Accelerate · AI Integration
Point AI at the thing your team is waiting on.
LLM features, agents and background automation, built where somebody is currently waiting on somebody else.
Most AI projects fail in one of two ways: they chase the technology instead of a bottleneck, or they stop at a demo that never survives contact with real data and real users. The model capabilities are there. The gap is engineering judgement about where to apply them, and the discipline to ship something reliable.
Who calls me
- Companies with repetitive, knowledge-heavy work that is slowing the team down
- Product teams that want AI features customers will use twice
- Founders who know AI matters but don't know where it fits in their business
- Teams that tried a generic chatbot and were underwhelmed
How I'd work on it
- 01
We start with your operations, not the model. Where does work pile up? Where do people wait on other people? That's where automation pays for itself.
- 02
We prototype fast — often within days — against your real data, so you can see what's possible before committing.
- 03
We build for reliability: evaluation sets, guardrails, fallbacks, observability, and cost controls. A system that works 80% of the time is a system nobody trusts.
- 04
We design for asynchrony. The best automations run in the background and surface results when they're ready — so your team stops waiting.
What you get
AI opportunity review
A short, concrete assessment of where AI will remove bottlenecks in your business — ranked by impact and effort, with the first build scoped.
LLM product features
Search, summarisation, extraction, generation, copilots, and conversational interfaces built into your product with proper evals and guardrails.
Agents & automation
Multi-step workflows that read, decide, and act across your tools — with human checkpoints where they belong and full traceability.
Team enablement
We show your engineers the workflow We actually use, so the speed doesn't leave when We do.
What it costs, and how it starts
Every engagement starts with a short fixed-scope step, so we both find out what this is like before either of us commits to more. You get the same number anyone with the same scope would get.
01
Review
One to two weeks. Find the highest-value opportunity and scope the first build.
02
Build
Two to eight weeks. Ship the first automation or feature to production.
03
Expand
Ongoing. More workflows, more reliability, and a team that can run it.
FAQ
AI Integration: what people ask
Which AI models and providers do you work with?
Claude and GPT for most things, open-weight models when privacy or cost demands it. Model choice sits behind an interface, because a better one ships roughly every month and that shouldn't be a rewrite.
How do you handle data privacy?
Your data stays in your accounts. We use providers with no-training, enterprise data agreements, apply least-privilege access, and can deploy self-hosted or region-locked models where regulation requires.
Is AI actually reliable enough for business processes?
For the right processes, yes, when it's engineered for it. That means evaluation sets built from your real cases, confidence thresholds, human review for high-stakes steps, and monitoring. We won't ship an automation We wouldn't trust myself.
We already use ChatGPT. What's different?
Chat tools help individuals; integrated AI helps the whole system. The value comes from connecting models to your data and your tools so work happens without someone copy-pasting into a chat window.
Name the thing your team does by hand every day.
That's usually where the first build goes. Tell us what it is and We'll tell you whether it's worth automating.