AI agent evaluation in San Diego.

The technical reality of research-grade evaluation in San Diego.

Questions from San Diego buyers.

Our quality standard is a scientist's judgment. How does grading work without burning PI and senior-scientist time?

By spending that judgment only where it is irreplaceable and engineering the rest. The workflow: candidate sets drafted from real de-identified material (ELN entries, assay reports, trial documents), sampled across the variety that matters (instruments, protocols, authors, the abbreviation-dense shorthand variants), pre-screened by us for mechanical properties, then graded by your scientists in structured 60-to-90-minute sessions with rubrics in their dialect: not 'rate this summary' but 'would this rollup misdirect a program decision' and 'is the assay condition captured correctly.' Disagreements between graders resolve live and become rubric text. A durable standard (200 to 300 graded items) typically costs senior scientists a day or two total; calibrated judges then carry volume with monthly sampled re-grades keeping them anchored. The asset compounds: every correction a scientist makes in production gets sampled back into the set, so the ground truth grows with the program rather than fossilizing at the pilot.

Can evaluation infrastructure run inside certified or air-gapped environments?

Yes, and for controlled work it must, so we design the harness as deployable software rather than a service. The architecture for certified environments: eval sets, graded standards, and run histories in your approved storage; harness execution as containerized workloads on your compute; judge models served from whatever inference your environment certifies (GovCloud-hosted endpoints where available, self-hosted open-weight models where required), with the judge itself verified against your graded standard before its scores count, because a weaker certified judge with measured agreement beats an uncertified stronger one you cannot use. Reporting lands in your existing tooling, and the whole stack ships as infrastructure-as-code your cleared staff deploys, with documentation written for that handoff. The honest constraint we state up front: certified-environment lineups change the quality-cost frontier, so judge calibration matters more, not less, there. The disqualifying question for most eval platforms ('does our data leave?') is the design premise here: nothing leaves, including the evaluation.

How is faithfulness verified for trial and regulatory document workflows?

Claim-level verification with the error taxonomy regulated documentation actually fears, tuned to clinical-trial stakes. The harness decomposes generated outputs (deviation summaries, monitoring-visit syntheses, safety-narrative drafts) into atomic assertions and verifies each against source documents: supported, unsupported (the fabrication class, catastrophic in anything regulator-visible), contradicted (the draft misstates the source: the class that corrupts decisions), and omitted (source facts material to the document's purpose missing from the output, the silent class clinical operations rates worst). Verification respects document structure: a claim about protocol version 3.1 must trace to 3.1, not to the superseded 2.0 language, which makes version-aware retrieval part of what gets tested. Calibrated judges carry volume after agreement with your clinical-operations graders is demonstrated, gates tier by visibility (anything submission-adjacent holds the strictest bars), and the run records are kept GxP-shaped: versioned, attributable, reconstructable, because the evaluation evidence may itself be inspected.

Our culture expects methods sections. What does reproducible evaluation methodology mean concretely?

That another competent team, given your artifacts, would reach the same numbers, which is the same standard your science already holds. The practices: versioned everything (eval sets, rubrics, judge prompts, model identifiers, sampling frames, all pinned per run, so 'accuracy was 94 percent' always answers 'on which set, judged how, when'); documented sampling (how cases were selected, what population they represent, what was excluded and why); seeded determinism where the stack allows it, and reported variance where it does not (LLM outputs fluctuate; honest methodology reports the spread across repeated runs rather than the best draw); calibration provenance (judge-human agreement measured on held-out samples, reported with the scores); and a written methods document that travels with the results. The payoff is institutional: results survive personnel changes, audits, and skeptical collaborators, and the methodology document itself becomes reusable across programs. Teams from research cultures adopt this fastest, because it is not a new discipline; it is their existing one, applied.