Evals and reliability
LLM evaluation and AI reliability engineering
Without evals, a prompt edit or a model upgrade is a guess. The change looks fine on the three examples someone tried, ships, and quietly breaks a case nobody checked until a customer reports it.
- Eval harness
- Regression suites in CI
- Calibrated LLM judges
- Red teaming
Start a project
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You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.
Signs you need this
We build the measurement layer for LLM systems. A versioned test set from real traffic, scoring that combines code checks with model judges calibrated against human labels, regression runs in CI, adversarial testing, and production tracing that feeds new failures back into the suite.
- Model upgrades postponed because nobody knows what will break
- Quality judged by whoever last tried a few prompts
- An LLM judge nobody has checked against human scores
- No test cases built from real production failures
- Prompt injection and jailbreaks never tested
- No trace of what the model saw when it went wrong
What you get
What is included
Eval harness
A versioned dataset, scoring functions and a runner in your repo that produces one comparable number per change.
Regression suites in CI
Evals run on every pull request that touches prompts, models or retrieval, with thresholds that block a bad merge.
Calibrated LLM judges
Model-graded rubrics checked against human labels, with agreement rates reported, so the judge is trusted for a measured reason.
Red teaming
Prompt injection, data exfiltration, jailbreak and abuse testing against your actual tools and data, with findings fixed and added to the suite.
Observability
Tracing for every request: inputs, retrieved context, model calls, tool calls, latency and cost, searchable when something breaks.
Guardrails
Input and output checks for PII, policy, schema and grounding, tuned to block real problems without refusing good requests.
How it runs
How we deliver Evals and reliability
- 01
Define what good means
Turn product requirements into scored criteria with your domain experts, and label a first set of cases.
- 02
Build the harness
Dataset, scorers and runner in your codebase, with a baseline score for the current system.
- 03
Wire it into delivery
CI gates, production tracing and a loop that turns live failures into new test cases.
- 04
Attack and harden
Red team the system, add guardrails where it fails, and rerun the suite to confirm nothing regressed.
Industries we build this for
Evals and reliability questions
Something else? Email t@moher.ai and an engineer will answer it.
What is an LLM eval harness?
A set of representative test cases, scoring methods and a runner that gives a comparable quality number for any change to prompts, models or retrieval. It should live in your repo and run in CI.
Can we trust an LLM to grade another LLM?
Only after checking it. We measure the judge against human labels on a sample and report agreement. Where agreement is weak, we use code checks or human review instead.
We already shipped. Is it too late for evals?
No. Production traffic is the best source of test cases. We sample real requests, label the failures and build the suite from what actually goes wrong.
Which observability tools do you use?
OpenTelemetry-based tracing with Langfuse, LangSmith, Braintrust or your existing stack. We pick based on where your team already works and where the data is allowed to go.
Do guardrails slow responses down?
A little. Fast checks run inline and slower ones run in parallel or after the response. We measure the latency each guardrail adds and drop the ones that don't earn it.
Tell us what you need built
Four quick questions and an email. An engineer reads every brief and replies within one business day with next steps, and a written scope follows within a week.
- Written scope and price within a week
- Production deploy by week three
- Your repo and cloud from the first commit
- Senior engineers only, no account managers
Start a project
Step 1 of 5
Got it. An engineer is reading your brief.
You will hear from us within one business day, usually sooner. If it is urgent, email t@moher.ai.