Engineer measuring a part with a digital caliper, the way evals measure an AI system
Evals & AI Reliability · FDE Squad 04

LLM evaluation.
Know it works before your customers do.

Labs 24 builds the eval suites, release gates and production monitoring that tell you whether an AI system is right, how often, and when that changes. We also rescue AI pilots that stalled because nobody could prove they worked.

Eval sets from your real cases Release gates on every change Drift alerts in production
Discipline04 · Evals & AI Reliability
Service tracksD-03 Fix · built into every build
What we measureAccuracy, faithfulness, safety, cost
In productionSampling, scoring and alerts
In shortUpdated 30 Oct 2026 · Labs 24 FDE Squad

LLM evaluation at Labs 24, in short

LLM evaluation is the practice of measuring whether an AI system gives correct, grounded, safe and well-formed outputs, on real inputs, every time it changes and while it runs. Labs 24 builds eval suites, release gates and production monitoring with forward deployed engineers, and uses them to rescue stalled AI pilots.

What we build
Eval sets, automated scorers, release gates, monitoring and human review queues
What we score
Task accuracy, faithfulness, format, refusals, safety, cost and latency
When it runs
On every commit and release, and continuously on sampled live traffic
Rescue
Audit of stalled pilots: baseline, failure categories, fix plan, re-launch
Stack
Langfuse, custom eval suites, calibrated LLM-as-judge, CI release gates, alerting
Pricing
Set after an initial audit and baseline
01 / What it means

If you can't measure it, you can't ship it.

Most AI pilots don't fail on the model. They stall because nobody can say how often the output is right, so nobody will sign off putting it in front of customers. Evals turn "it looked good in the demo" into a number your team can trust.

Reviewer scoring AI outputs against expected answers on paper and laptopYour team defines what right looks like. We automate the checking.
AI tested by vibes
  • A few prompts tried by hand before launch
  • Changes shipped without comparing to the last version
  • Model updates from the provider go unnoticed
  • Problems found by customers
AI tested forward deployed
  • Eval set built from your real cases and answers
  • Every change scored against the current version
  • Releases blocked when a metric drops
  • Live outputs sampled and scored every day
  • Failures become new test cases
02 / Capabilities

LLM evaluation capabilities.

Eight parts of evals and AI reliability we own end to end, from the first test case to the alert at 2am.

E-01

Eval set design

Golden datasets built from your real inputs and correct answers, covering the common cases, the edge cases and the ones that went wrong before.

Golden setsEdge cases
E-02

Automated scoring

Code checks for format and rules, plus LLM-as-judge scorers calibrated against your human graders so the scores mean something.

LLM-as-judgeCalibration
E-03

Release gates

Every prompt, model or code change scored in CI against the current version. A drop past the threshold blocks the release.

CIRegression tests
E-04

Tracing and observability

Every request traced end to end: inputs, retrieved context, tool calls, outputs, cost and latency, searchable and replayable.

LangfuseTracing
E-05

Monitoring and drift alerts

Live outputs sampled and scored daily. Alerts when quality, cost or latency move, including after silent model updates from a provider.

DriftAlerting
E-06

Human review workflows

Review queues for your experts, with sampling rules and simple labelling, so their judgement feeds straight back into the eval set.

SamplingLabelling
E-07

Red teaming and safety tests

Adversarial test sets for prompt injection, jailbreaks, data leaks and harmful output, run before every release.

InjectionPII leaks
E-08

Production rescue

For pilots that stalled or systems that misbehave: audit, baseline, failure categories, fixes in order of impact, and a measured re-launch.

D-03 FixAudit
03 / A release gate

Every change has to pass before it ships.

An example from a document-processing system. Two proposed changes, scored against the current version on the same 1,200-case eval set. Switch between them.

Current version v2.13 vs proposed change
MetricCurrentProposedAgainst threshold
Faithfulness by document type

Example figures. Thresholds are agreed with your team at the start: what can never drop, and what may trade off against cost.

04 / Eval layers and rescue

Six layers of testing, from every commit to every hour.

No single test catches everything. Cheap checks run constantly; expensive human review runs on a sample. Together they cover the system.

  1. 1
    Code checks

    Schema, required fields, business rules

    Every commit
  2. 2
    Golden set scoring

    Accuracy against known correct answers

    Every release
  3. 3
    Calibrated LLM-as-judge

    Faithfulness, tone, completeness, checked against human grades

    Every release
  4. 4
    Red-team set

    Injection, jailbreak, data-leak and abuse attempts

    Every release
  5. 5
    Human review sample

    Your experts grade a slice of live outputs

    Weekly
  6. 6
    Production monitoring

    Sampled scoring, cost, latency and drift alerts

    Continuous

What we usually find in stalled AI pilots

SymptomUsual causeFix
"Works sometimes"No eval set, so nobody knows the real hit rateBaseline on real cases, then fix the largest failure category first
Made-up factsRetrieval hands the model the wrong contextFix the data path, add faithfulness checks and citations
Quietly got worsePrompt edits or a provider model update, untestedVersion everything, release gates, drift alerts
Too expensive to scaleOne large model on every stepRoute steps to smaller models, proved safe by evals
Team won't use itOutputs can't be checked or correctedCitations, confidence flags and a one-click correction loop
05 / How it ships

Measure first. Then fix what matters most.

