One programme must never see another.
Research AI fails on confidentiality long before it fails on accuracy. Retrieval is scoped to the programme the question was asked inside — enforced when documents are fetched, not filtered afterwards.
And every action is written to an audit trail a regulator can read.
A call with the engineers who built it, not a sales pitch.
This is for you if
If none of these are true we are probably not the right call yet, and we will say so.
- Researchers are pasting confidential material into public chatbots
- Literature review is the bottleneck on every programme
- You cannot evidence who accessed which programme's data
- Regulatory drafting consumes senior scientific time
What we build for research organisations
Specialised agents over your own corpora, coordinating rather than one model guessing broadly.
Retrieval boundaries enforced per programme, so cross-contamination is structurally impossible.
Who asked, what was retrieved, what was produced — retained in a form built for inspection.
Protocols, reports, correspondence and scanned material read as they actually arrive.
Retrieval across internal and published sources with provenance kept on every claim.
First-pass drafting against your templates, with every statement traceable to a source.
Confidentiality first, accuracy second.
Select an area for how the boundary is actually enforced, and what an inspector would be shown.
One programme must never see another.
Retrieval is scoped to the programme the question was asked inside. Cross-contamination is not something the model could produce even if prompted, because the boundary is applied when documents are fetched.
- Per-programme retrieval boundaries
- No shared index across programmes
- Access decisions logged per query
- Blocked attempts recorded, not silently dropped
- Survives re-indexing and model changes
- Verifiable by your security team
Researchers reach for whatever is fastest.
The danger is rarely a wrong answer — it is a scientist pasting a confidential protocol into a consumer chatbot because nothing sanctioned was quick enough. The sanctioned tool has to be the convenient one.
- Runs in your tenant, so nothing leaves
- Fast enough that the shortcut is unattractive
- Available where researchers already work
- Literature and internal corpora in one place
- No queue for access approval
- Usage visible without surveilling individuals
An answer without a source is not evidence.
Every claim carries the passage and document it came from, including page and section. Scientific review depends on chasing a citation back, and a system that cannot support that is unusable regardless of accuracy.
- Passage-level provenance on every claim
- Internal and published sources side by side
- Version and revision of the source retained
- Conflicting sources surfaced, not averaged
- Confidence stated per claim
- Exportable citation trails
Built for an inspection years later.
The audit trail records who asked, what was retrieved, which programme it was scoped to and what was produced — designed to answer an inspector rather than populate a dashboard.
- Immutable action log
- Programme scope recorded per interaction
- Retention windows under your control
- Structured for regulatory formats
- Reconstructable by someone who was not there
- No dependency on us to interpret it
One programme must never see another.
Retrieval is scoped to the programme the question was asked inside. Cross-contamination is not something the model could produce even if prompted, because the boundary is applied when documents are fetched.
- Per-programme retrieval boundaries
- No shared index across programmes
- Access decisions logged per query
- Blocked attempts recorded, not silently dropped
- Survives re-indexing and model changes
- Verifiable by your security team
Why shadow AI is the real risk.
The danger in research is rarely a wrong answer. It is a scientist pasting a confidential protocol into a consumer chatbot because nothing sanctioned was fast enough.
The whole stack runs inside your cloud, so the sanctioned tool is also the safe one.
Programme boundaries live in the retrieval layer, not in a usage policy nobody reads.
An audit trail designed to answer questions asked years later, not to populate a dashboard.
Let's talk about one programme.
The question your scientists keep asking, and the confidentiality boundary it has to respect. Nothing has to leave your environment for that conversation to be worth having.
Life sciences AI — common questions
Access is enforced in the retrieval layer. A question asked inside a programme can only fetch that programme's material, so cross-programme leakage is not something the model could produce even if prompted to. Filtering after retrieval is not equivalent and we do not rely on it.
No. The models, retrieval, evaluation and audit logging all run inside your own cloud tenant. That matters for confidentiality, and it also removes the main driver of shadow AI — researchers reach for a consumer tool when the sanctioned one is slower or unavailable.
Who asked, what was retrieved, which programme the question was scoped to, what was generated and when. It is built to answer an inspector's questions years later rather than to satisfy an internal dashboard.