AI that earns its keep, built into your stack.

Five patterns we've shipped: AI-drafted customer service (Fun Cases and Twisted Tailor run this, 50–70% of CS hours saved), AI product generators that publish a range in minutes not days, AI image pipelines for standardising lifestyle and product shots, AI competitor and inventory trackers (Sublishop), and internal search across docs/Drive/email. All measurable. No chatbot theatre.

Every output our AI produces crosses a human desk before it goes out. Humans keep the taste. AI does the bulk work. Autonomous AI fails in edge cases and erodes brand voice, so we don't ship it. The win is in human-reviewed throughput, not human-replaced pipelines.

Custom AI builds range from £1k to £15k depending on scope. Small, single-purpose tools (a content drafter, a tagging agent, a simple workflow script) land at the lower end. More involved systems (product generators with Shopify integration, live competitor tracking with alerts, internal search with RAG) land at the top. Ongoing API costs depend on volume, typically £50 to £300 per month per tool.

1–4 weeks for a single tool, 4–8 weeks for larger systems. We scope against a 'ships lean first' principle: smallest version that proves value, live in weeks, then widen based on team feedback.

You do. Every AI system we build lives in your stack, on your infrastructure, owned outright. No vendor lock-in to our platform because we don't have one. If the team wants to take it in-house and extend, the codebase is handed over clean.

Claude for most production work. It's the best available on reasoning and writing, and Anthropic's safety stance fits our 'human-in-the-loop' philosophy. We'll use other models (GPT, Gemini, open-source) where they fit a specific use case better.

Every system we build abstracts the model behind an interface. Swapping to a different provider takes days, not a rebuild. We also version-lock models so your production pipeline doesn't break when Anthropic ships a new release.

Yes. We architect around data minimisation (only the fields the model needs), use enterprise endpoints with no training opt-in, and run everything through a human review gate for customer-facing outputs. Happy to share our compliance playbook during scoping.

Usually, yes. We've taken over AI projects started by other teams, either to fix production issues or to add new capability. Start with an audit of the codebase and infrastructure before committing to scope.