Key Takeaways

• Enterprise Life Sciences Platform

• Process & Stability Data

• Quality Review Workflows

• Source-to-Report Traceability

• Secure Production Infrastructure

Challenge

Enterprise life sciences workflows require more than extracting information from documents.

Process records, stability studies, analytical methods, and related data need to be structured consistently, connected back to their original sources, reviewed by qualified users, and managed within secure operational controls.

The company had already built the core technology. The challenge was preparing that foundation for enterprise use without introducing the cost and risk of rebuilding the platform.

Strategy

Build on the existing architecture and focus engineering investment on the requirements that matter most for enterprise life sciences deployment.

SF AI Labs organized the program around four workstreams:

1. Process Data Automation — structure process and stability information into usable datasets.

2. Quality Review — introduce reviewer workflows, exception routing, and source validation.

3. Enterprise Readiness — strengthen deployment, reliability, access control, and infrastructure.

4. Operations & Governance — establish administration, auditability, monitoring, and support.

Each workstream was tied to measurable deployment gates and acceptance criteria.

Solution

• Structured extraction of process and stability data

• Confidence scoring and source-level traceability

• Master data views, filters, and export workflows

• Human-in-the-loop QC review queues

• Threshold-based exception routing

• Source-to-report cross-checking

• Analytical-method review support

• Batch summaries and trend-ready datasets

• Dedicated single-tenant infrastructure

• Role-based access and administrative controls

• Audit logging and data-retention controls

• CI/CD, load testing, monitoring, and alerting

• Operational support and escalation workflows

Execution

• Assessed the existing application, AI, data, and cloud architecture

• Identified gaps between the current platform and enterprise deployment requirements

• Preserved the existing application foundation through a reuse-first architecture

• Designed structured process and stability data workflows

• Established reviewer and exception-management workflows for quality control

• Defined source traceability and regression-testing requirements

• Designed the dedicated production environment and deployment controls

• Strengthened role-based permissions, auditability, and administrative capabilities

• Defined monitoring, support, and operational governance for ongoing enterprise use

Results

• Established an enterprise architecture for life sciences data and review workflows

• Created a phased engineering roadmap from data processing through production operations

• Introduced a structured human-review layer around automated outputs

• Defined source-to-report traceability across critical data workflows

• Preserved the existing platform while addressing enterprise infrastructure and governance gaps

• Connected data automation, QC, infrastructure, security, and operations into one deployment framework

The program established deployment targets including 90%+ critical-parameter extraction accuracy, 95%+ source-to-report traceability, and performance testing for 100 concurrent users.

Business Value

The engagement provided a practical path for converting an existing life sciences technology platform into an enterprise-ready system.

Rather than rebuilding the product, SF AI Labs focused investment on the layers required for commercial deployment: reliable data processing, reviewer controls, traceability, secure infrastructure, and operational governance.

This allowed the company to preserve its existing technology while building the capabilities required to support larger pharmaceutical customers and more demanding production workflows.

Why SF AI Labs

SF AI Labs combines AI engineering, enterprise architecture, and production software development.

For this engagement, that meant treating the product as an enterprise life sciences platform—not simply an AI model—and addressing the complete operating environment around it: data, quality review, infrastructure, security, governance, and support.