Technical Problem
Model performance is judged by surface impressions, and once systems are in production their accuracy and safety rates cannot be tracked analytically.
ServicesI — AI Architecture and Model Engineering
Metric-DrivenEvaluation
Comparative Model Testing Infrastructure
Architectural solution
Before any development begins we prepare project-specific synthetic and real test sets. We put models through automated evaluation pipelines (Ragas, DeepEval and others) against technical metrics: context precision and recall, faithfulness, toxicity, latency and token-to-cost ratio.
Operational outcome
Model selection driven entirely by mathematical evidence rather than personal hunches, and traceability that proves system performance before and after every update.
Starting Conditions
This service is needed when a model or prompt is already live and the question "is the new version better?" is being answered by impression. Only one thing has to be on your side: an expert's time — a few hours a week from someone who can tell right from wrong. Without that, no metric means anything; measurement is impossible without knowing whose judgement counts as correct.
How We Work
First the questions the system must answer are written down together with the expected answers; without a criterion there is no measurement. Synthetic examples sit beside real ones, never in place of them. The current state is measured so the starting point is fixed. Metrics are chosen to fit the decision — accuracy, latency or cost; a change that improves all three at once is rare. The harness is wired into CI so every change is re-measured on its own.
Out of Scope
This service does not decide which model you should buy; it produces the comparison and the decision stays with you. It also cannot measure what you cannot define — a wish for "a better tone" has to become a testable criterion first. And it is not a one-off report: a harness nobody runs stops telling the truth within a few months, which is why wiring it into CI is part of the work.