Benchmarking against your data
Candidate models run against a held-out set drawn from the decisions and language your team handles.
CAPABILITIES / AGENT AND LANGUAGE SYSTEMS
Model selection and tuning
Choosing per job on results and cost, then keeping the choice honest as models change.
What this covers
Prompt engineering
Prompts define the job, allowed context, output contract, and failure behavior in versioned code.
Fine-tuning where it pays
We tune only when measured errors persist after retrieval, prompting, and deterministic checks are sound.
Model routing and fallback
Routing selects a model by task and sends known failure modes to a tested fallback path.
Cost and latency budgets
Token spend and response time are tracked per workflow against limits set before production.
How it shows up in the work
ENTERPRISESOne platform for a growing agent workforce.The first agent is a project. The tenth is a platform problem. Shared retrieval, model routing, monitoring, and tuning are what make the second agent take weeks instead of months.ENTERPRISESMove governed AI into production.A review queue is a place where trained people apply rules to a stream of items. Agents can carry most of that load, if every decision arrives with the evidence behind it.BUSINESSESRun leaner with agents on the busywork.Intake, quoting, scheduling, chasing. The middle of the day disappears into work that has a pattern. Agents can carry the pattern and leave your team the judgment.
WHERE IT FITS
Model benchmarks belong in the proof of value and continue as part of operated model reviews.
