Generative AI
Generative AI has moved from experiment to infrastructure. We build it into the places where it earns its keep — customer-facing product features, internal knowledge systems, content pipelines, decision support — with the retrieval architecture, evaluation loops, and observability that separate a system you can run from a demo you can show.
Production GenAI is an engineering problem wearing a prompt's clothing.
Anyone can demo a wrapper around a chat model. Shipping something that behaves consistently at volume means answering the boring questions: which model, at what latency and what cost per thousand calls, grounded in which retrieval index, validated by which evaluation set, and failing into what when the provider has an outage. We design the full system, from model selection and RAG pipeline through output validation to a cost report you can read, because the unglamorous half is the half that survives contact with real traffic.
LLM-powered product features
Semantic search, natural-language interfaces, generated summaries, personalisation. We scope, architect, and ship the features that make the product meaningfully smarter, without adding latency your users feel or a per-call cost you did not budget for.
RAG and knowledge systems
Retrieval-augmented generation over your own documents, databases, and knowledge bases, so answers are grounded in your material and can be traced back to the source that produced them. Retrieval quality, not model choice, is where most of these systems are won or lost.
Content and creative systems
Structured pipelines for briefing, first-draft generation, brand-voice enforcement, and editorial review, so a content operation can scale its output without scaling its headcount at the same rate.
Guardrails and evaluation
An evaluation set built from your real cases, run on every change, plus output validation and monitoring in production. Without it you cannot tell whether the prompt someone edited last Tuesday made the system better or quietly worse.
- GenAI system architecture and model selection rationale
- Fine-tuned or prompted model with evaluation benchmarks
- RAG pipeline + vector store setup (where applicable)
- Guardrail framework + output monitoring dashboard
- Integration into your product or CMS stack
- Cost and latency optimisation report
The engagement
Baseline
Define the use case and measure the workflow as it runs today. Without a baseline, an AI feature can only be evaluated on how impressive it feels.
Architect
Select the model and retrieval strategy against your latency, cost, and privacy constraints. Design the evaluation set and the guardrails at the same time.
Ship
Build, fine-tune, integrate, and red-team. The system faces adversarial cases from us before it faces them from your users.
Operate
Monitor outputs and cost, refresh retrieval indexes as your content changes, and re-run the evaluation suite on every model or prompt change.
Common questions
OpenAI, Anthropic, Google Gemini, Mistral, and open-weight models such as Llama. Selection follows your latency, cost, privacy, and capability requirements. We have no vendor relationship to protect, which is the point.
By layering: grounding responses in your verified data through retrieval, adding output validation, and running a structured evaluation set before deployment and on every change after it. No system eliminates hallucination entirely — anyone claiming otherwise is describing a demo — so we build the tooling to detect, flag, and contain it.
We model inference cost at your expected volume during architecture, agree a budget, and report against it. Cost per call is a design constraint, not something you discover on the invoice in month three.