Evaluation framework
Define representative test sets, quality measures, unacceptable outcomes and release thresholds.
AI quality can change as models, prompts, data and user behaviour evolve. Juan Infotech helps teams define what to measure, who owns each decision and how feedback, incidents, access, cost and model changes are reviewed after release.
This service is intended for organisations moving an AI pilot into production or strengthening an existing AI-enabled workflow that lacks consistent evaluation, operational visibility or change governance.
The exact scope is agreed after discovery, with dependencies and responsibilities made visible before implementation.
Define representative test sets, quality measures, unacceptable outcomes and release thresholds.
Capture useful signals for quality, latency, cost, tool use, errors and human escalation without unnecessary sensitive content.
Assign ownership for models, prompts, data sources, access, provider changes and release approval.
Create a route to investigate harmful, incorrect or unexpected behaviour and turn evidence into controlled improvements.
Each phase produces something reviewable before the next commitment is made.
Document the workflow, risks, current performance and responsible owners.
Create evaluation sets and production signals linked to user outcomes.
Define access, release, provider and data-change procedures.
Prepare triage, rollback, escalation and communication paths.
Use recurring evidence to improve or restrict the capability.
No. Useful operation may also require retrieval quality, task completion, safety, latency, cost, escalation and user-feedback signals.
No. We help implement technical and operational controls within an agreed scope; clients should obtain appropriate legal and regulatory advice.
Yes, although evaluation and observability are more effective when designed before production release.