Technical problem
Model performance is judged by surface-level observation, and once systems are live there is no analytical way to track accuracy and safety rates.
Metric-driven evaluation and comparative model test infrastructure
Architectural solution
Before development begins we prepare synthetic and real test sets specific to the project. We put models through automated evaluation pipelines (Ragas, DeepEval and others) against technical metrics: context precision and recall, faithfulness of the answer, toxicity, latency, and the token-to-cost ratio.
Operational outcome
Model selection driven entirely by mathematical evidence rather than personal guesswork, and traceability that proves what a change did to system performance before and after.
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AI architecture and model engineering
Enterprise RAG architecture and vector database integrationStandard large language models cannot reach private in-house data, fall out of date, and produce confident answers with no basis in fact when questioned.Fine-tuning, LoRA/QLoRA and model distillationGeneral-purpose models fall short against the jargon of a given sector — law, medicine, finance, manufacturing — or against business logic specific to one company; and running general models in production brings high API and hardware costs.
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Which one shall we start with?
See it on your own data in one session, or write first and ask what we do.