RAG over your data
Chat and search grounded in your documents, so answers are accurate and cite their sources.
Services
Generative AI Integration
LLMs and agents inside your actual workflows.
LangChainOpenAIAnthropic ClaudePinecone
LLMs and agents inside your actual workflows.
Generative AI is easy to demo and hard to ship. We specialise in the hard part: taking LLMs from an impressive prototype to a reliable production feature with the cost controls, guardrails and evaluation that real users demand.
We build retrieval-augmented generation (RAG) pipelines that ground answers in your own documents, AI copilots that live inside your product, and workflow assistants that save your team hours a week. Our stack centres on LangChain with OpenAI and Anthropic Claude models, plus vector databases like Pinecone for retrieval.
Every build includes the parts that separate a toy from a tool: prompt evaluation, hallucination guardrails, token-cost monitoring, and fallbacks so the experience stays trustworthy and affordable at scale.
What we deliver
The core capabilities inside our generative ai integration engagements.
AI copilots & assistants
In-product assistants that help users complete real tasks, not just answer questions.
LLM app development
End-to-end GenAI features built with LangChain, OpenAI and Claude.
Guardrails & evaluation
Prompt testing, output validation and hallucination controls before anything reaches users.
Cost & token optimisation
Caching, model routing and monitoring that keep GenAI affordable at scale.
Vector search
Embedding pipelines and vector databases (Pinecone and others) tuned for relevant retrieval.
Who it's for
Is this the right fit?
We do our best work when the problem is a genuine fit. Generative AI Integration typically makes sense for:
Companies sitting on documents or knowledge users struggle to search
Product teams that want an AI copilot inside their app
Support teams looking to deflect repetitive questions with grounded answers
Businesses with a GenAI prototype that needs to become production-ready
FAQ
Common questions
How do you stop the AI from hallucinating?
We ground responses in your own data with RAG, add output validation and guardrails, and evaluate prompts against test cases before launch. No system is perfect, but these controls make it trustworthy for real use.
Which LLMs do you build with?
Primarily OpenAI and Anthropic Claude models, orchestrated with LangChain. We route between models based on cost and quality for each task.
How do you keep GenAI costs under control?
Through caching, smart model routing (using cheaper models where quality allows), and token-level cost monitoring, so spend stays predictable as usage grows.
Let's build
Ready to start?
Tell us what you're trying to build. We'll tell you honestly whether we're the right team — and how we'd approach it.
Talk to us