2026AI engineering, proven in production
Seven products of our own are live right now — voice agents, retrieval, generation and tool use, running under real traffic for real customers.
Seven products of our own are live right now — voice agents, retrieval, generation and tool use, running under real traffic for real customers.
products in production
Designed, shipped and operated by us — including the on-call rota.
practices, one team
The AI work plus the engineering that keeps it alive after launch.
client builds
Auctions, luxury retail, logistics, healthcare and trade data.
faster data processing
At EximFetch — the client's figure, not ours.
{ what we ship }
Not case studies from a client engagement — software we designed, shipped and now operate, including the on-call rota and the inference bill.
{ infusing AI into a business }
01
We map the process first — where the hours go, where the errors start, and what "better" would actually measure. The model gets chosen last, and sometimes the answer is a query and a cron job.
02
Retrieval over your documents and live systems, answers with citations you can check, and an evaluation suite that runs in CI — so a regression is caught before a customer sees it.
03
Cost ceilings, provider fallbacks, latency budgets and observability. The unglamorous layer that decides whether an AI feature survives its first Monday morning.
{ what we do for clients }
The AI work, and the engineering around it that decides whether AI survives contact with production. Every one of these is a service we deliver end to end.
Fine-tuning
Model selection, LoRA/QLoRA fine-tuning, distillation, structured outputs and the evaluation harness that proves it works.
Read moreHybrid retrieval
Hybrid retrieval, re-ranking, real-time indexing and permission-aware answers with citations you can defend.
Read moreTool calling
Autonomous agents that plan, call real tools against real systems, and know when to hand control back to a person.
Read moreContent generation
Content, code and creative generation systems built around your data, your brand voice and your review process.
Read moreSpeech-to-speech
Speech-to-speech agents that answer the phone, look things up in your systems and hand off cleanly to a human.
Read moreDocument AI
Document intelligence, OCR at scale, detection and tracking, video understanding and generative visual pipelines.
Read moreInference
Inference infrastructure, semantic caching, provider fallbacks, evals in CI and the cost observability that keeps AI viable.
Read moreEnterprise
Knowledge-based and decision-making agents deployed inside enterprise workflows, security boundaries and approval chains.
Read moreRPA
AI, machine learning and workflow optimisation applied to the repetitive processes that quietly consume your operating budget.
Read moren8n
Intelligent workflows that remove repetitive tasks and integrate cleanly across the systems you already run.
Read moreAWS
Migration, modernisation and cloud-native engineering across AWS, Azure and GCP — with cost optimisation that holds.
Read moreNext.js
Web, mobile and enterprise applications engineered for performance, scale and integration into your existing stack.
Read morePlaywright
End-to-end QA, automated testing and AI-assisted security review so what you ship is reliable and defensible.
Read moreShopify
Personalised, data-driven commerce with AI recommendations, smart inventory and frictionless checkout.
Read moreMost AI projects die in the gap between a promising demo and something the business can rely on. The sequence below exists to close that gap early rather than discover it at launch.
We start with what is not working and what better would measure. Sometimes the answer is a query and a cron job rather than a model — we will say so before you spend on inference.
One complete path, end to end, against your real data with a measured baseline. Integration risk surfaces in week two instead of month five.
Evaluation suites in CI, provider fallbacks, cost ceilings and observability. The unglamorous layer that decides whether an AI feature survives real traffic.
We operate it alongside you, or hand it to your team with the practices to maintain it — mainstream stacks, documented decisions and a pairing period.
{ selected work }
Plenty of projects look good in a screenshot. These are the ones where the client measured the difference afterwards.
