AI development that ships to production — not just demos.
We build production AI systems — LLM integrations, autonomous agents and RAG pipelines — with the engineering rigour of a software product, not an experiment.
- OpenAI · Claude · Gemini
- GPT-4o
- OpenAI · Claude · Gemini
- Vector search & retrieval
- RAG
- Vector search & retrieval
- Monitored & evaluated
- Prod-ready
- Monitored & evaluated
How we build AI differently
- Evaluate before you buildWe prototype and measure accuracy against your real data before scoping a full build.
- Model-agnostic architectureWe design integrations so you can swap models as the landscape evolves without a rewrite.
- Prompts in version controlPrompt templates are code — versioned, tested and reviewed like everything else.
- Production monitoringLLM outputs are instrumented so you can catch regressions and measure quality over time.
What we build
AI development services
LLM integrations
OpenAI, Claude and Gemini integrations built into your existing product — chat interfaces, document processing, summarisation and generation.
AI agents
Multi-step agents that call tools, search the web, query databases and execute actions — built with structured outputs and deterministic fallbacks.
RAG pipelines
Retrieval-augmented generation systems that let your LLM answer questions grounded in your own documents, using vector search and semantic retrieval.
Private AI deployments
Open-source models (Llama, Mistral) deployed on your own cloud infrastructure — full data sovereignty, no external API calls.
Process
From idea to production AI
- 01
Use-case scoping
We map the problem, the data available, the acceptable accuracy threshold and the cost envelope before recommending an approach.
- 02
Prototype & evaluate
A fast prototype with an evaluation suite so you can see accuracy and cost metrics before committing to a full build.
- 03
Production build
Latency-optimised, cost-monitored and observable — prompt versions in version control, evals in CI.
- 04
Monitor & iterate
LLM outputs drift. We instrument your AI features for production monitoring and improve them with real usage data.
Technologies
AI engineering stack
FAQ
AI development questions
What AI use cases do you build for?
Document analysis and Q&A, customer-facing chatbots, internal knowledge bases (RAG), data extraction pipelines, automated report generation, AI-assisted search, and multi-step AI agents that call tools or APIs. We scope the right approach for each use case rather than defaulting to the newest model.
Which LLMs and AI providers do you work with?
OpenAI (GPT-4o, o3), Anthropic (Claude), Google (Gemini), and open-source models (Llama, Mistral) via Ollama or hosted APIs. We recommend based on cost, latency, and capability requirements for your specific use case.
How do you keep LLM responses accurate and on-topic?
We combine prompt engineering, retrieval-augmented generation (RAG) with vector databases, output validation, and human-in-the-loop review gates depending on the risk level. For high-stakes outputs we add structured output schemas and deterministic post-processing.
Can you integrate AI into our existing product?
Yes. Most of our AI work is integrating LLMs into an existing codebase rather than building from scratch. We design the integration to be testable, replaceable (model-agnostic where possible) and cost-monitored.
How do you handle data privacy when using external LLM APIs?
We design data flows to minimise what reaches external APIs — anonymising or redacting PII before it leaves your infrastructure. For regulated industries we can implement fully private deployments using open-source models on your own cloud.
Ready to ship your AI feature?
Tell us what you want to build and we'll come back with a scoping proposal covering approach, timeline and cost.
Start the conversation