AI agent evaluation in Raleigh.

The technical reality of research-grade evaluation in the Triangle.

Questions from Raleigh-Durham buyers.

Our quality team asks what validation an AI assistant needs in a GxP-adjacent environment. What is proportionate?

Proportionate to the system's documented role, which is why the role gets documented first. The framework we scope with quality teams: if the system drafts and retrieves while a qualified human remains the documented decision point (the sane configuration for most CRO and clinical-operations assistants), validation is fit-for-intended-use evidence: documented intended use and boundaries, performance qualification on your real documents (faithfulness rates, retrieval accuracy, with acceptance criteria your quality lead signs), vendor assessment for the model layer, change control (model or prompt updates trigger re-qualification of affected behaviors), and periodic review with records. What it is not: full computerized-system validation as if the model were writing to GxP records unsupervised, a burden that kills useful tools for no risk reduction. We write the validation plan in your quality system's own format, scope it to the documented role, and keep the boundary bright: the moment a workflow proposes removing the human step, the validation conversation legitimately changes, and we say so.

Can evaluation results serve as evidence in SBIR reports and grant deliverables?

Yes, and designed slightly differently, they serve well. Program officers reviewing Phase I and II reports want demonstrable progress against stated technical objectives, and evaluation artifacts are exactly that when framed for it: baseline-versus-current performance on a documented test set (the improvement narrative with numbers), methodology described reproducibly (sampling, grading, metrics, the same rigor as any methods section), limitations stated honestly (reviewers trust reports that name what does not work yet), and per-aim mapping (each technical objective gets its evaluation evidence, so the report assembles rather than gets written). The design adjustments for grant service: lock a benchmark set early so progress is measured against a constant (drifting eval sets make progress claims unfalsifiable), version everything so Phase II can cite Phase I's numbers cleanly, and keep raw run records, since the commercialization diligence that follows successful phases increasingly asks how the numbers were made. We set the harness up with reporting calendars in mind, which costs nothing extra and saves report-writing weeks.

What makes an evaluation methodology reproducible enough for a research collaboration?

The standard your collaborators already apply to experiments: another team, given the artifacts, reaches the same numbers. Concretely: versioned eval sets with documented provenance (where cases came from, what population they represent, exclusion criteria), pinned configurations (model identifiers, prompts, judge prompts, temperatures, all recorded per run), variance reported (repeated runs with spread, not the best draw, because LLM outputs fluctuate and honest error bars are the difference between measurement and marketing), calibration provenance for any LLM judge (agreement against human grades on held-out samples, reported beside every score), and a methods document that travels with results. For multi-institution collaborations, add data-handling notes (what crossed institutional boundaries, under what agreement) and grading-protocol documentation so a collaborator's staff can grade compatibly. This is not ceremony; it is what lets a Duke lab, an RTP company, and an NC State group trust one number, and it is the existing culture of this region applied to a new instrument.

Can the evaluation harness run on university or institute infrastructure?

Yes, and for IRB-governed or pre-publication work it often should. The deployment: eval sets and run records in institutional storage, harness execution as containerized jobs on campus compute (the same clusters your pipelines already use; evaluation workloads are bursty and schedule politely), and judge models served from campus GPUs where data policy requires it, with the judge verified against your graded standard like any other, since open-weight judges on campus hardware trail frontier judges and the gap gets measured, not assumed. Scheduling reality gets designed for: campus clusters have queues and maintenance windows, so the harness checkpoints and the CI integration tolerates delay (smoke suites run on departmental machines; the full battery queues for cluster time). The deliverable includes the deployment-as-code and a runbook written for the lab member who inherits it, because student turnover is the institutional constant and the harness should survive it. Where unrestricted workloads can use commercial APIs, the hybrid is fine and the boundary lives in configuration, not discipline.