AI & Agentic Development
We design and ship production AI: agentic systems, RAG assistants, and LLM features that plug into the software you already run.
Most AI projects stall in the demo. A prototype answers three questions well, then meets real users, real data and real edge cases — and nobody can say why it failed or how to fix it. We build the other kind: AI systems with evaluation harnesses, tracing, fallbacks and cost ceilings, deployed into your existing stack and owned by your team.
Who this is for
Founders with an AI product idea that needs to survive contact with paying users. Operations leaders drowning in repetitive judgement work. Engineering teams that have proved an LLM feature in a notebook and now need it to run reliably at scale, in production, on a budget.
Problems we solve
- Demos that do not survive production. No evaluation set, no regression tests, so every prompt change is a gamble.
- Confident wrong answers. A model with no grounding will invent a policy, a price or a part number rather than say it does not know.
- Runaway token spend. Unbounded context and retry loops that quietly cost more than the headcount they replaced.
- Nothing to debug. When an agent takes the wrong action, there is no trace of what it saw, what it decided, or which tool it called.
- Data you cannot send to a vendor. Contracts, patient records or source code that rule out a public API entirely.
What we build
- Agentic systems that plan, call tools, and act against your APIs and databases — with human approval on anything consequential
- Retrieval-augmented assistants grounded in your documents, so answers cite a source instead of guessing
- Conversational and voice interfaces on the channels your customers already use
- LLM features embedded in existing products: drafting, classification, extraction, summarisation, search
- Evaluation suites, tracing and dashboards, so quality is a number you can watch rather than a feeling
How we work
We start with the decision, not the model. What judgement is being made today, by whom, using what information, and what does a wrong answer cost? That determines whether you need retrieval, an agent, a fine-tune, or simply a better-designed form. We then build the smallest thing that answers the question end to end, put an evaluation set behind it, and iterate on measured accuracy. Model choice comes last, because it is the easiest part to change.
Technologies we use
OpenAI, Anthropic Claude, Google Gemini and self-hosted Ollama for generation; LangChain and LlamaIndex for orchestration; Qdrant, pgvector and Pinecone for vector search; the Model Context Protocol for tool access; Redis for memory and rate limiting; Laravel, Node.js, Python and Next.js for the application around it. We are deliberately model-agnostic — the abstraction sits in your codebase, so switching providers is a config change, not a rewrite.
Business benefits
- Skilled staff stop spending their day on work a system can do
- Answers stay consistent whoever asks and whenever they ask
- Institutional knowledge stays searchable when people leave
- Support and research capacity scales without proportional hiring
- Self-hosted options keep regulated data inside your own network
Where it pays off
- Support teams answering the same policy question hundreds of times a week
- Sales teams qualifying and routing inbound leads by hand
- Finance and legal teams re-keying data out of PDFs
- Staff hunting through wikis, drives and inboxes for one authoritative answer
- Products that need search, drafting or classification built into the core experience
Common questions
Do we have to send our data to OpenAI?
No. We deploy self-hosted models with Ollama and a local vector database when data cannot leave your infrastructure. It costs more in engineering and hardware, and the quality ceiling is lower than a frontier model, so we will tell you honestly whether your use case can afford that trade.
How do you stop it making things up?
Grounding and scope. Retrieval ties every answer to a retrieved passage, prompts constrain the model to that context, and the system is built to say it does not know rather than improvise. We measure this against a fixed evaluation set before launch, not after.
What does it cost to run?
We model token spend per interaction during the design phase and set hard ceilings, caching and fallbacks to cheaper models. You get a projected monthly cost before we build, not a surprise invoice after.
Can it work with our existing systems?
That is the point. An agent without tool access is a chatbot. We integrate with CRMs, ERPs, ticketing, email, Slack, WhatsApp and your own databases so the system can actually complete work.
Have an AI idea that needs to reach production? Tell us the decision you want automated and we will tell you honestly whether AI is the right tool for it.