How Domain Experts and PMs Collaborate in Confident AI

Confident AI lets subject-matter experts (SMEs) and product managers review production traces and annotate them in the same workspace engineers use, so quality judgments from the business side feed directly into the evals that gate every release. This fits teams where AI quality decisions currently sit with engineers alone, or where review cycles stall because feedback has to be routed through an engineering queue.

The collaboration model: one workspace, four roles

Confident AI describes its platform as a shared workspace for engineering, product, QA, and domain experts, built around the idea of shipping AI "as a team, not as individuals." The roles shown in the product's own illustration are a PM, an engineer, a QA lead, and an SME — all working on the same traces and evals rather than in separate tools.

The practical division of labor looks like this:

The key structural point is that annotation is not a separate side channel. SMEs and PMs annotate alongside engineers, on the same trace data, so a domain judgment becomes a test case without a handoff.

How a review actually flows

The collaboration is anchored on the trace — the record of a single AI interaction, including the agent/tool/LLM call tree, inputs, outputs, model, token counts, and latency.

1. A trace is flagged. Someone reviewing production traces — an SME who knows the domain, or a PM scanning for quality problems — marks a response as bad.
2. The flag becomes a metric. Confident AI's "Turn into metric" step converts the flagged trace into a named eval (for example, "Advice Accuracy") with a type such as G-Eval and a threshold (for example, ≥ 0.8).
3. The metric runs on every release. From then on, every release is checked against that eval, so the same failure can't ship again unnoticed.

The product's own example makes the value of domain review concrete: a response advising a user to "close old cards" to raise a credit score is flagged as bad advice, because closing old accounts lowers credit age. An engineer without that domain knowledge might let it through; an SME catches it in one pass, and it becomes a permanent check.

Why this matters for PMs specifically

Confident AI's customer evidence points to cycle time as the main gain. Igor Kolodkin, Head of AI Quality at Finom, describes the pre-platform workflow: a single improvement cycle took 10 days because it required creating a task, assigning an engineer, waiting for availability, and going back and forth. With PMs able to run the cycle themselves in the shared workspace, the same cycle takes three hours.

That is the collaboration argument in one data point: the bottleneck wasn't the fix, it was the routing. When PMs and SMEs can act on traces directly, engineering time goes to building evals rather than triaging feedback.

What to check before adopting this workflow

• Who will actually review traces? The model only works if named SMEs and PMs have review time allocated. Annotation capacity, not tooling, is usually the constraint.
• What threshold discipline will you use? Each converted metric carries a threshold; teams need a shared convention for how strict those are, or "one consistent bar" fragments across teams.
• How does this fit existing QA? Confident AI positions itself as standardizing evals and observability across an organization. If you already have a QA process, decide whether trace review replaces or feeds it.
• Access and pricing: the site offers "Contact sales" and "Try Now For Free" entry points and a pricing page, but seat limits, role permissions, and plan details aren't specified in the available material — confirm these with sales before planning a rollout across non-engineering roles.

For teams where AI quality feedback currently travels through engineering tickets, the collaboration model here is the main thing to evaluate: it moves the judgment to the people who hold the domain knowledge and keeps the resulting eval in the release path.