Evaluation & Continuous Improvement
AI systems cannot be improved reliably if success is judged through a small number of impressive examples.
We create representative datasets based on real tasks and define measurable criteria for success. Models, prompts, retrieval strategies, tools, architectures, and training approaches can then be compared objectively.
Evaluation happens at multiple levels: model outputs, components such as retrieval and routing, and complete agent trajectories, including tool selection, task sequence, permissions, failure recovery, and final outcome.
Deterministic checks, model-based evaluation, and human review are combined where appropriate. Once the system enters production, real failures, user feedback, unusual edge cases, and execution traces become new evaluation cases and expand the regression suite.
We also evaluate the economics of the complete workflow. Model price alone does not capture reasoning depth, context size, tool calls, infrastructure, retries, failures, and latency. We care about cost per successful outcome, not simply cost per token.
Over time, the evaluations, workflows, feedback, data, and trained models become part of the company’s own intelligence and competitive advantage.