Ship AI features without shipping regressions
Evigauge
Product engineering · Use case
AI features fail quietly — a prompt change degrades an answer and a customer finds it first. Evigauge catches it at review.
Product engineering
Treat an AI feature like software: give it a gate at review time, and the regressions stop reaching customers.
The situation
Copilots, assistants, summarisation and search — shipped by the same teams, on the same release cadence as the rest of the product.
The difference is the rest of the product has tests. An AI feature can regress on a prompt edit and still pass every one of them.
Where the value leaks
Three costs land before anyone files a ticket. None of them shows up as a failure, which is exactly why they persist.
| Cost | Where it goes |
|---|---|
| TIME | Debugging is archaeology A bad answer means five dashboards and a guess at which layer moved. The day goes on narrowing, not fixing. |
| COST | Premium models on simple work Stages are provisioned once at the top model and never revisited. The bill grows with traffic and nobody owns it. |
| RISK | Regressions reach users first With no gate at review time, the first signal quality dropped is a support ticket. |
One answer, beat by beat
A single run, with the record Evigauge writes at each step and what that record is worth once it exists.
- 01
Request in· Trace opened
Every request is debuggable later, without anyone turning logging on.
- 02
Context retrieved· Retrieval path
A bad answer is traced to what it read, in one place.
- 03
Model call· Cost and latency
Spend is attributed to a stage someone owns.
- 04
Guardrail· Failure flags
Quality drops show up at review, not in a support ticket.
- 05
Response out· Replayable trace
Last Tuesday’s complaint can be opened and examined today.
What we bring to it
Each capability, what it does to the run, and the result the business can hold someone to.
| Offering | What it does | What you get |
|---|---|---|
| Reasoning | Rebuilds the decomposition and decision graph behind an answer from the app's own execution. | An engineer opens one execution and names the layer, instead of narrowing across five. |
| Performance | Slices latency by retrieval, tool call, generation and guardrail, with thresholds learned per operation. | Slow is attributed to a layer, so the fix is scoped before anyone starts. |
| Ship Check | Runs at pull-request time against the diff and can fail the build on a broken cache, a bloated prompt or rising failures. | Quality regressions are caught in review, on the same cadence as every other bug. |
| Cost & speed | Names the stage running a premium model on simple work, the cheaper model that fits, and the monthly figure. | A costed change your team can make this week, with the traces attached. |
What actually changes
Five dashboards and a hypothesis
One execution path and a named layer
Quality regressions found by customers
Caught at pull request, before merge
Model spend grows with traffic
Over-provisioned stages surfaced and priced
Around the table
The question each officer is asked, and the answer this puts in their hands.
CTO
“Why did this break, and how long to fix?”
The layer is named in the trace, so the estimate is real rather than a guess.
VP Engineering
“Are we slowing down because of AI?”
AI defects enter the same review gate as everything else, so they stop consuming sprint time.
Head of AI Product
“Is quality holding as we ship?”
Grounding and failure rates are compared release over release, not judged by feel.
Treat an AI feature like software: give it a gate at review time, and the regressions stop reaching customers.
Ready to see it on your own workload?
Bring one real run. We will show you what happened.