SERVICES
The Decisions That Shape Your Regulatory Future
One Vector brings AI, IT advisory, and GxP compliance together as a single integrated practice for life sciences organizations that need all three working in the same direction.
A UNIFIED APPROACH
Three Disciplines and One Integrated Strategy
OUR SERVICES
AI / ML & Data
AI That Gets to Production and Stays There
Life sciences AI initiatives stall because data readiness, validation strategy, and governance weren't addressed early enough. We help organizations move from experimentation to validated, production-ready systems with traceability, human oversight, and audit-readiness built in from the start.
Focus areas:
- AI Strategy and Roadmap
- Domain-Specific LLMs and Scientific AI
- Agentic Workflows for Regulated Environments
- FAIR Data Architecture and GxP Data Foundations
- GxP-AI Governance and Drift Monitoring
- Custom Model Development and Deployment
OUR SERVICES
IT Advisory
Technology Decisions That Hold Up Under Scrutiny
Early technology decisions in regulated environments are expensive to reverse. We provide executive-level IT advisory that brings GxP literacy and strategic depth to every major technology decision, so your infrastructure investment moves the business forward without generating the kind of findings that set programs back by months.
Focus areas:
- Fractional CIO and CDO Leadership
- GxP Cloud Architecture for AWS and Azure
- Pharmacovigilance and Safety Systems Strategy
- Digital and Data Transformation
- Cybersecurity and Resilience Strategy
- IT Program Management and Delivery
OUR SERVICES
IT Quality & Compliance
Validation That Accelerates Milestones
The organizations that move fastest through inspection apply the right level of rigor to the right systems. We apply modern Computer Software Assurance methodology to reduce unnecessary documentation overhead, keep AI and cloud deployments on track, and build a defensible audit trail that holds up when regulators arrive.
Focus areas:
- CSV to CSA Transition and Modernization
- AI and ML Application Validation
- Cloud and Software Validation
- Data Integrity and ALCOA++
- Audits, 483 Remediation and Inspection Readiness
- Flexible Staffing and Managed Services
OUR APPROACH
All Three Disciplines in the Room at the Same Time
Getting an AI system into validated production means solving an infrastructure problem, a data integrity problem, a validation strategy problem, and a change control problem simultaneously. This is how that looks in practice.
- 01
Use-Case Assessment and Data Readiness
Map your data estate, identify AI-ready use cases, and define what production-ready means in your regulatory context before model development begins.
AI Strategy · FAIR Data
- 02
GxP Infrastructure and Architecture
Design the cloud environment and data pipelines with Part 11 controls and validated infrastructure before a line of model code is written.
Cloud Architecture · PMO Governance
- 03
Model Build With Oversight by Design
Build domain-literate models or agentic workflows with human-in-the-loop controls and explainability from the first commit.
Domain Models · Agentic AI
- 04
CSA-Aligned Validation and Data Integrity
Execute risk-based validation matched to the system's regulatory classification, with drift monitoring, ALCOA++ audit trails, and change control at deployment.
AI Validation · Data Integrity
- 05
Governance, Monitoring and Continuous Improvement
Continuous drift monitoring, periodic review, change control, and infrastructure optimization as your science, regulations, and organization evolve.
AI Governance · IT Advisory · Managed Services
WHO WE WORK WITH
The Right Partner at Every Stage
Early Stage — Series A and B
Building regulated infrastructure for the first time, ahead of a clinical milestone, with a team that isn't ready to hire a full-time CIO and VP of Digital Quality Assurance.
Fractional CIO and IT leadership · GxP cloud build · First validation framework
Growth Stage — Scale-Up
Modernizing systems that have outgrown their original design, without creating compliance gaps in the programs that depend on them.
Legacy system migration · PV and data platform modernization · CSV to CSA transition
AI Adoption — Any Stage
Identifying AI use cases & moving an AI initiative from a promising pilot to validated production with a clear governance and change control framework in place.
AI validation strategy · GxP data architecture · Change control for adaptive AI
Post-Inspection — Remediation
Responding to a 483 or warning letter with a technically defensible answer that addresses root cause rather than the surface observation.
Root cause analysis · CAPA design · Inspection readiness
WHEN TO ENGAGE
- Before selecting core systems such as LIMS, QMS, PV platforms or data infrastructure
- Before moving to cloud or modernizing architecture
- Before introducing AI into regulated workflows
- Before audits, submissions or commercialization
- When scaling beyond early-stage operations
Next Steps
Let's Look at Where You Are
A 30-minute call where you describe the program and the constraint. We give you a straight answer on what's driving the problem and the most practical path forward.