AiVACO Advisory combines practising regulatory and quality expertise with hands on AI engineering. We help regulated organisations find out where they stand, decide what to do, and build the evidence to prove it.
Our regulatory rule sets and advisory methods are authored by practising pharmaceutical quality specialists who prepare organisations for inspection today.
Our AI Lab designs, builds and hardens production AI systems. Advice is grounded in what is achievable, testable and maintainable.
We are model agnostic by design. Our advice serves your intended use, not a vendor's roadmap, and stands on its own with or without AAP.
Most organisations come to us with one of two questions. Is the AI we already use defensible? Or, should this AI idea go ahead? Our AI Audit answers the first. Our AI Feasibility Study answers the second.
In most regulated organisations, AI is in operation before anyone has formally assessed it. Our AI Audit examines that estate against current regulatory expectations and good practice and tells you plainly where you stand, in the format your Quality function already understands.
Quality and compliance leaders preparing for inspection, boards and governors seeking independent assurance, and organisations that adopted AI tools quickly and now need to catch up.
Request an AI AuditBefore a pilot is approved, leaders need three answers: can it be done safely, what will it take, and is the value worth it? Our Feasibility Study assesses a specific AI use case through five lenses and gives you a documented recommendation you can take to your investment or change board.
Innovation and transformation teams with a pipeline of AI ideas, Quality leaders asked to approve a proposal, and executives who want a go or no go decision grounded in evidence.
Request a Feasibility Study| If you need to... | Choose |
|---|---|
| Get independent assurance on AI already in operation | AI Audit |
| Decide whether a specific AI idea should go ahead | AI Feasibility Study |
| Understand where AI is used across the organisation and what to do first | Readiness Assessment |
| Prove one use case end to end with inspectable evidence | 90 day Assurance Pilot |
| Equip people to use and govern AI | Training |
Find out where AI is already in use and where the gaps are.
What you get: a classified AI inventory with consequence tiers and owners, and a gap analysis against an eight domain assurance checklist.
Turn ambition into a sequenced, defensible programme.
What you get: a prioritised roadmap built on our six phase framework, with the first pilot selected and scoped.
Put accountability where every framework says it must sit.
What you get: intended use statements, RACI per use case, AI and acceptable use policies, and controls for supplier and embedded AI.
Evidence proportionate to the consequence of being wrong.
What you get: acceptance criteria, independent test data strategy, documented test execution and a risk assessment aligned with ICH Q9(R1) and GAMP 5.
Design the environment before you choose the model.
What you get: data residency and deployment design, model selection and provenance guidance, security controls and procurement support.
Equip people to use, review and govern AI with confidence.
What you get: role based training for Quality, IT, process owners and leadership on intended use, model limitations, testing expectations and review responsibilities.
A mock inspection of your AI use, an evidence pack review and a remediation plan. Optionally paired with an avatar led rehearsal through AiVACO Avatars.
EnquireAn AI strategy for a regulated organisation is an operating capability, not a technology purchase. These six phases turn regulatory expectations into a sequence that works for a first pilot and scales across an enterprise.
"Start with the process, not the platform."
Most organisations should start in tiers 1 and 2. Tier 3 is where an assurance layer earns its place, because it lets a non deterministic model contribute while a deterministic control verifies, tiers and records the output. Tier 4 remains the domain of static, deterministic, fully validated models and explicit human decision.
Drafting, searching, summarising, document comparison. Governance, defined intended use, trained human review.
Trend detection, investigation support, anomaly flagging. Stronger evidence, acceptance criteria, expert review, monitoring.
Outputs that inform safety, quality or data integrity decisions. Formal risk based controls and deterministic verification before use.
Batch disposition, release and specification decisions. Static, deterministic models only, full assurance, explicit human accountability.
| Format | Purpose | Commitment |
|---|---|---|
| Executive briefing | Frame the regulatory picture and your exposure | 60 minutes |
| AI Audit | Independent assurance on AI already in use | Fixed scope |
| AI Feasibility Study | Go, redesign or stop decision on a specific use case | Fixed scope, per use case |
| Readiness assessment | Inventory, classify and identify gaps | Fixed scope |
| Assurance pilot | Prove one use case end to end, in shadow mode | 90 days |
| Retained advisory | Ongoing governance, validation and inspection support | Agreed annually |
