AI Readiness for Life Sciences

Strategic assessments and technical guidance to build compliant, production-ready AI capabilities in your organization.

AI adoption in life sciences requires more than good intentions—it demands robust data infrastructure, regulatory compliance frameworks, and organizational capabilities that most companies lack. We provide focused assessments and ongoing technical guidance to bridge the gap between AI ambition and operational reality, with patient safety and data integrity as non-negotiable priorities.

Core Services

Data Infrastructure Evaluation

Current state analysis of data architecture, quality, accessibility, and integration capabilities. We assess readiness for ML/AI workflows, identify critical gaps, and provide actionable recommendations for infrastructure modernization—from data lakes and ETL pipelines to compute resources and API strategies.

Regulatory Compliance Planning

Navigate FDA AI/ML guidance, 21 CFR Part 11 requirements, EU AI Act implications, and GxP validation protocols. We develop compliance frameworks specific to your AI use cases, including validation strategies, documentation requirements, and audit trail specifications for regulated environments.

Use Case Prioritization

Systematic evaluation of AI opportunities across your R&D and operations. We assess feasibility, data requirements, regulatory risk, and business impact to build a prioritized roadmap—identifying quick wins and transformational initiatives with realistic timelines and resource estimates.

Technical Capability Assessment

Evaluate current analytics infrastructure, data science capabilities, MLOps maturity, and technology stack. We identify skill gaps, recommend training pathways, and provide guidance on team structure, build-vs-buy decisions, and vendor selection for AI/ML platforms and tools.

Engagement Approach

Phase 1: Focused Assessment (2-4 weeks)

Rapid evaluation across data infrastructure, regulatory posture, technical capabilities, and prioritized use cases. Includes stakeholder interviews, system reviews, and documentation analysis.

Deliverables:

AI Readiness Assessment Report (current state, gaps, risks)
Prioritized Use Case Portfolio with feasibility analysis
Infrastructure Recommendations with architecture diagrams
Compliance Gap Analysis for target AI applications
Quick-win opportunities and pilot project specifications

Phase 2: Strategic Roadmap (2-3 weeks)

Development of implementation strategy based on assessment findings.

Deliverables:

Phased Implementation Roadmap (12-24 months)
Technical Architecture Design for priority initiatives
Validation Protocol Templates and compliance frameworks
Vendor Evaluation Criteria and RFP templates (if applicable)
Capability Building Plan (hiring, training, partnerships)
Budget and Resource Requirements

Phase 3: Ongoing Advisory (Flexible)

Continuous technical and strategic guidance as you execute AI initiatives. Engagement structured as monthly retainer, project-based support, or ad-hoc consultation based on your needs.

Support includes:

Implementation oversight and technical review
Validation protocol development and review
Regulatory strategy consultation
Vendor evaluation and management
Architecture and code review
Team mentoring and capability building

Technical Focus Areas

Infrastructure & Data

Data lake and warehouse architecture
ETL/ELT pipeline design for AI/ML
Feature stores and model registries
MLOps and model lifecycle management
Cloud vs. on-premise strategies
API design for AI integration

Regulatory & Compliance

FDA 21 CFR Part 11 validation for AI systems
AI/ML model validation protocols
Computer System Validation (CSV) for AI platforms
Risk assessment frameworks (FMEA, FTA)
Change control and version management
Audit trail and documentation requirements

Life Sciences Applications

Drug discovery AI readiness (molecular dynamics, screening)
Clinical trial optimization (patient selection, site selection)
Pharmacovigilance automation (adverse event detection, signal detection)
Proteomics and omics data analysis infrastructure
Real-world evidence (RWE) data integration
Clinical decision support systems

Sample Deliverables

From a recent mid-size biotech assessment:

Data Infrastructure

Current state: Siloed databases, manual ETL processes, limited API access
Recommendations: Unified data lake architecture, automated pipelines, RESTful API layer
Tools evaluated: Snowflake, Databricks, AWS/Azure data services

Regulatory Framework

Target use case: AI-powered adverse event signal detection
Compliance requirements: 21 CFR Part 11, GxP validation, model explainability
Validation approach: Retrospective validation with prospective monitoring
Documentation templates: 15 protocols and SOPs developed

Use Case Roadmap

Quick wins: Automated safety report triage, proteomics data QC
6-12 months: Predictive adverse event modeling, clinical trial site optimization
12-24 months: AI-assisted drug target identification, real-world evidence synthesis
Resource requirements: 2 data engineers, 1 ML engineer, 1 regulatory specialist

Why XData Lab

We combine hands-on technical experience building production systems in life sciences with deep regulatory and compliance expertise. Our team has developed pharmacovigilance automation tools, proteomics analysis platforms, and clinical data infrastructure—we understand both the AI technology and the operational realities of regulated environments.

Our focus is practical guidance that accelerates your AI initiatives while maintaining the compliance and safety standards that life sciences demands.

Getting Started

Initial consultation to discuss your AI objectives and current challenges. From there, we scope a focused assessment tailored to your priorities—typically 2-4 weeks to actionable recommendations.