AI & LLM development
We build AI into the business, end to end.
From LLM integrations and RAG knowledge bases to autonomous agents and full custom software — production-grade AI systems that solve a real business problem, not prototypes that break at scale.
Engineering stack
The tools we actually build on.
Frontier models, orchestration frameworks and the production infrastructure underneath — chosen per job rather than by habit.
- Claude
- GPT-4o
- Gemini
- Llama 3
- Mistral
- LangChain
- LlamaIndex
- LangGraph
- CrewAI
- Pinecone
- Weaviate
- Chroma
- pgvector
- Qdrant
- Python
- TypeScript
- FastAPI
- Node.js
- Docker
- AWS / GCP / Azure
What we build
Where AI actually earns its keep.
Every system we build is production-grade, documented and designed for long-term reliability.
LLM application development
Applications built on Claude, GPT-4o, Gemini or open-source Llama — well past simple API calls. Context architecture, prompt engineering, structured outputs, tool use, multi-modal inputs, streaming interfaces, evaluation and fallback strategies, and token-cost optimisation.
Work that needed a specialist runs on demand
RAG systems & knowledge bases
Retrieval-augmented generation connects the model to your own data, so it retrieves exact information from your documents and databases instead of guessing. Ingestion and chunking, embeddings, vector store, hybrid semantic and keyword search, reranking, and the generation layer.
An assistant that genuinely knows your business
AI agents & multi-agent systems
Autonomous agents that plan, use tools and complete multi-step work, built on LangChain, LangGraph or CrewAI, with human-in-the-loop checkpoints where the stakes justify them.
Processes that ran on people now run on demand
Custom AI software
Full-stack applications, SaaS products and enterprise systems with AI capability built in from the architecture up rather than bolted on afterwards.
A product, not an integration
AI chatbots & assistants
LLM-powered assistants for web, WhatsApp and Slack, with CRM integration, lead qualification and clean handoff to a human at the right moment.
Quoted per build — scope decides the number
Predictive analytics & ML
Forecasting, scoring and classification models trained on your own operational data, deployed where the decision actually gets made rather than in a notebook nobody opens.
Decisions made on evidence rather than instinct
Where it applies
Sectors we have built for.
- Healthcare
- Financial services
- Legal
- E-commerce
- Real estate
- Education
- Logistics
- Marketing & media
How we build
What exists at the end of each stage.
01
Discovery
We establish what the system has to do, what it must never do, and how success will be measured — before any code is written.
02
Architecture
Model choice, data flow, retrieval strategy, evaluation approach and cost envelope, written down and agreed. Fixed-price scope comes out of this stage.
03
Prototype
A working slice against your real data, so the approach is proven — or corrected — while changing direction is still cheap.
04
Production build
Hardening, evaluation harness, fallbacks, monitoring, cost controls and documentation. The difference between a demo and something you can depend on.
05
Deploy & optimise
Live, monitored, and tuned against real usage — accuracy, latency and token cost — rather than handed over and forgotten.
FAQ
Common questions.
Which model do you use?
Whichever fits the job. Claude, GPT-4o, Gemini and open-source Llama all have different strengths in reasoning, cost, latency and context length, and the right answer changes per use case — and over time. Systems are built so the model can be swapped without rewriting the application around it.
How do you stop it making things up?
Mostly by not asking it to remember. RAG grounds answers in your actual documents and data, so the model retrieves rather than recalls. On top of that: structured outputs, an evaluation harness that tests against known-correct answers, and explicit fallbacks when confidence is low.
Is our data used to train anyone’s model?
No. We use provider APIs under terms that exclude your data from training, and the retrieval layer keeps your documents in infrastructure you control. Data handling is specified in writing before a build starts.
What does it cost to run once it’s live?
Token cost, hosting and any vector database. We model that during architecture and design for it — caching, context trimming and model routing all materially change the running cost, and it is cheaper to plan for than to retrofit.
Next step
Ready to build something intelligent?
Bring the problem, not a spec. We will tell you whether AI is the right tool for it — including when it isn’t.