AI is increasingly informing decisions. What it says about your brand shapes commercial outcomes.
Search is now a conversation.
People are increasingly asking AI what to buy, trust and consider before they make a decision.
We're in the room.
Live queries across four AI engines and sentiment from six forum communities, run on a cadence.
It compounds.
Every listening cycle sharpens the picture, and the longer we listen, the harder your intelligence is to replicate.
What a finding looks like.
One answer, traced to its source and checked against the record.
The answer
“It's typically taken as a once-daily injection alongside diet and exercise.”
Source cited
clinic-directory.example / patient-guides
Third-party clinic listing, not a manufacturer or regulatory source.
Checked against
The approved label, dosing and administration section.
Verdict
Contradicts the label
Approved dosing frequency is weekly, not daily.
Basis
Present in 28 of 240 classified answers.
Held 3 consecutive scans · 3 of 4 engines
Every figure carries its denominator and the cycle that produced it.
See the information environment as it actually is.
Six questions, asked continuously, of every engine that answers about your brand.
Representation and narrative
What is AI saying?
Who is named, how they are described, and against a stated base.
Movement across cycles
What changed?
Confirmed beyond statistical noise and sustained across cycles, never inferred from a two scan difference.
Evidence and regulated truth
Is it accurate?
Material claims traced to their source and checked against the record.
Competitive position
Who is gaining ground?
Head to head, share of the category narrative, displacement.
Treatment pathway and access
Where is it breaking?
Where recognition stops converting into selection.
Prioritised intervention
What requires action?
With the retest that closes the loop, on the record.
Three functions, one information environment.
Commercial
Where the brand stands
Presence and share of narrative, with the base stated. Head-to-head record, and who is displacing you. What moved this cycle, and how many scans it has held.
Medical & Regulatory
Whether the answer holds up
Claims checked against the approved label. Guideline concordance and registry-verified facts. Representation errors, evidenced and dated.
Market Access
What stops adoption
Access and formulary conditions as AI describes them. HTA and payer determinations, by jurisdiction. The friction between being named and being chosen.
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?”
- 100%
Verified mention detection
Independent evaluation agreed with every brand identification we tested.
- 95%
Confidence interval on every score
We measure the uncertainty behind every score and publish it at 95%, so you know how much weight the number will carry.
- 85.4% up from 43%
Sentiment, published honestly
Sentiment is subjective and much harder to grade than a mention. We publish our real agreement rate rather than a rounded claim, and we update it as it improves.
AlphaCitation Advisory
Disappearing from the answer is a board-level risk.
Govern it like one. Named oversight of your programme by the practice that built the engine: quarterly executive briefings with the confidence intervals attached, prescribed remediation against each finding, and a retest that closes the loop on record.
In a regulated market? Label Ledger checks what AI claims about your medicine against your approved label, on the record.
Request a briefing