The AI platform Pharmaceutical Manufacturing uses to get recommended on
Your buyers shortlist before they ever contact you, and they build that shortlist with AI. LBOX measures what the engines say about your company, finds where the answer is wrong or missing, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Pharmaceutical Manufacturing different
Not general marketing advice. These are the constraints your category actually operates under.
What you may claim is regulated. What the engines claim is not.
Every promotional statement you make is bound by FDA and EMA rules on claims, fair balance and safety information. An answer engine describing your product carries none of those obligations, cites sources you never approved, and is under no duty to be accurate. You are accountable for a conversation you are not part of.
The shortlist is built before the RFI exists.
Sourcing teams, formulary committees and CDMO buyers now open with an AI query: who can run sterile fill-finish at commercial scale, who holds the approvals, what are the alternatives to this supplier. By the time a request for information reaches you, the field has already been narrowed by an answer you did not see.
Your strongest proof is in the format machines read worst.
Prescribing information, capability decks, audit and inspection history, certificates of analysis, congress posters. The documents that establish you are qualified are PDFs, and answer engines extract them poorly or skip them entirely. The evidence exists and is invisible.
MLR review is slower than the answer.
Medical, legal and regulatory review exists for good reason, and it means weeks between a decision and published copy. The engines form their answer continuously. Most pharmaceutical manufacturers have never measured what that answer currently says, so review cycles are spent on pages nobody asked about.
What you get back
The console, measuring a company in Pharmaceutical Manufacturing against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See how every model describes your pipeline
Visibility, share of voice and position, measured on the unbranded questions procurement and formulary teams actually ask. Not a list you supplied.
- Ranked against the companies the engines actually named
- See where a model reads your documentation without naming you
You against the field, engine by engine
unbranded questions| Brand | Gemini | AI Overviews | OpenAI | Perplexity | Claude | ChatGPT |
|---|---|---|---|---|---|---|
| Cadence Therapeutics | 38% | 29% | 31% | 26% | 18% | 34% |
| Veritas Pharma Group | 27% | 24% | 22% | 19% | 14% | 26% |
| Northmark Biologicsyou | 31.6% | 19.4% | 24.5% | 16.3% | 12.2% | 0% |
| Halcyon Labs | 16% | 13% | 11% | 14% | 7% | 12% |
| Pinehurst CDMO | 9% | 6% | 5% | 8% | 3% | 7% |
Turn what the answers get wrong into work MLR can approve
Findings arrive as a worklist sorted by who can act: what LBOX drafts, what needs medical and regulatory sign off, what needs your web team.
- Scored against your category, not a generic SEO checklist
- Nothing publishes without your approval
The worklist
Prove the change, run after run
Every measurement is stored, so the line lengthens each cycle and the movement you paid for is on the record rather than asserted.
- The same questions, asked again and checked
- Sources broken out per model, per page
Visibility over time
every measured runKnow when the models come to read your documentation
Crawler activity captured at the edge, so you can see which models fetch your specification and regulatory pages, and how often they return.
- Every model, every page, logged over time
- Robots and content signals kept current
Crawler activity
hits by model, last 30 daysWhere you disappear as the buyer gets closer to a contract
Presence falls away as intent rises. The qualification and supplier-selection questions are the ones that convert, and the ones most pharmaceutical manufacturers lose.
- Visibility split by where the question sits in the buying journey
- Names the stage that costs you the shortlist
Visibility by buying stage
where you show up in the journeyThe sources the engines trust in pharmaceutical manufacturing
Regulators, literature and trade press decide what the engines repeat about you. Almost none of it is yours, which is exactly why it can be changed.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
904 citationsThe program for Pharmaceutical Manufacturing
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your company and your products today, through the engines' own APIs
- The questions your buyers actually ask, mapped: capability, comparison, substitution and supplier-qualification queries
- Where competitors are named instead of you, and on which questions
- Which sources the engines cite for your category, and which of those you control
- Evidence pages built from your existing regulatory and capability documentation, in a form engines can read
- MLR-ready drafts, so review approves copy that is already targeted at a measured gap
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.