Technology
OpenAI · Claude · Gemini Testing& Development
We build LLM-powered features, chatbots, agents, RAG knowledge systems, on OpenAI, Claude, and Gemini, and validate them with our own AI Quality Engineering practice: prompt regression suites, retrieval accuracy against golden datasets, and hallucination checks before anything reaches users.
Chatbots, agents, and RAG systems built production-grade
Model/provider selection based on measured evaluation, not hype
Prompt regression + output schema validation in CI
The vendor rarity: we build it AND know how to test it
- Models
- OpenAI, Anthropic Claude and Google Gemini APIs
- Patterns
- RAG pipelines, tool-using agents, structured extraction, chat
- Validated by
- Our own evaluation practice, not vibes
- Ships with
- A golden dataset and evaluation harness in your CI
LLM features fail differently
Conventional software fails loudly.
Conventional software fails loudly. An LLM feature fails quietly: it returns a fluent, well-formatted, confident answer that happens to be wrong. No exception is raised, no status code changes, no alert fires. The user believes it, and you find out from a support ticket weeks later, if at all. That is the core reason shipping AI features without an evaluation practice is a different risk category from shipping ordinary features, and we set it out in 5 Ways LLM Features Fail Silently.
We build integrations against OpenAI, Claude and Gemini, and we build them with the evaluation layer attached from the start rather than retrofitted after the first incident. That is the same discipline our AI Quality Engineering practice applies to features other teams have already shipped.
What we build
RAG pipelines: chunking and embedding strategy, retrieval tuning, grounding enforcement, and citation so an answer can be traced to a source rather than trusted on tone.
Tool-using agents: scoped tool definitions, argument validation, permission boundaries, loop and cost limits, and explicit handling for the case where the model calls the wrong tool confidently.
Structured extraction: schema-constrained outputs with validation and repair paths, because a malformed JSON response is a production incident in a pipeline.
Conversational features: context management, memory boundaries, refusal and safety behaviour, and graceful degradation when the provider is slow or rate-limited.
Provider abstraction: an interface that lets you switch or A/B models without rewriting the product, plus caching and cost controls that keep the bill predictable.
The evaluation layer is not optional
Every integration ships with a golden dataset, real inputs with agreed correct outputs, and a harness that scores each change against it: accuracy, grounding, refusal behaviour, latency and cost per request.
Every integration ships with a golden dataset, real inputs with agreed correct outputs, and a harness that scores each change against it: accuracy, grounding, refusal behaviour, latency and cost per request. It runs in CI, and a meaningful regression fails the build the same way a broken unit test would. That is what makes a prompt change a reviewable engineering event instead of a hopeful edit to a string.
We also test the things people forget: adversarial and prompt-injection inputs, behaviour when retrieval returns nothing relevant, non-determinism across repeated runs, and what the user sees when the provider times out. The method is written up in How to Test a RAG Pipeline and Testing AI Agents: The Pre-Launch Checklist.
Engagement path
How the engagement runs
How a OpenAI · Claude · Gemini engagement runs from first call to handover.
Define correct
Before any code, we agree what a good answer looks like and build a golden dataset from real inputs, because unmeasurable quality cannot be engineered.
Build the integration
RAG, agent or extraction pipeline with grounding, schema validation, permission boundaries, cost limits and graceful degradation designed in.
Evaluate in CI
Accuracy, grounding, refusal, latency and cost scored on every change, with thresholds that fail a build on meaningful regression.
Monitor in production
Output sampling, cost tracking and drift detection, so quality is observed continuously rather than assumed after launch.
Deliverables
What you get
Artefacts you keep and can run without us. Everything lives in your repositories and your pipelines.
Handover pack
6 artefacts · yours to keep
A working integration against OpenAI, Claude or Gemini in your codebase
A golden dataset of real inputs with agreed expected outputs
An evaluation harness scoring accuracy, grounding, refusal, latency and cost
CI thresholds that fail builds when quality regresses
Adversarial and prompt-injection coverage, plus empty-retrieval behaviour
Provider abstraction with caching and cost controls
Self-check
Signs your team needs this
If more than one of these is true, it is usually cheaper to fix now than after the next release.
An AI feature is live and quality is judged by spot-checking outputs
Prompt changes ship without any regression signal at all
Nobody can say whether last month's model update made the product better or worse
Token spend is unpredictable and nobody owns it
The feature has never been tested against adversarial or empty-retrieval inputs
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
Still curious? Talk to usKeep reading
Where to go next
Teams evaluating OpenAI · Claude · Gemini usually read these next.
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- A Senior Engineer replies, not a sales layer
- Within one business day, every time
- NDA available before you share any details
16+
Years QA leadership
16
Testing disciplines
6
Markets served
1
Business day to reply
Tell us where quality hurts
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