The same four steps whether we are building evals into a new system or rescuing one that stalled. A demo every Friday, on your real data.

Step 01 · Audit

Baseline the truth

Tracing switched on, real cases collected, and today's quality measured and split into failure categories.

  • Traces on every request
  • Failure categories ranked
  • Thresholds agreed
Step 02 · Suite

Build the eval suite

Golden set, code checks and LLM-as-judge scorers, calibrated against your experts' grades.

  • Golden set and scorers
  • Judge calibration report
  • Friday demo
Step 03 · Gate

Gates and first fixes

Release gates in CI, red-team set added, and the biggest failure categories fixed and proved.

  • CI release gate
  • Red-team set
  • Top fixes measured
Step 04 · Monitor

Watch it live

Sampled scoring on live traffic, drift and spend alerts, and a human review queue for your team.

  • Quality dashboard
  • Drift alerts
  • Runbook and handover
06 / Stack

LLM evaluation and observability tools.

Your evals stay yours: plain test files in your repo and dashboards in your stack, not locked in a vendor.

Evals
Custom eval suitesGolden setsLLM-as-judgeRed-team sets
Observability
LangfuseTracingCost trackingReview queues
Gates and alerts
CI release gatesDrift alertsSpend alertsOn-call runbooks

Traces contain sensitive data? Observability can run fully inside your infrastructure, with PII redacted before storage. See Security & Compliance.

07 / In production

Evaluated systems in production.

Under NDA, so clients stay unnamed. Every system we ship runs with its eval suite and monitoring handed over.

Metro train at a platform, representing a transit authority rewards system
Public Sector● In production

Transit authority rewards layer

Compliance-first build with every rule-driven decision logged, tested before release and auditable after.

Audit-ready
Customer using a banking app, representing a digital banking offer engine
Fintech● Licensed module

Digital banking offer engine

Every generated offer checked against eligibility rules before it reaches a customer, with a regression suite on each release.

Release gates
Container port, representing a logistics dispatch system
Logistics● Live

Regional logistics dispatch

Proof-of-delivery photos read from WhatsApp, with a sample reviewed by the ops team each week to keep accuracy honest.

Human review loop
08 / The squad around it

Evals sit under every other discipline.

Each part of the system gets its own tests. These are the disciplines whose work the eval suite checks.

01

Agent Engineering

Agent runs traced, scored and replayable.

02

LLM Engineering

Model and prompt changes, proved before they ship.

03

Data & Retrieval

Retrieval hit rate and cited-answer checks.

05

Production & Cloud

Alerts, on-call and rollbacks.

10

Architecture

Reference architecture and build-or-buy calls.

11

Security & Compliance

Red-team results and audit evidence.

09 / FAQ

LLM evaluation FAQ.

What are LLM evals?

LLM evals are automated tests for AI systems. They run a set of real inputs with known correct answers through the system and score the outputs for accuracy, faithfulness to sources, format, safety, cost and latency, so every change can be compared with the version before it.

What do LLM evaluation services include?

LLM evaluation services cover building eval sets from real cases, automated scorers including calibrated LLM-as-judge, release gates in CI, tracing and observability, production monitoring with drift alerts, human review workflows and red-team safety testing. Labs 24 delivers these with forward deployed engineers and hands the whole suite over.

How do you measure hallucinations?

By scoring faithfulness: whether every claim in an answer is supported by the source material the system was given. Labs 24 combines citation checks, rule checks and an LLM-as-judge scorer calibrated against human graders, and tracks the rate by document type.

What is LLM-as-a-judge, and can you trust it?

LLM-as-a-judge uses a language model to grade another model's outputs against a rubric. It can be trusted only after calibration: Labs 24 compares the judge's scores with human expert grades on the same cases and adjusts the rubric until they agree closely, then keeps spot-checking.

How often should LLM evals run?

Cheap code checks on every commit, the golden set and red-team set on every release, a human review sample weekly, and sampled scoring of live traffic continuously so drift and provider model updates are caught quickly.

How do you monitor an LLM in production?

Every request is traced, a sample of live outputs is scored automatically each day, and alerts fire when quality, cost or latency move past agreed thresholds. Failures found in production are added to the eval set so they are tested on every future release.

Can you fix an AI pilot that isn't working?

Yes. This is Labs 24's Production Rescue track. We baseline the current system on real cases, rank the failure categories, fix the biggest ones first, add release gates and monitoring, and re-launch with numbers the team can sign off on.

What are the steps in setting up LLM evaluation?

Labs 24 switches on tracing and baselines today's quality, builds the eval suite and calibrates it against your experts, adds release gates and fixes the biggest failure categories, then runs sampled scoring and drift alerts on live traffic.

How much do LLM evaluation services cost?

Labs 24 prices after an initial audit, once the system, its volume and the number of failure categories are known. The eval suite, dashboards and runbooks are handed over and owned by the client.

Find out how often your AI is actually right.

A 30-minute call. Tell us what your AI system does and where it worries you. We'll tell you how we'd measure it and what a rescue would take.

Book a build call