Life Sciences
AI built for the pace and precision of drug development.
Regulatory constraints, trial data, and scientific literature — synthesized into infrastructure that supports the full development lifecycle.
Life sciences organizations produce some of the most structured and well-documented data in any industry, yet most AI tools are not built for the specific demands of regulatory submissions, clinical trial analysis, and scientific synthesis. RonanLabs builds the knowledge and model infrastructure that connects that data to the workflows that need it.
The data problem in life sciences (pharma, biotech, cros)
Clinical trial data sits in isolated silos: CTMS systems, EDC platforms, safety databases, and regulatory dossiers that don't talk to each other. Synthesizing across these sources requires manual effort that grows faster than team capacity as portfolios expand.
Regulatory submissions demand a level of traceability that general AI cannot provide. Every claim must map to a cited source; every conclusion must be auditable. Models that generate confident-sounding summaries without source attribution are a regulatory liability, not an asset.
The published literature relevant to a drug development program spans hundreds of journals, trial registries, and agency guidance documents. Keeping a complete, current picture of that corpus — let alone querying across it — is a significant ongoing cost without purpose-built infrastructure.
CROs face the additional challenge of data scoped to individual sponsors. Infrastructure built for one sponsor's program cannot simply be redirected to another without careful separation of customer-specific knowledge from shared domain science.
What we deliver
Synthetic data
Synthetic clinical trial datasets with realistic patient demographics, biomarker distributions, and adverse event profiles — for protocol simulation, EDC testing, and model training without exposing live trial data.
Custom models
Models fine-tuned on your regulatory submissions, internal literature reviews, and trial data to support scientific writing, signal detection, and structured data extraction specific to your therapeutic area.
Knowledge & retrieval
A continuously updated knowledge graph spanning published literature, regulatory guidance, and internal program documents — queryable with citation-level traceability for every retrieved claim.
See the full architecture for how these layers fit together.
Common deployments
Regulatory submission drafting
Models trained on the sponsor's prior submission style and regulatory guidance that assist with section drafting, cross-reference checking, and consistency review.
Literature surveillance
Automated ingestion and structured extraction from new publications relevant to the therapeutic area, surfaced as a queryable index with citation links.
Clinical trial data synthesis
Structured extraction and summarization across trial datasets, with every conclusion traceable to the source record or analysis.
Adverse event signal detection
Pattern models trained on the sponsor's safety database to surface early signals in incoming adverse event reports against baseline rates.
Frequently asked
Discuss your life sciences (pharma, biotech, cros) deployment
Tell us about your data, your constraints, and your workflows. We'll design the layers around them.
Start the conversation