Capability
AI Intelligence Platform
Matching an asset to a partner is an information problem before it is a relationship problem. We maintain three structured matrices so the shortlist is derived, not recalled.
Overview
Why matching usually fails
Conventional business development runs on individual memory. A dealmaker knows perhaps a few dozen counterparties well and proposes from that set. The proposal is fast, and it is bounded by one person's network rather than by the actual field of candidates.
The consequences show up late. An asset is shopped to companies whose therapeutic focus has shifted. A supplier is proposed without anyone checking whether its inspection history would survive the buyer's audit. A territory is targeted where reimbursement makes the commercial case implausible.
We maintain three matrices — customer, supplier and product — continuously rather than assembling them per deal. AI-assisted collection keeps them current; structuring makes them queryable. A shortlist is generated from the matrices and then reviewed by the people who will actually run the transaction.
Derived, not recalled
The candidate set comes from a structured query across the matrices, so it is not limited by which counterparties a particular dealmaker happens to know.
Continuously refreshed
Automated collection tracks pipeline, regulatory, inspection and transaction developments, so the matrices reflect the current field rather than a snapshot.
Screened before proposed
Candidates are filtered on therapeutic fit, territory rights, development stage and compliance history before they reach a shortlist.
Human judgement at the end
The platform narrows the field. The decision about who to approach and how to structure the deal stays with the people accountable for it.
The assets
The three matrices
Each matrix answers a different question. Their value comes from being queried together — a match must be viable on all three axes at once.
Customer Matrix
Who is actually in the market for this asset, in this territory, at this stage — and what have they done before?
Built from
- Therapeutic focus and current pipeline composition
- Historic in-licensing and acquisition behaviour
- Territory footprint and commercial infrastructure
- Development-stage preference and typical deal structure
- Publicly disclosed transaction terms and timing
What you get
- Ranked counterparty shortlist for a given asset and territory
- Fit rationale for each candidate rather than a bare name
- Indication of realistic deal structure based on precedent
- Flags where therapeutic focus has recently shifted
Supplier Matrix
Which manufacturing sites can actually deliver this molecule to this standard — and will still be able to in two years?
Built from
- Capability by chemistry type, modality and scale
- Regulatory documentation status including DMF and CEP coverage
- Inspection and audit history
- Capacity position and known expansion activity
- Upstream dependency and geographic concentration
What you get
- Qualified candidate sites for a given specification
- Documentation gap view before commercial discussion begins
- Continuity and concentration risk indicators
- Second-source candidates for critical molecules
Product Matrix
What is the competitive and regulatory reality of this molecule in the target market?
Built from
- Approval status and registration footprint by territory
- Patent and exclusivity position
- Competing and pipeline products against the same target
- Pricing and reimbursement environment by market
- Epidemiology and treatment-pattern context
What you get
- Territory-level opportunity and crowding assessment
- Registration-pathway implications for the asset
- Inputs into HTA pre-assessment and valuation
- Early warning where a market is becoming crowded
In practice
How the platform is used in a live deal
The matrices are infrastructure. This is the sequence in which they are applied to an actual licensing or sourcing question.
- 01
Define the question
The asset, target territories, development stage and acceptable deal structures are specified. A vague brief produces a long and useless shortlist.
- 02
Query across matrices
Candidates are drawn from the customer matrix and constrained by what the product matrix says about the territory and by what the supplier matrix says about deliverability.
- 03
Screen and rank
Candidates are filtered on therapeutic fit, territory rights, stage preference and compliance history, then ranked with an explicit rationale for each.
- 04
Human review
The transaction team reviews the ranked set, applies relationship context the data cannot capture, and agrees the approach list.
- 05
Feed results back
Outcomes — including rejections and the reasons given — return to the matrices, so the next query starts from better information.
Questions
Frequently asked
Public regulatory databases, clinical trial registries, patent and exclusivity records, disclosed transaction filings, inspection outcomes and published market data, combined with information gathered directly through our own programs. TODO: verify with business team — confirm the licensed commercial data sources before publication.
Put a question to the platform
Describe the asset or the sourcing problem and the territories that matter. We will come back with a screened shortlist and the reasoning behind it.
Related
The other capabilities
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