Biotech & Life Sciences
AI biotech applications that ship to production
PrismBase builds production ML and Gen AI systems for biotech teams, document intelligence, R&D pipelines, and predictive models under NDA. One senior principal, one contract, governed deliverables your team owns.
Biotech AI use cases
We build applications biotech teams search for, not generic "AI transformation" decks.
Scientific document intelligence
Extract structured entities from lab reports, ELN exports, regulatory filings, and vendor COAs, with human-in-the-loop QA and full audit trails for IP-sensitive data.
R&D data pipelines
Production ETL and feature pipelines connecting assay results, instrument outputs, and metadata, versioned, reproducible, and ready for modeling or downstream LIMS integration.
Predictive & classification models
Assay outcome prediction, QC anomaly detection, and experiment prioritization, classical ML where it outperforms Gen AI, with monitoring and drift detection in production.
Governed Gen AI for research teams
Internal RAG over approved document corpora with access controls, citation requirements, and evaluation harnesses, not open-ended ChatGPT deployments on proprietary sequences or formulations.
MLOps for regulated workflows
Model cards, lineage tracking, CI/CD for pipelines, and incident runbooks designed for biotech compliance expectations, even when formal GxP scope is client-owned.
Infrastructure & cost optimization
Right-size cloud spend for compute-heavy workloads, batch training, genomics-adjacent pipelines, and high-volume document processing.
Anonymized case study · NDA
Production AI application for R&D workflows (NDA)
Shipped a governed document-intelligence and data pipeline under NDA: structured extraction, human-in-the-loop QA, audit trails, and handoff runbooks. Client identity and proprietary science remain confidential.
Read full case study →Production
Under active NDA
Scoped engagement, production deploy within agreed window
Why biotech teams choose PrismBase
- NDA-first engagements, client identity and proprietary science stay confidential
- Production Standard: deployed code, monitoring, and runbooks
- Senior principal builds and deploys, you work with the person doing the work
- Honest scoping: we redirect when AI is not the right tool for the workflow
- IP transfer included, your team owns models, pipelines, and documentation
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Frequently asked questions
Do you work under NDA with biotech clients?
Yes. We routinely execute NDAs before discovery. Case studies and marketing references are anonymized unless the client approves public attribution.
What biotech AI applications do you build?
Document extraction from scientific and regulatory text, R&D data pipelines, predictive models for assay and operational outcomes, and governed internal Gen AI (RAG) over approved corpora. We focus on production systems your team can operate.
Are you GxP validated?
PrismBase is not a GxP validation vendor. We design architecture and documentation to support your compliance requirements; formal validation remains client responsibility with your QA/RA partners.
How do engagements start?
Discovery call to assess fit, then a scoped SOW with KPI targets and Production Deliverable Checklist, or a DB Optimizer audit if Postgres performance is the immediate bottleneck.
Building AI for biotech?
Tell us about your R&D workflow, data sensitivity, and production timeline. We'll be honest about fit, and sign an NDA before any technical deep dive.
Start a confidential conversation