AI evaluation and Reliability Audit
If you already run an AI feature, assistant or agent, we measure how often it is wrong, why, and what it costs, using your real traffic. Then we tell you what to fix first, or fix it. The packaged version is the AI Reliability Audit.
When you need this
- Users report wrong answers and you cannot tell how often it happens.
- Costs or response times are higher than expected.
- You want to change models or prompts without breaking what works.
- Someone has to sign off on the system and wants evidence it is reliable.
How we do it
1. Baseline
Agree the tasks and success criteria, then measure the current system on a sample of real, redacted traffic.
2. Error analysis
Group the failures into recurring patterns: wrong sources, invented answers, unreliable tool calls, unsafe actions, slow or expensive calls.
3. Prioritize
Rank the fixes by impact and effort, with an estimate for each.
4. Make it repeatable
Leave a test set and checks you can run on every change, in the tools you already use.
What you get
- Scorecard with the current baseline
- Ranked fix list with effort estimates
- Reusable test set and evaluation checks
- A scoped proposal for the fixes, if you want us to make them
A software company's support assistant gives confident but wrong answers about billing. The audit finds most errors trace to outdated help articles being retrieved ahead of current ones. Fixing retrieval ranking and adding a freshness check comes first on the list, ahead of changing the model.
Start with an AI Reliability Audit
1 to 2 weeks, scope agreed per project
We measure your system on real traffic and hand you a scorecard, a ranked fix list and a test set you can rerun on every change.
Questions
Do you need access to our production systems?
Usually not. We work from redacted or sampled data under an NDA, with read-only access where any access is needed.
Is this a compliance certification?
No. We engineer and measure reliability. Regulatory certification is a separate process with its own specialists.
Often combined with
- Custom AI ImplementationTailored AI systems, agents and integrations connected to your data, tools and operating environment.
- AI agent engineeringAgents that do defined jobs with approved tools and human approval.
- Managed Hosting & Ongoing EngineeringKeep your software running and improving after launch.