Why a structured evaluation matters
An AI platform for pharmacovigilance will hold your safety data, shape your processes and be examined by inspectors for years. Demonstrations are designed to impress; they rarely show how a system behaves on your documents, under your SOPs, with your volumes. A structured evaluation protects you from choosing on presentation quality.
The nine areas to test
1. Scope: what is live today?
- Which modules are in production with clients now, and which are on the roadmap?
- Does automation cover the whole case lifecycle, or individual steps?
- Which product types are supported: medicines, vaccines, devices, consumer products?
- Which markets’ requirements are supported today?
2. Validation and change control
- What is the validation approach, and what documentation will you provide?
- How are AI model changes tested and released? How often?
- Who is responsible for which parts of validation?
- Which frameworks is the platform built to support, such as 21 CFR Part 11, EU Annex 11 and GAMP 5? Ask to see how, not just a list.
3. Explainability and traceability
- Does every extracted value link to its source?
- Can a reviewer see why a term was coded or a case was judged serious?
- Are AI actions recorded in the audit trail with model versions?
4. Accuracy and drift
- How is accuracy measured, on what data, and by whom?
- Will you measure it on a sample of our data before we commit?
- How is performance monitored after go-live, and what happens when it drifts?
5. Human oversight
- Can review gates be set from our SOPs?
- What happens to low-confidence results?
- Which decisions does the AI never make on its own?
6. Fit to our processes
- Can case rules, coding conventions, thresholds and templates be configured from our SOPs?
- How are configuration changes versioned and approved?
- How are local requirements handled for each market?
7. Integration and deployment
- Which safety databases do you connect to today?
- Do we have to replace our current system, or can you work alongside it?
- Which deployment options and hosting regions are available?
8. Security and data use
- Which security certifications do you hold? Can we see the reports?
- Is our data used to train models used by other clients?
- How are data residency, encryption and access control handled?
9. Proof
- Will you agree baselines with us before go-live?
- Can you show results from a referenceable client?
- Can we run a time-boxed pilot on our own data first?
Red flag: headline percentages with no baseline, method or client behind them. Ask exactly how each number was measured, on what data, over what period.
A scorecard you can use
Score each vendor from 1 to 5 in each area, multiply by the weight and add up. Adjust the weights to your priorities, and agree them before you see any demonstrations.
| Area | Example weight | What a score of 5 looks like |
|---|---|---|
| Validation and change control | 20% | Documented approach, evidence package, controlled releases |
| Explainability and traceability | 15% | Every value source-linked; AI actions in the audit trail |
| Accuracy and drift | 15% | Measured on your data; continuous monitoring with alerts |
| Fit to your processes | 15% | Rules, gates and templates configured from your SOPs |
| Scope live today | 10% | All needed modules in production with clients |
| Integration and deployment | 10% | Works with your systems; your hosting choice |
| Security and data use | 10% | Certifications with reports; clear written data-use terms |
| Proof | 5% | Pilot on your data against an agreed baseline |
Example weights for illustration. Human oversight is treated as a pass/fail requirement rather than a weighted score: a vendor that cannot keep your experts in control should not proceed.
How to run a pilot that proves something
A pilot is the single most useful step in an evaluation, but only if it is designed to produce evidence.
- Pick one process. Literature monitoring or a defined slice of case intake works well: contained, measurable and representative.
- Agree the baseline first. Measure current cycle time, effort, quality findings and timeliness before the pilot starts.
- Use your real data. Your documents, your products, your languages, under your SOPs.
- Run in parallel. Keep your current process running, and compare results case by case.
- Define success in advance. Write down what result would justify moving forward, and what would not.
- Involve QA from day one. The pilot should produce validation-style evidence, not just a dashboard.
Common mistakes
- Choosing on the demo. Demos use the vendor’s best documents. Insist on yours.
- Buying the roadmap. Features promised for next year do not help your inspection this year.
- Leaving QA until the end. Validation requirements discovered late add months.
- Skipping the baseline. Without one, nobody can say whether the investment paid off.
- Ignoring data terms. How your data may be used should be settled before contract, not after.
What this means for executives
- Insist on a pilot on your own data with agreed baseline measures before any multi-year commitment.
- Make validation evidence and written data-use terms contractual, not optional.
- Ask your QA lead to sign off the scorecard weights before demonstrations begin.
- Prefer what is live today over what is on a roadmap.
Bring this checklist to every vendor conversation, including ours. Our answers are throughout this site, and we are happy to go through them with your team. Book a session.