AI Development
AI development, from idea to shipped product
We build real AI products, not demos: agents, retrieval (RAG), LLM-powered apps and workflow automation, engineered by the team behind cGen, an AI-native compliance platform.
We build real AI products, not demos: agents, retrieval (RAG), LLM-powered apps and workflow automation, engineered by the team behind cGen, an AI-native compliance platform.
The model is the easy part. Anyone can wire a prompt to an API and get a demo. The 95% that decides whether an AI feature helps your business or embarrasses it, the data work, retrieval, guardrails, evaluation, cost control and knowing where a human must stay in the loop, is real engineering. That is the part we build.
Assistants and chat built on Claude, GPT and similar models, grounded in your data and your rules.
Agents that take real actions across tools and workflows, with humans in the loop where it matters.
Retrieval-augmented generation over your documents so answers are grounded, cited and current.
Adding AI features to an existing web or mobile app, search, drafting, classification, summarisation.
Evaluation, guardrails, monitoring and cost control, so an AI feature survives contact with real users.
An honest read on where AI genuinely helps your business and where it does not, before you spend.
The real problem, the data available, and whether AI is the right tool, said honestly.
A working proof on your data and prompts, evaluated against clear success criteria.
Production engineering, guardrails, evals, monitoring, cost control, around the model.
Deployed into your product or workflow, with the code and accounts in your name.
The mistakes we have seen cost founders the most, so you can avoid them.
A demo on clean data is not a product that survives real users. The production gap, edge cases, bad input, hallucination and cost, is the actual work.
An AI that is confidently wrong destroys trust fast. We ground answers in your data with retrieval and citations, and take hallucination seriously.
Without evaluation and guardrails, an AI feature that works today fails silently tomorrow. We measure and fence it before it ships.
Token and infrastructure costs can quietly balloon. We design for cost control so the feature is affordable at scale, not just in a pilot.
LLM-powered apps and assistants, AI agents that take actions across your tools, retrieval (RAG) over your own documents, and AI features inside an existing web or mobile app, search, drafting, classification and summarisation. We build the production engineering around the model, not just a prompt.
We are model-agnostic and pick per task, typically Claude or GPT-class models for language, with the right retrieval and tooling around them. Because we default to the most capable current models, we can also move as the frontier moves.
We design for your data staying yours, scoped access, clear boundaries on what is sent to a model, and deployment in accounts you own. For sensitive or regulated work we scope data handling explicitly before building.
A chatbot answers; an agent acts, calling tools, updating records, moving a workflow forward, with humans in the loop where the stakes require it. We build both, and advise honestly on which your use case actually needs.
It depends on scope and data readiness, so we usually start with a small prototype on your real data to prove value before committing to a full build. Pricing is fixed by milestone and agreed before we begin.
Yes, that is a related but distinct service. Our GEO and AEO (generative and answer engine optimisation) work helps your brand get cited inside ChatGPT, Perplexity and Google AI Overviews. See our AI visibility page.