Intelligent features powered by large language models.
We integrate AI into your existing products and workflows — from retrieval-augmented generation and semantic search to fully autonomous copilots. Every implementation is grounded in your data, evaluated rigorously and designed to deliver measurable business value.
Built for performance, scale and clarity.
LLM integration & prompt engineering
We connect your application to OpenAI, Anthropic, Mistral or open-source models via secure APIs. Our prompt engineering ensures reliable, brand-consistent outputs with guard rails that prevent hallucination and off-topic responses.
Retrieval-augmented generation (RAG)
We build RAG pipelines that ground LLM responses in your own data — documents, knowledge bases, product catalogues or internal wikis — using vector databases like Pinecone, Weaviate or pgvector for fast, accurate retrieval.
AI copilots & assistants
Custom copilot interfaces that help your users draft content, analyse data, navigate complex workflows or get instant answers. We design conversational UX that feels natural and integrates seamlessly into your existing product.
Embeddings & semantic search
Replace keyword search with semantic understanding. We generate and index embeddings from your content so users find what they need even when they do not use the exact right words.
Workflow automation with AI agents
We build autonomous AI agents that classify support tickets, extract data from documents, generate reports and trigger actions across your tool stack — reducing manual work and speeding up operations.
Evaluation, monitoring & safety
Every AI feature ships with automated evaluation suites, output monitoring, cost tracking and content moderation. We measure accuracy, latency and user satisfaction so you can iterate with confidence.
A structured approach, every time.
AI opportunity assessment
We audit your product, data and workflows to identify the highest-impact AI use cases. You get a prioritised roadmap with estimated ROI, data readiness requirements and risk considerations for each opportunity.
Data preparation & architecture
We design the data pipeline — ingestion, chunking, embedding and indexing — and select the right models and infrastructure. A proof-of-concept validates feasibility before full development begins.
Build, evaluate & iterate
We develop the AI features in sprints, running automated evaluations after every change. Prompt tuning, retrieval optimisation and UX refinements happen continuously until accuracy and user experience targets are met.
Deploy, monitor & improve
We deploy to production with observability dashboards that track latency, cost, accuracy and user feedback. Post-launch we fine-tune prompts, update retrieval sources and add new capabilities based on real-world usage patterns.
Common questions.
How much does AI integration cost for a UK business?+
A focused AI feature such as intelligent search or document summarisation typically costs between £10,000 and £30,000. A full RAG pipeline with a custom copilot interface ranges from £30,000 to £80,000+. Costs depend on data complexity, model selection and the level of evaluation and safety tooling required. We provide a detailed quote after an initial assessment.
How long does it take to integrate AI into an existing product?+
A proof-of-concept can be ready in 2–4 weeks. A production-grade AI feature with evaluation, monitoring and safety guard rails typically takes 8–14 weeks. More complex multi-agent systems or large-scale RAG deployments may take 16–24 weeks. We recommend starting with a focused use case and expanding from there.
Is my company data safe when using AI models?+
Yes. We use enterprise API agreements that guarantee your data is not used for model training. All data is encrypted in transit and at rest, and we can deploy within specific regions to meet data residency requirements. For highly sensitive use cases, we can run open-source models on your own infrastructure so data never leaves your environment.
Which AI models do you work with?+
We work with all leading providers including OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini) and open-source models such as Llama and Mistral. We select the model that best balances accuracy, latency, cost and privacy requirements for your specific use case, and we design for model-agnosticism so you can switch providers as the market evolves.
What is retrieval-augmented generation (RAG) and when do I need it?+
RAG is a technique that feeds relevant chunks of your own data to a large language model alongside the user's question, so the model's response is grounded in factual, up-to-date information rather than its general training data. You need RAG whenever you want an AI feature to answer questions about your specific products, documentation, policies or internal knowledge.
How do you measure whether an AI feature is working well?+
We set up automated evaluation pipelines that measure factual accuracy, relevance, latency and cost per query against a curated test set. In production we track user satisfaction signals such as thumbs-up/down ratings, follow-up question rates and task completion rates. These metrics feed into regular optimisation cycles.
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