Production AI application for R&D workflows (NDA)

Challenge

A biotech team needed production AI to structure scientific and operational data from lab outputs and regulatory documents, not another notebook prototype. Prior efforts stalled on governance, IP sensitivity, and integration with existing R&D tools.

KPI targets

• Structured extraction accuracy on validated golden sets
• End-to-end pipeline latency within R&D workflow SLAs
• Full audit trail for IP-sensitive document processing
• Client team able to operate pipeline without PrismBase post-handoff

Approach

1. 1.NDA and data-boundary review before any credential access
2. 2.Metrics before models: KPIs tied to R&D cycle time and data quality, not model vanity scores
3. 3.Hybrid pipeline: classical extraction + governed Gen AI where citation and QA required
4. 4.Governance from day one: access controls, model cards, retention policy alignment
5. 5.Production Standard checklist signed at acceptance

Production artifacts

• Deployed extraction and enrichment pipeline with CI/CD
• Human-in-the-loop review queue for low-confidence outputs
• Monitoring, logging, and incident runbooks
• Model cards and data-handling documentation for client QA/RA review
• Knowledge transfer sessions and IP transfer to client repositories

Outcomes

• Production system replacing stalled notebook prototypes
• Engagement details anonymized per client NDA
• Foundation for expanded AI applications across additional R&D workflows

Stack