AlphaCitation Enterprise
Govern the AI information environment around your brands.
AlphaCitation is configured to your portfolio, your markets and your real competitive set, then run on a cadence. Your teams work from one measured record of what AI says about you, what that rests on, and what requires action.
Configured for Commercial, Brand, Medical, Regulatory, Market Access and Competitive Intelligence teams, and for the leadership they report to — one measurement, read for each function's decision.
Monitor measures your brands. An engagement is configured around your portfolio and read for the people who act on it.
AlphaCitation Monitor tracks how AI represents your brands — up to 10 on the Agency plan — across four engines, re-measured on a recurring schedule, with your team reading the results. For many teams that is the whole requirement.
An engagement is for what self-service is not set up to do. The questions are configured with your team around your portfolio, markets, competitive set and buyer personas, rather than drawn from a standard battery. For regulated portfolios, Label Ledger checks what AI says against your own approved label and holds each discrepancy as a governed case — it is available only in an engagement. And an analyst interprets each reading and briefs the teams who act on it.
What an engagement provides
Three pillars, and they are not interchangeable: what is configured before the measurement runs, what runs, and what reaches a decision-maker.
Configured intelligence
The measurement is scoped to your world before it runs, so the readings are about your market rather than a generic category.
- Your brand portfolio and the real competitive set
- Categories, target markets and buyer personas
- Many brands across many markets in one engagement
- One methodology throughout, so the reads are comparable
Continuous surveillance
Your configured questions, put to the same engines on the cadence you set, so movement across your portfolio is measured rather than asserted.
- Scheduled tracking across ChatGPT, Claude, Gemini and Perplexity
- Change alerts when the AI narrative shifts
- Movement measured between cycles, not asserted
- Longitudinal comparison against your own baseline
Executive intervention
Advisory, our analyst service, interprets each reading before it reaches you, so every finding arrives attached to what it implies and who acts on it.
- The reasoning behind the numbers, written for leadership
- Prioritised interventions, not a dashboard to interpret
- Recurring briefings on a set cadence
- Regulated cases governed with a verifiable audit trail
Tested before the market moved.
Scenario Lab introduces a hypothetical evidence, indication, access or competitive change into matched decision scenarios and measures how AI-mediated decisions respond — two arms, one difference, a design frozen before the first call.
In August 2026 we assumed a cardiovascular indication a weight-management medicine does not hold in its market. Favourable decisions rose 8.8 points across 147 matched pairs, in the same direction on all four engines. The brief closed by naming four events that would reopen the finding; ten days later a competing medicine was approved for a cardiovascular claim on the same attribute. Read the measurement.
How the engagement runs
One run, in three phases. Configuration is done once with your team; everything after it repeats on the cadence you set.
Discovery & configuration
We map your brands, real competitors, categories, target markets and buyer personas with your team.
Ongoing measurement
We measure how ChatGPT, Claude, Gemini and Perplexity represent and recommend you across the questions your buyers ask.
Recurring briefings
Your team receives board-ready reads on a set cadence, plus alerts when the AI narrative shifts.
Evidence discipline
Analysis you can act on is analysis you can check.
An engagement is governed intelligence rather than an opinion delivered on a schedule. Four rules make that difference verifiable rather than asserted.
Grounded in the answers themselves
Every reading is taken from answers the models actually returned, captured at the time of the scan. Nothing is inferred from what a model would probably say.
A confidence band on every read
Scores carry a stated interval rather than a bare number, so a reading that the data cannot yet support says so instead of rounding into false precision.
No fabricated market data
Where a real-world market position is independently citable, the read is anchored to that source. Where it is not, it is not asserted, so the gap between commercial reality and AI mindshare stays honest rather than estimated.
The boundary is published
Where this measurement stops is stated in public, in the same detail as what it measures. The four boundaries are on the methodology page, and every engagement is scoped inside them.
Request a briefing.
Tell us your brands and the markets that matter. We come back with a scoped, configured engagement — not a self-serve plan.
- We review your request.
- We discuss your brands, markets and priorities.
- You receive a written scope covering deliverables, cadence and price.