Stage 01
Research
Target identification, omics harmonisation and preclinical intelligence that shorten the path to a defensible candidate.
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Production agents
Across commercial, clinical and research
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Life sciences engagements
Delivered since inception
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Client retention
Multi-year programme continuation
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Regulated data under management
Governed, lineage-tracked, audit-ready
Governed data foundations
Semantic layer
Agent orchestration
GxP validation
Real-world evidence
Omics harmonisation
Batch analytics
Payer intelligence
Lineage & audit trails
MLOps in regulated estates
Governed data foundations
Semantic layer
Agent orchestration
GxP validation
Real-world evidence
Omics harmonisation
Batch analytics
Payer intelligence
Lineage & audit trails
MLOps in regulated estates
Sales Operations Agent
Marketing Intelligence Agent
Market Access Agent
Medical Affairs Agent
Study Planning Agent
Study Conduct Agent
Site Management Agent
Regulatory Intelligence Agent
Target Identification Agent
Preclinical Intelligence Agent
Strategic Planning Agent
Sales Operations Agent
Marketing Intelligence Agent
Market Access Agent
Medical Affairs Agent
Study Planning Agent
Study Conduct Agent
Site Management Agent
Regulatory Intelligence Agent
Target Identification Agent
Preclinical Intelligence Agent
Strategic Planning Agent
The value chain
One reasoning layer, from target to territory
The same governed foundation serves discovery scientists, study teams, plant operations and commercial leadership. That is what makes the intelligence consistent.
Explore research
Stage 02
Clinical
Feasibility, conduct and site intelligence that surface risk while there is still time to act on it.
Stage 03
Manufacturing
Batch, yield and supply analytics that protect release timelines and continuity of supply.
Stage 04
Commercial
Field, payer and brand intelligence grounded in compliant, lineage-tracked commercial data.
Context AI
Models are commodity. Context is not.
We invest in the semantic and governance layer first, because that is the part competitors cannot copy and auditors will always ask about.
01
Context before models
We invest in the semantic and governance layer first. Models are interchangeable; the grounded context that makes them trustworthy is not.
02
Regulated by construction
Validation artefacts, lineage and audit trails are produced by the delivery pipeline itself, not reconstructed before an inspection.
03
Agents with accountability
Every agent explains its reasoning, cites its sources and escalates to a named human for consequential decisions.
04
Life sciences fluency
Our teams speak protocol, formulary and batch record. That fluency is why programmes reach production instead of stalling in pilot.
AI agents
Eleven agents, built for accountability
Each agent explains its reasoning, cites its sources and escalates to a named human when the decision warrants it.
Services
Life sciences fluency, engineering discipline
Domain teams that speak protocol, formulary and batch record, paired with engineers who ship production systems in regulated environments.
Life Sciences Expertise
Discovery Research
We build the data foundation research organisations need to reason across genomics, proteomics, imaging and literature — then layer agentic analysis on top so scientists spend their time on hypotheses, not plumbing.
- Multi-omics harmonisation pipelines
- Target and biomarker evidence graphs
- Reproducible analysis environments
- Scientist-facing exploration interfaces
Life Sciences Expertise
Clinical Development
From protocol feasibility through to submission, we operationalise clinical data so that quality signal surfaces early and every derived dataset carries lineage a regulator can follow.
- CDISC-aligned clinical data products
- Risk-based monitoring analytics
- Site and enrolment forecasting
- Submission traceability tooling
Life Sciences Expertise
Manufacturing & Supply
We connect MES, LIMS and supply planning data into a single operational view, applying anomaly detection and scenario modelling to protect yield, release timelines and continuity of supply.
- Batch genealogy and deviation analytics
- Yield and OEE modelling
- Cold-chain and logistics monitoring
- Supply scenario simulation
Solutions
Accelerators, not slideware
Packaged capabilities that shorten the distance between a governed data foundation and a decision someone actually makes.
Case studies
Programmes that reached production
Anonymised but specific: what the constraint was, what we built and what changed.
Insights
What our teams are working through
Experts & research
Podcast episodes and whitepapers
Longer-form thinking from the people doing the delivery work.
Episode 18
Grounding agents in regulated data
Why the retrieval corpus deserves the same governance as a validated document repository, and what changes when it gets it.
Dr. Priya Raghavan — Chief Scientific Officer, Aurenex42 min