One use case · 10 business days

From “is it good?”
to a quality bar you can use.

A focused AI evaluation engagement for teams with a working system and a quality problem worth solving. Establish a baseline, test one improvement, and leave with a repeatable method.

Scope your Sprint

The work, in four parts.

01 / Quality contract

Agreed success criteria, an anchored rubric, and a representative evaluation set for one priority use case.

02 / Baseline

A repeatable procedure or lightweight harness, recorded configuration, and the first evaluation run.

03 / Failure analysis and experiment

A breakdown of observed failures, one targeted improvement experiment, and a rerun against the agreed criteria.

04 / Handoff

Evaluation artifacts, an executive readout, and a prioritized 30-day plan. Your team can carry the work forward.

A working system.
A decision to make.

Bring one technical owner, representative examples, access to the relevant system, and time for two working sessions and timely feedback. The 10-business-day period starts once agreed access and inputs are ready.

A focused scope.

The Sprint is not a full product build, unlimited labeling, ongoing monitoring, or a comprehensive security audit. Additional use cases and implementation are scoped separately. A measured accuracy improvement or production-readiness guarantee is not promised.

Scope, pricing, access, tools, and data terms are agreed before kickoff. Keep initial inquiries high-level.

Inspect the thinking
behind the work.

Our internal rubric-generator case study shows how specific failures became seven prompt fixes. It is an internal demonstration, with no claimed accuracy uplift.

Read the case study
What an evaluation engagement should deliver

What isn’t working
the way it should?

Tell us what you’re building, where it breaks, and what you need next. We’ll reply by email to discuss fit and scope.

evals@rubrex.ai
WHAT HAPPENS NEXT
  1. A short email exchange about the problem.
  2. A technical conversation if there’s a fit.
  3. A written scope and quote before any work begins.

Keep it high-level. No credentials, sensitive datasets, or customer records. How we handle this message.