Evals
Your experts set the bar,every run is held to it.
Evals turns execution into measurable improvement. Your experts define the rubric for what good looks like, every run is scored against it, and known work routes to the cheapest model that still clears it.
Every run scored against your standard
Outcomes are evaluated against rubrics your domain experts author, so a regression in a runbook, model, or context change is caught the same day.
How is factor-decay-triage scoring this week?
Root-cause 0.91, fix specificity 0.88, runbook quality 0.94, citations 1.00. INC-234 drifted on the citation dimension yesterday.
Quality your team defines and owns
Not an academic benchmark, but your organization's own definition of a good answer, enforced on every run.
Expert-authored rubrics
Domain experts define the dimensions that matter for a task. Humans own the rubric; the platform improves against it.
Every run is scored
Outcomes are evaluated automatically, so a degraded runbook, model, or context shift is caught immediately.
Routing to the cheapest pass
Known work routes to the lowest-cost model that still clears the rubric. Every task records which model ran it.
Golden sets
Accepted outputs become reference examples, the benchmark new models and runbooks are measured against.
Validated before it ships
Every runbook, model, or context change is checked against held-out past work. Nothing deploys unless it improves quality.
A path to owned models
Once rubrics and traces reach critical mass, your accepted work trains models you own and serve.
Compounding
The same work gets better over time
As your documents, runbooks, and standards build up, the same AI models pass more of your work, without switching to a newer model.
- Pass rate climbs as your context and standards are added.
- Corrections become rubric entries the next run is held to.
- The gain is the infrastructure around the model, not the model.
Cheaper at the bar
Only pay for the model the work needs
Once a cheaper model can meet your standard for a kind of task, that work moves to it, and your cost per task drops as volume grows.
- Each task type routes to the cheapest model that clears the rubric.
- Frontier models are reserved for the genuinely novel.
- Pin a workspace to a model when routing changes are not acceptable.
Built for production work.
Run anywhere.
Hosted. Your VPC. Air-gapped. The on-prem Context appliance.
Use any model or agent.
Claude, GPT, Gemini, Kimi, or open weights. Bring your own agent framework, or use ours.
Enterprise-grade authorization.
Identity through your IdP. Customer-managed keys. Audit on every action. Permissions inherited at every connector call.
A complete working environment.
Documents, spreadsheets, decks, kanbans, and file viewers built in. Your team and agents work on the same files in the same environment.
Faster, cheaper, better
Custom models trained on your work
Your team's accepted outputs become training data for models you own and serve, and they beat general-purpose agents on your specific tasks.
Evals gate every change
Rubrics and golden sets validate every runbook, model, and context change against past work before it ships. Regressions are caught automatically.
Step-level model routing
Each step routes to the cheapest model that clears your rubric. Frontier models handle only the genuinely novel, so cost falls without losing quality.
Talk to us.
Bring a workflow your team runs today and see it run in your environment.