Solutions / Life Sciences Intelligence
Scientific answers your Medical Affairs team can verify.
An enterprise AI product in development for pharmaceutical Medical Affairs: answers grounded in your approved scientific content, with every statement traced to its source and every answer reviewed before it is used.
IN DEVELOPMENT · INVITING MEDICAL AFFAIRS DESIGN PARTNERS
Where the product is today.
We would rather you know exactly what exists than be surprised later. This page is updated as the first release takes shape.
- Design-partner discovery sessions
- Workflow and content review with your team
- Ingest and index approved scientific content
- Answer questions with citations to the exact supporting passage
- Limit retrieval to approved sources, by role
- Route answers to human review before they are used
- Connect to repositories such as Veeva Vault and SharePoint
- Claim-to-evidence lineage
- Evidence monitoring
- Regulatory intelligence
- Clinical intelligence
- Agentic workflows
Finding the evidence is slow. Trusting the answer is slower.
BUILT FOR
- Medical Information
- Medical Excellence & Operations
- Medical Affairs leadership
- IT and AI teams supporting Medical Affairs
Medical Affairs teams answer high-stakes scientific questions from evidence spread across literature, approved content libraries, standard response documents, and internal repositories. Finding the right source takes time. Confirming it is current and approved takes longer.
General-purpose AI assistants give fluent answers without the traceability a medical reviewer needs. An answer that can't be traced to an approved source can't be used, so the work of checking it falls back on the team.
Less time searching, more time evaluating. Every answer shows where it came from, so reviewers check sources instead of reconstructing them.
Answering a scientific inquiry, from question to approved response.
The first release is scoped around one workflow Medical Information teams run every day. This is how it is designed to work.
EVERY STATEMENT LINKED TO ITS EVIDENCE AND ITS APPROVAL
A question arrives
A Medical Information specialist enters an unsolicited scientific question, in their own words.
Retrieval stays inside approved content
The system searches only the sources your organization has designated, and only those the user's role permits.
A cited draft, not a final answer
It drafts a response in which every statement links to the passage that supports it. Where the approved content doesn't answer the question, it says so rather than filling the gap.
A reviewer checks the sources
A qualified reviewer opens each citation, edits the draft, and approves or rejects it. Nothing is used until a person signs off.
The decision is recorded
The question, sources retrieved, draft, edits, reviewer, and decision are logged, so the response can be explained later.
Where the same foundation could go next.
Proposed, not built. We'll prioritize these with design partners.
Scientific content review support
Draft materials checked against approved evidence before formal review, with statements that lack a supporting source flagged.
MSL scientific preparation
The approved evidence on a topic, assembled before a scientific exchange, with sources an MSL can open and verify.
Evidence synthesis for medical review
What approved content and selected literature say on a question, and where the sources differ, for a reviewer to assess.
Governance designed in, not added later.
These are design commitments for the first release. They are not certifications or claims of regulatory compliance.
Approved-content boundary
Retrieval is limited to sources your organization designates, so answers draw only from content you have chosen to trust.
Citations on every statement
Each statement links to the passage that supports it. Unsupported statements are flagged, not smoothed over.
Role-based access
Access follows the roles and permissions your organization already defines.
Human review
Answers are drafts until a qualified reviewer approves them.
Audit trail
Question, sources, draft, edits, reviewer, and decision are recorded for every answer.
Your environment
Designed to run in your cloud environment, under your identity provider, using AI models your organization has approved.
Life Sciences Intelligence is intended to support qualified professionals. It does not replace medical review, and it is not intended for diagnosis, treatment decisions, or direct patient care.
An intelligence layer across your systems of record, not a replacement for them.
Illustrative architecture for the first release. Connectors beyond document ingestion are planned, not built.
From cited answers to connected scientific knowledge.
Our long-term direction. None of this is available today, and design partners will shape what comes first.
Claim-to-evidence lineage
VISIONA knowledge model linking studies, evidence, claims, labels, and approved content, so teams can see what each claim rests on.
Evidence monitoring
VISIONAlerts when a source that approved content depends on is updated or superseded.
Regulatory intelligence
VISIONLabel history, health-authority interactions, and regulatory commitments, connected to the evidence behind them.
Clinical intelligence
VISIONStudy, endpoint, and publication relationships across a program.
Agentic workflows
VISIONMulti-step research and drafting tasks that run under human approval at each consequential step.
Concept: how the knowledge model would relate the objects Medical, Regulatory, and Clinical teams work with.
How a design partnership would work.
A first conversation
Thirty minutes on how your team handles scientific questions today, where the time goes, and what your governance requires.
Workflow and content review
A few working sessions to map one workflow, the content it depends on, and the review steps it has to respect.
A scoped pilot
A test on a representative set of your approved content, in an environment your IT and security teams agree to, against success criteria we define together.
Pilot terms are agreed case by case. There is no formal program, and no commitment at the first conversation.
Discuss a Design PartnershipStraight answers.
- Is Life Sciences Intelligence available today?
- No. It is in active development. We are talking with Medical Affairs teams to shape and validate the first release before it ships.
- Does it replace Veeva or our content repositories?
- No. Systems of record stay systems of record. Life Sciences Intelligence is designed to read from them, not to replace them.
- Where would our data be processed?
- The first release is designed to run in your own cloud environment, within the data boundary your organization defines. Deployment details are agreed during pilot scoping.
- Which AI models does it use?
- It is designed to work with models your organization has already reviewed and approved, rather than a fixed model you have no say in.
- Has it been validated for regulated use?
- Not yet. It has not been validated for any regulated use. Validation requirements would be part of scoping a pilot with your quality and IT teams.
- What does a design partner commit to?
- Time from a Medical Affairs lead, a representative content set when we reach a pilot, and candid feedback. A first conversation commits you to nothing.
Life Sciences Intelligence is Cirrusage’s first industry solution, designed to build on the reusable capabilities of Cirrusage Intelligence Platform and the same engineering practice as our engineering services.
Help shape the first release.
We are looking for a small number of Medical Affairs teams to build this with.