit Free one time

We load your facts, ask your highest-intent questions, and hand back every wrong answer we can evidence. The report is yours either way.

Start the audit
Monitor $750 / month

50 question clusters across four assistants, sampled weekly. Alerts have to clear a significance test.

Talk it through
Operate $2,000 / month

100 clusters, sampled daily, plus the full fact registry, the action list and the experiment ledger.

Talk it through
Enterprise $5,000+ / month

Multiple brands or clients in one place, CRM handoff, approval trails and full export.

Talk it through

Start with the free audit

See what the assistants are telling your buyers this week.

The audit is the whole product, run once by hand on your domain. Most teams have never read a transcript of what the assistants say about them.

  • We load your facts from your docs. You approve each one before we ask anything.
  • We ask your highest-intent questions across every assistant this deployment has a key for.
  • You get every wrong answer we can evidence, with transcripts, setups and citations.
  • If we find nothing worth fixing, we say so and you owe us nothing.

Request an answer audit

Two fields. The sample starts immediately and the report link appears here.

This deployment has no assistant API keys configured. An audit requested now runs the full pipeline against a deterministic stand-in, not against ChatGPT, Claude, Gemini or Perplexity. The report will say so at the top, and none of its numbers describe what a real assistant tells your buyers.

We keep your request so we can run the audit, and delete it if you ask. We ask nothing and email nobody until you approve the facts we load.