Do we need our own AI model to do this?

Almost never. For the large majority of business use cases, the practical, cost-effective path is building on top of established hosted models (OpenAI, Anthropic, and similar) and grounding them in your data. Training or fine-tuning a custom model is rarely the right first step — we'll tell you honestly on the rare occasion it is.

How do you stop the AI from giving wrong answers?

Two ways, together. Retrieval grounds answers in your actual content rather than the model's memory, and evaluation measures accuracy against real examples so we can see how well it performs and catch regressions. Where the stakes are high, we keep a human in the loop to review before anything is acted on.

Is our data safe if we use these AI providers?

We design for it deliberately — using providers and configurations that don't train on your data, keeping sensitive information out of prompts where it isn't needed, and being clear about exactly what is sent where. Data handling is part of the design, not an afterthought.

How do we know it's actually worth it?

We start small and measurable. Before building anything large we validate that the AI is accurate enough on your real inputs and that the cost per task makes sense. If it doesn't earn its place, we'll say so rather than ship it anyway.

Build

AI & LLM Engineering

Practical AI built on large language models — assistants, retrieval over your own data, document processing, and AI features inside your product, engineered to be accurate, safe, and affordable to run.

Discuss your project

Overview

AI is genuinely useful when it's pointed at a specific, well-defined job — answering questions from your own documents, drafting a first version a human approves, classifying and routing incoming work, or pulling structure out of messy text. It becomes a liability when it's bolted on for its own sake, left free to invent answers, or shipped without a way to measure whether it's actually right.

We build applied AI on current-generation language models — from OpenAI, Anthropic, and comparable providers — with the same engineering discipline as the rest of our work. That means grounding answers in your data with retrieval, evaluating outputs against real examples before launch, keeping a human in the loop where the stakes call for it, and watching cost and latency so the feature is sustainable in production, not just impressive in a demo.

Technologies we use

OpenAIAnthropic ClaudeRAGVector databasespgvectorTypeScriptPython

Business challenges

Problems this solves

An LLM that confidently makes things up

A model answering from its own training will invent plausible, wrong answers. We ground responses in your actual content with retrieval, cite sources, and constrain the model so it says 'I don't know' instead of guessing.

AI that can't be measured or trusted

Without evaluation you're shipping vibes. We build test sets from real examples and measure accuracy, so you know how well it works — and notice when a prompt or model change makes it worse.

A prototype that's too slow or too expensive to ship

Naïve LLM features can be sluggish and costly at scale. We control cost and latency with the right model for each step, caching, and retrieval — so the economics work in production, not just in a demo.

How we approach it

Our process

Find the job worth automating

We start from a concrete task and honest expectations — where AI genuinely helps, where it doesn't, and what 'good enough' means for this use case.

Ground it in your data and evaluate

Retrieval over your own content, prompts designed against real inputs, and an evaluation set that tells us — and you — how accurate it actually is before anyone relies on it.

Ship with guardrails and monitoring

Human-in-the-loop where it matters, fallbacks when the model is unsure, and monitoring of quality, cost, and latency so the feature stays dependable as usage grows.

What's included

Key features

Retrieval-augmented generation (RAG) over your data
AI assistants & chatbots grounded in your content
Document intelligence — extraction, classification, summarisation
LLM features inside your existing product
Evaluation, guardrails & human-in-the-loop review
Cost, latency & quality monitoring

Benefits

What you get

AI grounded in your data, not guessing from training
Measured accuracy instead of hopeful hand-waving
Costs and latency that hold up in production
A clear, honest view of where AI helps and where it doesn't

In practice

Example use cases

A support assistant that answers from your own documentation and help centre
A pipeline that reads incoming documents and extracts structured data
An internal tool that drafts responses a human reviews and approves

FAQ