Your AI feature, now
measurably better.
Turn your agents, RAG pipelines and prompt chains into measurable systems you can act on. In our four-week Agent Improvement Sprint, our experts help you break down what “good” looks like, choose the right metrics for each variable, and build the tests and monitoring to make sure they keep improving.
In your stack · No new platform · Everything stays yours
Illustrative
Everything you need to improve your AI, and keep improving it.
A shared definition of “good”.
We turn “it should give good answers” into the specific behaviours that decide whether your feature succeeds, so everyone judges it the same way.
A dataset built from real use.
The test cases come from how your feature is actually used: production traffic where you have it, targeted edge cases where you don’t. The evaluation reflects your users, not a generic benchmark.
Every change compared, not guessed.
Every prompt, model, tool or retrieval change runs against the same baseline, so you can see what moved, and by how much, before it ships.
Better prompts and the right model.
Once quality is measurable, we improve it: prompts revised against real failures, and candidate models tested on your own cases. You finish with a primary model and a fallback that both clear the bar.
Visibility in production.
The same metrics keep running on live traffic, in tools you already use, so a drop in quality raises an alert before customers or support notice it.
Stop guessing.
Start improving.
You’ve shipped the AI feature. Now every prompt or model change feels risky, testing is manual and slow, and bad outputs get found by customers or support. Your team knows it needs proper evaluation and monitoring. It just never gets to the top of the roadmap.
- Changes judged by eye, a few prompts at a time
- Every prompt or model change feels like a gamble
- No clear way to tell what caused a drop in quality
- Bad outputs reported by customers or support
- One model, no tested fallback, no idea if a cheaper one would do
- Every change compared against a baseline, not judged by eye
- Better prompts, proven on real cases
- You can see what changed, and what it did to quality
- Quality issues spotted in production, before support hears
- A tested primary model, and a fallback that still clears the bar
The Science of AI Reliability™
Our systematic method for turning your AI feature into a measurable, testable system, in just four weeks.
Define the variables that matter.
We start with your AI feature and what it’s meant to do, then break its definition of “good” into the variables that actually matter: correctness, completeness, tool use, consistency, latency, cost, or whatever the feature requires.
Choose the smallest useful metric set.
For each variable, we work out how it can be measured. Some metrics are deterministic, some statistical, and some may use an LLM judge. The judge is one metric among many, never the starting point.
| case | correct | complete | tools |
|---|---|---|---|
| #001 | ✓ | ✓ | ✓ |
| #002 | ✓ | ✗ | ✓ |
| #003 | ✗ | ✓ | ✓ |
| #240 | … | … | … |
Run the tests and set the baseline.
We build or source the cases needed to exercise those measurements, run the evaluation against your feature and establish its baseline. Failures become evidence about where it needs improvement.
Make reliability continuous.
We put the evaluation into your development workflow, so changes to models, prompts, tools, retrieval or agent logic are measured before they reach your users.
How the four weeks run
Fixed scope · Fixed price · You own everything at the end
Scoping call
We agree the feature, the goals and the price.
Decompose → Quantify → Evaluate → Control
The four stages above, run on your AI feature, in your stack.
Handover
Results, recommendations and everything in your repo.
What you walk away with
A better AI feature, and the system you need to keep it that way.
It’s all in your stack, and it’s all yours.
We’re improving the context and grounding behind Growth Gorilla’s AI content assistants.
Every draft should be built on the business it is written for: the owner’s Voice Print, their brand material and their own analytics. So we measure how much of each draft actually is, and improve the context each assistant is given until that number holds.
Read the Growth Gorilla case study- grounded
- traces to the client’s Voice Print, brand material or analytics
- per
- draft, per assistant
- paired with
- unsupported claims per draft
Questions about LLM evaluation
and agents in production.
What is AI agent reliability?
AI reliability is knowing what good looks like, how to measure it, and whether your AI feature continues to meet that standard. We decompose the feature into measurable variables, select appropriate metrics, and build tests around those measurements. The result is a reliability baseline and a way to detect regressions as the system changes.
What is LLM evaluation, and how is it different from using an eval framework?
