Task first
Define the decision, user, risk, and evidence before selecting a model or interface.
Research & evaluation
Evaluate the workflow, not just the model.
A model can perform well in a benchmark and still fail in your process. We evaluate the complete system—sources, prompts, tools, users, review steps, and operating context.
Research principles
Evidence-led, bounded, and useful in context.
Our research posture is designed for pharmaceutical work, where a fluent answer is not enough. The system must be useful to qualified users, grounded in the intended evidence, and explicit about limitations.
Failure aware
Test missing data, disagreement, prompt injection, stale sources, and out-of-scope requests.
Reviewable
Keep sources, assumptions, uncertainty, and human actions connected to each output.
Evidence loop
Learn from real cases without letting production drift.
Useful evaluation is continuous but controlled. Representative issues become new test cases, approved changes move through a repeatable test process, and production monitoring identifies when re-evaluation is needed.
- 01
Define representative cases
Include normal work, edge cases, high-risk scenarios, and known past failures.
- 02
Set acceptance criteria
Agree thresholds with domain experts and connect them to operational outcomes.
- 03
Test the full system
Evaluate retrieval, model output, tool use, interface behaviour, and escalation.
- 04
Review and revise
Record failures, root causes, changes, retest results, and the decision to proceed.
Evaluation framework
Quality has several dimensions.
No single score describes whether an AI workflow is fit for purpose. The evaluation record combines technical, domain, and operational evidence.
01 Groundedness
Claims are supported by cited sources and do not overstate what the evidence shows.
02 Completeness
Important evidence, limitations, conflicts, and unanswered questions are surfaced.
03 Relevance
The response addresses the intended decision without irrelevant or duplicated material.
04 Traceability
Source, configuration, model, and review information can be connected to the result.
05 Safety & robustness
The workflow behaves appropriately under adversarial input, failure, and conflicting evidence.
06 Workflow usefulness
Expert review measures whether the system improves speed, consistency, confidence, or discovery.
Human review is part of evaluation. Expert reviewers assess the quality of the system, the evidence, and the final operating decision as separate but connected activities.
Human review
Design the approval path with the same care as the answer.
Review is not a final button added after generation. We define when a person must inspect sources, when a domain expert is required, when escalation is mandatory, and what evidence the reviewer needs to make the decision.
- 01Reviewer preparation
Show the question, retrieved evidence, limitations, and relevant history—not only the generated conclusion. - 02Decision state
Record approval, revision, rejection, or escalation with the responsible person and time. - 03Feedback quality
Capture the reason for correction so issues can become evaluation cases and operational improvements.
Evidence boundaries
Know what the system is allowed to know.
Data minimisation, source governance, and access controls are part of research design. The system should work within a defined evidence boundary rather than searching everything available.
Approved evidence
Name authoritative corpora, document versions, data owners, and permitted uses.
Excluded evidence
Identify unapproved, personal, confidential, or out-of-scope information and test that it is not used.
Staleness
Define how source freshness is shown and when an older document should block a recommendation.
Conflicting evidence
Do not hide disagreement. Show source, date, context, and the reason for prioritisation.
Responsible claims
We do not confuse capability with proof.
This website describes intended capabilities and delivery principles. It does not claim that any specific model, workflow, or deployment is clinically validated, regulatory approved, or guaranteed to produce a particular business outcome. Those conclusions require defined evidence for the actual system and intended use.
Need an evaluation plan? Contact us to discuss a specific use case, evidence boundary, and risk profile.
Make quality measurable
Define what good looks like in your workflow.
We can help turn expert expectations and known failure modes into an evaluation plan your team can use.