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Sectors

Where life-science data meets real constraints

Three domains, one standard of rigour — from the discovery bench to the manufacturing floor to the breeding field. See shipped examples on Work.

01

Drug discovery & R&D

From target identification to the bench — we help discovery teams move faster with AI that reads the literature, generates protocols, and turns multi-omics data into decisions scientists trust.

In practice

All examples →

Use cases

  • AI lab protocol generation

    Agents search scientific literature and existing protocols to draft precise, reproducible lab procedures — with human-in-the-loop refinement and API integration into your stack.

    Genlabai on Work →

  • Multi-omics target triage

    Integrate genomics, proteomics, and screening readouts to prioritise targets and compounds — reducing time spent on dead ends.

  • Discovery literature intelligence

    RAG over patents, publications, and internal notebooks — cited answers for target validation, competitive landscaping, and experiment design.

02

Manufacturing, quality & operations

GxP-aware AI for the shop floor and the quality suite — accelerating batch reviews, deviation management, and regulatory research without compromising data integrity or compliance.

In practice

All examples →

Use cases

  • FDA regulatory research assistant

    Grounded answers on 21 CFR, FDA guidance, device clearances, drug approvals, and clinical trials — with citations from a knowledge graph of 1M+ regulatory data points.

    CFR21 on Work →

  • Batch record review acceleration

    Extract and cross-check critical quality attributes from batch records and manufacturing data — cutting review time while preserving auditability.

  • Deviation & CAPA assistance

    Surface similar historical deviations, accelerate root-cause analysis, and draft consistent CAPA documentation within your quality framework.

03

Ag-biotech & crop science

AI for breeding programmes and agricultural R&D — genomics, phenotyping, and field data at scale, turning multi-environment trials into faster, better selection decisions.

Use cases

  • Genomic selection & breeding analytics

    Rank breeding candidates from genotype and trait data — shortening selection cycles without sacrificing genetic diversity.

  • Phenotyping at scale

    Computer vision and sensor fusion on greenhouse, drone, and field imagery — automating trait scoring across thousands of plots.

  • Multi-environment trial modelling

    Model genotype × environment interactions across sites and seasons — predicting performance before committing to costly field programmes.

Not sure where you fit?

Tell us about your data and your goals — we’ll tell you honestly whether and how AI can help.

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