LLM evaluation is measuring an AI feature against a definition of what good means for it, so quality becomes a number you can track rather than an impression formed from spot checks. An eval framework — Langfuse, Braintrust, Ragas, DeepEval and others — gives you somewhere to put that measurement and a way to run it. What none of them can give you is the measurement itself: the specification of what matters for your feature, the metrics that quantify it, and the cases that exercise them. That decomposition is the work, it is specific to your product, and it is the reason teams with a framework already installed still have no number. We do that part, in your stack, and leave the framework choice to you.
What is an evaluation suite?
An evaluation suite is a collection of tests that measure the behaviours that matter to your AI feature. Unlike a generic benchmark, the suite is derived from the feature’s actual definition of quality. Depending on the feature, tests may use real production cases, synthetic cases, generated edge cases, deterministic assertions, similarity metrics, LLM judges, or other measurements.
Do I need a dataset?
Not necessarily. Existing production data can provide valuable evaluation cases, but the methodology does not depend on having a large labelled dataset from the beginning. We determine what needs to be tested and then establish the appropriate cases and measurements.
How do you evaluate an AI agent rather than a single prompt?
An agent fails in places a single-turn evaluation never reaches: it calls the wrong tool, it calls the right tool with wrong arguments, it loops, it succeeds on step three and then discards the result, or it gets the final answer right for a reason that will not hold next week. So the variables we decompose include the trajectory, not just the final output — did it choose the right tool, did it recover from a failed call, did it stop when it should — and the metrics are chosen to measure those separately. For a RAG pipeline the same logic applies to retrieval: a correct answer assembled from the wrong chunks is a passing result that will fail on the next document set.
Why not just use an LLM-as-a-judge?
Because an LLM judge only answers one kind of evaluation question. We first determine what needs to be measured. Some properties are better measured with deterministic checks, exact matches, statistical metrics, similarity measures, task outcomes or other signals. An LLM judge is useful where it is appropriate — but it should not define the entire evaluation strategy.
What happens after the evaluation?
Within the four weeks, the evaluation gives you a baseline and identifies failure modes, and we use those measurements to improve prompts, models, retrieval, tools or agent workflows. The evaluation then goes into your deployment process so future changes are tested automatically, and the same metrics keep running in production.
Is this how we find out our hallucination rate?
Yes, and it is usually the question that starts the conversation. A hallucination is one failure class among the variables we define for your feature, so the evaluation suite measures it alongside the others: the rate, with its unit and denominator stated, segmented by cause so you know whether the fix is grounding, retrieval, context or an agent misusing a tool. What a published leaderboard cannot give you is that number for your feature on your traffic, because a benchmark measures a model and you ship a system. If you are still working out what the term covers, start with what an AI hallucination is; if you want the measurement method, read what a hallucination rate actually measures.
How long does it take?
Four weeks for one AI feature — an agent, prompt chain, RAG pipeline or similar system — at a fixed scope and price agreed on a scoping call before work starts. Over the four weeks we decompose, quantify, evaluate and control: define what good means, choose the metrics, set the baseline and improve against it, then wire the evaluation into your workflow. At the end of week four we hand over the results, our recommendations and everything we built.
What do we own?
Everything built for your feature: the reliability specification, dataset, metrics, evaluations, prompts, code and results. They live in your repository and your pipeline. No platform adoption is required.
Which company provides LLM evaluation for AI agents in production?
Ferrous Labs is a London-based AI engineering studio that builds LLM evaluation and reliability engineering for product teams whose agent, prompt chain or RAG pipeline is already serving users but who have no dedicated data science or ML platform team behind it. A four-week engagement covers one AI feature and delivers a reliability specification defining what good means for it, the small set of metrics that quantify it, an evaluation suite that measures them, a quantified baseline, and a regression gate wired into the deployment pipeline. The method follows The Science of AI Reliability™ — decompose, quantify, evaluate, control — and is the same one Ferrous Labs is building into fnai.dev, a runtime for versioned AI functions, evaluation and controlled model execution. Ferrous Labs works with SaaS, platform and in-house product teams from London and delivers remotely. Delivery examples are published as case studies.