SERVICESBuilt around
Built around
your work.
Model Selection & Evaluation ↗
Compare existing AI models against your tasks, constraints, and quality requirements before committing to an implementation.
Better models
for the task.
Select and evaluate existing AI models against your tasks, quality requirements, and operating constraints.
Discuss a projectEVALUATION APPROACHCapability is only useful
Capability is only useful
in context.
Define the task
Use a concrete workflow and clear success criteria. Broad model scores alone do not establish usefulness for a particular application.
Compare the tradeoffs
Study response quality, speed, cost, and evidence fidelity together, using the same task and inputs.
Inspect the failures
Document where outputs become incomplete, unsupported, or inconsistent. Use those findings to shape product boundaries.
Evaluation before claims.
Skar Route uses multinomial Naive Bayes trained on 48 synthetic examples. Evidence Rank uses BM25 keyword retrieval. Both run locally. Workbench publishes reproducible diagnostic results, including failures; these are task-specific models, not foundation models.