Fit-for-Purpose AI Validation for Life-Sciences
We design and execute risk-based validation so your AI systems are auditable, traceable, and inspection-ready under GxP/CSV/CSA—without slowing your teams down.

Who we help
QA/Validation • Digital/IT • R&D Informatics • Clinical & Medical Affairs • Pharmacovigilance

What you get

  • A Validation Plan tied to your SOPs/URS and risk profile

  • A test harness with traceable prompts, expected outputs, and pass/fail rules

  • An audit-ready evidence pack (requirements ↔ tests trace matrix, deviations, change control, sampling plan)

  • Clear guidance on ongoing monitoring and periodic review

Book a 20-min scoping call

How I work (at a glance)

  1. Assess — clarify intended use, data boundaries, risks, success criteria

  2. Specify — write/align URS and validation plan (risk-based, CSA-aligned)

  3. Validate — implement test harness + acceptance criteria; execute and log evidence

  4. Operationalize — hand over monitoring & periodic review playbooks; train stakeholders

Fixed-Fee Packages

1) 2-Week Pilot — LLM Risk & Evidence QuickScan

Best for: early discovery or pre-pilot systems (chatbots, triage, summarization, search, data extraction).
Timeline: 10 business days.
Deliverables:

  • Risk register (FMEA-style) + validation strategy (CSV/CSA)

  • Draft URS and acceptance criteria

  • Prototype test harness (prompt library, expected outputs, pass/fail rubric)

  • Sample traceability matrix and evidence storyboard (what to capture, where)

  • Read-out with top risks, “must-fix” items, and a go/no-go recommendation
    Investment: starting at $12k (single system, ≤2 personas, ≤3 critical risks)

Outcome: you’ll know exactly what needs to be validated, what to test, and what “good” looks like to an auditor—before you commit real budget.

2) 30-60-90 Build — Full Validation from URS to Audit Pack

Best for: production-bound systems in regulated contexts (GxP records support, safety/PV triage, R&D knowledge assistants).
Timeline: ~12 weeks (30/60/90 cadence).
Deliverables:

  • Final URS mapped to intended use & risk controls

  • Validation Plan (risk-based; evidence to collect; roles/RACI)

  • Test harness (prompt sets, expectation rules, pass/fail thresholds, sampling strategy)

  • Executed test protocols with logs, deviations/CAPA, and trace matrix

  • Operational controls: monitoring plan, drift checks, change-control templates

  • Audit-ready Evidence Pack (PDF/Confluence/SharePoint structure)
    Investment: typically $45–$85k (scope-dependent; multi-workflow discounts)

Outcome: a validated, documented AI workflow that stands up to CSV/CSA and inspection—plus the playbooks to keep it that way.

3) Periodic Review — Post-Deployment Monitoring & Change Control

Best for: live systems that must stay fit-for-purpose over time.
Cadence options: quarterly or semi-annual.
Deliverables (each cycle):

  • Monitoring report (quality metrics, drift checks, exceptions)

  • Sampling plan refresh and targeted re-tests

  • Change-control review (model/version/data/tooling) with documented rationale

  • Updated risk register and retraining recommendations (if needed)
    Investment: $5–$12k per cycle (volume & complexity dependent)

Outcome: objective evidence your model is still fit-for-purpose—and a smooth path through audits.

Add-On Modules (bolt on to any package)

  • CSA for Analytics Pipelines (NGS/PV/HEOR): URS → risk assessment → test scripts → traceability for data/compute lineage

  • Human-in-the-Loop Design: decision thresholds, escalation rules, reviewer checklists

  • HEOR/Value Dossier Support: time-to-task, error-rate, and utilization metrics to quantify benefit

  • Vendor & Model Assessment: rubric-based evaluations (hosted LLMs, embeddings, retrieval stacks)

  • SOP Refresh: CSV/CSA alignment, monitoring-light SOPs, log-retention and access controls

  • Training & Enablement: 2-hour workshops for QA/IT/R&D; “how to read the evidence” for stakeholders

Compliance & Data Handling

  • No PHI accepted unless explicitly scoped; work is performed in client-owned tenants where feasible

  • Access minimization and least-privilege by default; all artifacts version-controlled

  • Log retention and traceability per SOW; we provide templates for secure storage (SharePoint/Confluence/GxP DMS)

  • Aligns with GxP, CSV/CSA, 21 CFR Part 11, Annex 11 principles and your QMS

What we need from you (to go fast)

  • Intended use statement and success criteria

  • Named process owner and QA/CSV contact

  • Access to a representative dataset or safe synthetic equivalent

  • Your current SOPs/URS (if they exist) and system architecture diagram

Example SOW Milestones (30-60-90 Build)

  • Day 0–30: URS finalized; risk register and Validation Plan approved

  • Day 31–60: Test harness built; pilot execution; deviations triaged

  • Day 61–90: Full execution; evidence pack compiled; training & handover

FAQs

Q: Can you work with my preferred LLM/vendor?
Yes—open-source or hosted. We validate the system you run, not a specific model brand.

Q: Will you sign our MSA and follow our QMS?
Absolutely. Work is delivered under MSA/SOW, aligned to your QMS and change-control.

Q: Can you help before Q1 2026?
Yes—pre-bookings are open now. Discovery and pilots can start sooner; invoicing can match your fiscal needs.

Ready to get started?

  • Not sure where to begin? Start with the 2-Week Pilot to de-risk the project.

  • Have a production deadline? Book the 30-60-90 Build and lock your slot.

Book a 20-min scoping call • Contact • kayla@kaylaabritt.com