AI QA and LLM Evaluation Services for features that never answer the same way twice.

Traditional QA assumes deterministic output: input X always returns output Y, and a test asserts the match. An LLM feature can return a different, equally valid answer on every run, so exact-match assertions either fail constantly or get loosened until they catch nothing. The replacement is evals: score a dataset of representative inputs against defined criteria and gate the release on the aggregate. Classic QA still covers the layer around the model.

An LLM eval is a repeatable test that scores a language model's output against defined criteria instead of asserting one exact answer. A suite runs representative inputs through your feature and grades each output for accuracy, groundedness, format compliance or safety. Graders can be rule-based checks, human reviewers, or another model applying a calibrated rubric. Run it on every change and you get a regression signal for a non-deterministic system.

Four things: a dataset built from real, production-like inputs, graders that score each output against your definition of good, thresholds that turn scores into a pass or fail decision, and CI wiring so releases are gated on the result. RAG systems also need retrieval quality measured separately from generation quality, because the two fail differently. Production monitoring for drift and cost closes the loop.

Yes, and agent testing needs more than single-response evals. An agent takes multi-step actions, calls tools and makes decisions, so it can fail mid-trajectory in ways a one-shot output never shows. We test tool-call correctness, trajectory quality (a sane path, not just a sane answer), failure recovery, and resistance to prompt injection through tool inputs. We operate agents ourselves, so the test design comes from running them rather than from reading about them.

We scope before we quote, and the scoping is the part worth doing carefully: two features described in the same sentence can need labelled datasets of wildly different size. Four things move the number. How many criteria the feature has to satisfy. Whether representative production data already exists or has to be assembled and labeled. Whether the graders can be rule-based or need model-graded rubrics. And whether you already have CI to hang the suite on. Three billing models: fixed price, outcome based and quoted per project; time and materials, billed hourly and quoted per project; and team extension, billed monthly per team member.

Three things set the pace: whether representative inputs already exist or have to be gathered, whether anyone has written down what good means here, and whether there is a CI pipeline the suite can plug into. Arrive with production logs and a definition of done and a scored baseline lands early; arrive with a prototype and an opinion and the first stretch goes on the definition. Any duration quoted before those three are known would be a guess, so discovery produces the plan and the dated estimate before you commit budget.

Yes. The suite runs in your existing CI rather than a separate portal, calls your existing model provider and data stores, and reports into whatever observability you already run. We work across the stacks our own AI products use: Python services, OpenAI and Whisper models, LangChain orchestration, and AWS with Bedrock. Where the feature reaches your systems through tools we test that boundary too, which is why MCP server work sits next to this service. Because the dataset comes from production-like data, every engagement runs under our ISO 27001:2022 and ISO 9001:2015 certifications, audited by Quay Audit UK.

The EU AI Act entered active enforcement, requiring organizations in or serving European markets to classify AI systems by risk, prepare oversight plans, conduct red-team testing, and publish transparency documentation. Evals produce the evidence that kind of scrutiny asks for: defined quality criteria, repeatable results, and a documented record of behavior across changes. Which risk category you fall into is a question for your counsel; we are engineers, not lawyers. What we build is the discipline underneath compliance, so when the question comes you have measurements instead of assurances.

Every full-service vendor can claim evals plus guardrails plus observability, this page included, so the category separates nobody from anybody. Below it sit four things an outsider can actually check, and each one should be put to us too. Ask how a grader gets calibrated and what its measured agreement with human judgment was, because a grader nobody checked manufactures confidence. Ask what the vendor does not hold: we hold ISO 27001:2022 and ISO 9001:2015, audited by Quay Audit UK, with ISTQB-certified engineers on the classic-QA layer, and we do not hold SOC 2. Ask who keeps the dataset, the graders and the thresholds afterwards. And ask for review URLs instead of a rating: every client quoted here links to the full interview their sentence came from.

When the feature is a two-week internal prototype with friendly users: instrument it, ship it, and come back when it faces customers or auditors. When the outputs are deterministic enough for a test to assert one right answer, where QA and test automation is cheaper. When you need a foundation model trained or fine-tuned from scratch, or benchmark work aimed at publication: hire researchers, we are not a research lab. And when the data cannot support the feature at all, an AI-readiness assessment is the cheaper first conversation, and our enterprise AI roadmap covers what stalls a program first.