You're not in the room.We are.

AI is now a source. Sources get checked.

We ask four AI engines the kind of questions your customers ask, note the sources returned with each answer, check its claims against the authoritative record where one exists, show you what changed and tell you what to do next.

1. 01

   Recorded.

   Every answer we score is logged with its question, engine, model and time. Each cycle is compared with the last; you are alerted when your score falls, a new competitor leads or a head-to-head turns against you.

2. 02

   Checked.

   Claims about a medicine’s indication are checked against the approved label: the FDA in the US, the EMA for centrally authorised EU medicines. A Label Ledger finding becomes an issue only once verified.

3. 03

   Acted on.

   Each cycle comes with what to do next: the sources citing competitors and not you, who should own each fix and the review it needs. Cases sync to your Jira or ServiceNow; a rescan closes or reopens them.

See the information environment as it actually is.

Six jobs, done continuously, for every engine that answers about your brand.

1. 01

   Measure decisions

   What does AI actually recommend?

   Not whether your medicine is mentioned: whether it is chosen, and over which alternatives, with the base stated.

2. 02

   Trace the evidence

   What is the answer built on?

   The sources and claims behind consequential answers, and which of them entered or left.

3. 03

   Verify the claim

   Is it accurate?

   Claims about a medicine’s approved uses and populations checked against its regulator’s current label, dated: the FDA’s for the US, the EMA’s for medicines centrally authorised in the EU, and both as benchmarks elsewhere.

4. 04

   Diagnose change

   What moved, and is it real?

   Confirmed only beyond statistical noise and across cycles. Alerts flag a large one-cycle score drop, a new competitor or a battleground shift as it happens.

5. 05

   Act and retest

   Did it work?

   Each action owned, its expected outcome registered before the retest, and a verdict read against it.

6. 06

   Monitor continuously

   When should we look?

   Measured more often when a material signal appears. You hear from us only when something has changed.

Most tools count mentions. We built an instrument instead.

The difference shows up when somebody challenges a number and asks what sits underneath it, so every figure survives the question “how do you know?”

Read the standard: Which Evidence Settles Which ClaimOur open standard, version 3.0: which evidence can settle each kind of claim, for which market and as of which date, with conformance tests and an open benchmark.

100%0 errors

Mention detection, on a human-labelled gold set

Measured against a human-labelled gold set. Zero errors observed. We publish the bound rather than the streak: at 95% confidence, that set puts detection accuracy at 97.3% or better.

±CI95%

Every score ships with its confidence interval

95% is the confidence level, not an accuracy rate. It describes how much the sample supports the number — a wide interval on a small sample is reported as exactly that, rather than rounded away.

85.4%up from 43%

Sentiment agreement, published as measured

Graded against an independent AI judge rather than a human panel. Sentiment is a harder, more subjective call than a mention, and the figure moves between runs on the same set — we publish the measurement rather than a rounded claim.