Operational baseline
A view of current behavior, dependencies, reliability risks, costs, and ownership.
AI that can survive production
The patient work after the prototype: connecting, measuring, controlling, and improving a live system.
Production systems encounter cases no prototype could reveal. Models change, data drifts, tools fail, and users surprise us. We make that behavior visible and give teams practical ways to review failures, protect the work, and improve without guessing.
Where this can help
Often a good fit
- Teams moving an AI prototype into production
- Organizations with an AI feature they cannot evaluate
- Operators facing cost, latency, or reliability problems
Probably not the right fit
- Tracing with no measure of task success
- A handoff with no owner
- Optimizing scores disconnected from user outcomes
What the engagement can produce
Evaluation and monitoring
Representative tests, production signals, alerts, and review routines tied to the outcome.
Control surface
Practical ways to inspect, retry, approve, recover, and change the system safely.
Improvement cadence
A repeatable process for turning evidence into better releases.
How we work together
Observe
Understand what the live system does, including the work people perform around it.
Measure
Define quality, cost, latency, reliability, and user outcomes that matter for the job.
Control
Add safe release mechanics, fallbacks, approvals, and clear ownership where needed.
Improve
Review evidence regularly and make changes against a stable expected behavior.
What may be involved technically
- API integration
- Tracing
- Offline and online evaluation
- Versioning
- Cost controls
- Fallback design
Questions teams often ask first
Can you take over an existing system?
Yes. We begin by making behavior, dependencies, and failure modes observable before changing architecture.
What should be evaluated?
The end task, model-dependent steps, retrieval, tools, safety, latency, and cost using real cases.