NeuraScaleNeuraScale
Service · LLM Eval Sprint · 1–2 weeks

Stop your LLM quality from silently degrading.

We turn your real production failures into a trusted golden regression set, calibrated graders, and a CI quality gate that fails the PR when answer quality drops. The model that was good last release can't quietly get worse this one — because the gate catches it before it ships.

The problem

You can't see quality regress until a customer does.

You change a prompt, swap a model version, tweak retrieval — and unit tests stay green because the code still runs. But the answers got worse, and nobody notices until a user complains or a number quietly drops. LLM output isn't covered by the tests you already have.

The fix isn't a generic benchmark that has nothing to do with your product. It's a regression set built from the failures you actually hit, graders that agree with how you judge a good answer, and a gate in CI that blocks the release when quality slips — the same way a failing test blocks a bad deploy.

What you get
  • A curated golden regression set built from YOUR real production failures — not a generic benchmark
  • Calibrated graders that agree with a human reviewer before any threshold is trusted
  • A CI quality gate that exits non-zero (fails the PR) when answer quality drops below threshold
  • Per-grader breakdown so a failing run tells you which dimension regressed, not just 'it got worse'
  • Runs in your own CI — open-source tool, no portal, no lock-in
  • Handoff: you own the golden set, graders, and config. Extendable as your product changes.
Proof

A gate that actually bites.

The point of a quality gate is that it fails when it should. Here's the reference demo we run on llm-eval-ci — the same pattern we calibrate against your product.

Healthy bot · gate passes
100%

On a grounded support bot, the suite passes 100% of the golden set. CI is green; the release ships.

Silent regression · gate fails (exit 1)
100% → 17%

Introduce a plausible-looking rewrite that quietly degrades answers, and pass-rate collapses to 17%. The suite exits non-zero — the PR is blocked, and the per-grader breakdown points at exactly which dimensions regressed.

These are the real numbers from the tool's reference demo, which exercises five of the six grader types. We calibrate the same mechanism against your data — your numbers will be your own.

Six grader types

We wire the subset your product needs. A RAG support bot leans on the first three; an agent leans on tool-call + format; open-ended quality gets the rubric judge.

Grounding
Answer supported by retrieved context
Hallucination
Fabricated facts, confident-but-wrong
Relevance
Actually answers the question asked
Tool-call
Right tool, right arguments, right order
Format
Schema / JSON / structure conformance
Rubric LLM-judge
Open-ended quality on your criteria
LLM-EVAL-CI · sample runci/pull-request
goldencases mined from your real production failures
gradegrounding · hallucination · relevance · tool-call · format
resultpass-rate 100% → 17% on a silent regression
exitcode 1 → PR blocked before merge
How the sprint runs

From failures to a gate in 1–2 weeks.

01
Mine real failures

We pull the cases where your LLM actually got it wrong — from logs, tickets, your reviewer's notes — and turn them into a labelled golden set.

02
Calibrate graders

We tune each grader until it agrees with a human reviewer on your data. A grader you don't trust is worse than none — so we prove agreement first.

03
Wire the CI gate

The suite runs on every PR and exits non-zero below threshold. We tune the bar so it catches real regressions without false alarms on every release.

04
Handoff

You own the golden set, graders, and config. It's open-source in your CI — extend it as your product changes. We can stop and nothing breaks.

FAQ

Common questions.

The CI plumbing is the commodity part — we ship it, but it's not what you're buying. The hard, valuable work is curation and calibration: deciding which production failures belong in the golden set, writing graders that agree with a human reviewer, and tuning thresholds so the gate catches real regressions without crying wolf on every release. That judgment is the deliverable.
We also build the agentsOr embed us fractionalllm-eval-ci on GitHub
Start here

Tell us where your LLM quality could slip.

A sentence or two: what your model does, where you've seen it get worse, and what 'good' means to you. You get back scope, fit, and a fixed price — usually within a day. No discovery-call gauntlet.

By submitting, you agree to be contacted about your inquiry. No sales calls, no spam. 18-24h typical reply.

Prefer email? omar@neurascale.org · typical reply 18–24h · Cairo (GMT+2), built for EU hours.

Next step

Make a bad LLM release fail like a bad build.

A scoped LLM Eval Sprint turns your real failures into a gate that blocks quality regressions in CI. Tell us what you're shipping — we'll tell you the scope and a fixed price.

18–24h reply · Cairo + EU hours · honest scoping