Practical AI wired into the tools you already run: assistants that answer customers instantly, pipelines that draft, route, summarise and file, and product features powered by the latest models — built by developers who integrate AI properly, with guardrails and audit trails.
What you get. Practical AI wired into the tools you already run: assistants that answer customers instantly, pipelines that draft, route, summarise and file, and product features powered by the latest models — built by developers who integrate AI properly, with guardrails and audit trails.
Who it’s for. Teams drowning in repetitive digital work — enquiries answered at 9am that arrived at 9pm, data typed twice, documents summarised by hand — and product owners who know AI belongs in their roadmap but not as a bolted-on chatbot.
The wins are boring and enormous: first-line enquiries answered instantly and accurately from your own documentation; documents read, summarised, classified and filed; data extracted from PDFs and emails instead of retyped; drafts prepared for a human to approve rather than written from scratch.
We look for tasks that are high-volume, low-judgement and currently done by a person at a keyboard. Those are the ones where automation returns hours every week — and where the hours can be counted against a baseline measured before we started.
An assistant that confidently invents an answer is worse than no assistant. So retrieval is grounded in your own content, responses cite what they drew on, low confidence hands off to a human rather than guessing, and every interaction is logged so you can audit what was said on your behalf.
On data: we use business-tier APIs where inputs are excluded from training, keep personal data out of prompts wherever the task allows, and document exactly what leaves your infrastructure and where it goes. That documentation is part of the deliverable, not something you have to ask for.
A chat bubble floating in the corner of a website is the least interesting thing AI can do. The valuable version is wired into the systems that already hold your work — the CRM, the inbox, the document store, the platform your team lives in all day — so the output lands where the next step happens rather than in a chat window somebody has to copy out of.
Because we build the platforms too, that integration is a build task rather than a negotiation between two vendors.
Models move fast. We build behind an abstraction so the provider is a configuration decision rather than a rewrite, evaluate candidates against your actual task instead of a public leaderboard, and put cheap models on the easy steps with strong ones only where they earn it.
That keeps running cost visible, and keeps you free to switch when something better ships — which it will.
With a task, not a technology. The free Digital Scan includes an AI-readiness module, and a 2-day AI Sprint produces an opportunity map plus a working proof of concept on one real workflow — so you decide based on something running, not a slide.
No. We use business-tier APIs where inputs are excluded from training, keep personal data out of prompts where the task allows it, and document exactly what leaves your infrastructure and where it goes.
Answers are grounded in your own content rather than the model’s memory, responses cite what they drew on, low-confidence cases hand off to a human instead of guessing, and every interaction is logged so you can audit it.
Whichever fits the task. We evaluate candidates against your actual workflow rather than a leaderboard, and build behind an abstraction so switching provider later is configuration rather than a rewrite.
We measure the baseline before the build — how long the task takes now, how often it happens, who does it — and report against it afterwards. Hours back per week is the number we hold ourselves to.
Your website earns attention. Your marketing turns attention into traffic you can trace. Your platform turns traffic into customers and runs the operation behind them. Your AI automates the repetitive work and feeds what it learns back into all three. See how the whole loop closes.
Score your digital estate across all four disciplines and see — in your own data — what to fix first.