Find the AI work
you do every single day.
Tasks with a stable format, clear grading criteria and high volume are the best fit for cheaper, specialized models. Three typical workloads:
Evidence pack assembly
Pull evidence from tickets, repos and cloud configs, then organize it into structured documents mapped to your audit framework.
Task: build the quarterly access-control evidence pack
Codebase context lookup
The file hunting, symbol search and call-graph tracing that repeats in every coding session goes to a fast small model, so the frontier model can focus on real reasoning.
Task: list every caller of an endpoint
Weekly incident digest
Roll up each week's incidents with owners, status, blast radius and next actions into one consistent report.
Task: generate the weekly SRE incident summary
Three things to get started.
A set of real examples, a model you already use, and a job that keeps coming back.
Define "correct" first
Before changing anything, turn your quality bar into a scored evaluation set.
Then pick the lever
The eval tells you which lever to pull: sharper prompts, smarter routing, or a trained skill model.
Keep verifying
After launch, shadow comparisons keep scoring the work so regressions show up early.
Want to see real numbers?
We publish the baseline, grading method and outcome for every workload we report.
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