AI that earns its place in the codebase.
Applied AI for the workflows worth automating — built in code, in your stack, debuggable, and accountable to a real metric.
The hard part isn't the model call. It's deciding what's worth replacing with software.
Most 'AI for [whatever]' projects fail not because the model was wrong, but because nobody asked the harder question first: which manual workflow is worth automating, and which one will quietly break the business if a model gets it wrong.
We start there. A short discovery against your real workflows tells us where AI removes hours and where it just adds risk. Then we build only what passed the test.
The work, specifically.
Applied AI in your stack
Direct API calls to the model best suited to the job — Claude, GPT, Gemini, open-weights, whatever fits. No no-code builder in the middle. Prompts versioned like the rest of the codebase. Outputs you can debug. Costs you can predict.
Content & creative pipelines
We've replaced manual content workflows with pipelines that ghostwriters and marketers actually trust. Briefs in, drafts out, edits round-tripped. The kind of system that takes a week of weekly content production down to an afternoon.
Campaign creator, end-to-end
Drafts a launch — angles, copy, ad variations, landing-page outline — from a single brief. Built so the marketer is editing, not staring at a blank page.
Multi-aspect ad resizer
PPC platforms demand half a dozen aspect ratios per creative. We built a pipeline that takes one approved ad and produces all the variants — preserving brand, copy and focal point — replacing hours of manual reformatting per campaign.
RAG over your own knowledge
Internal tooling, doc search, support deflection — built on top of the content your team already produces. Indexed properly, evaluated against real questions, and wired into the tools your team already lives in.
Pipeline optimisation
The unglamorous wins. Lead enrichment, data extraction, classification, summarisation — the workflows where the real time goes and the case study never gets written.
Discovery, prototype, production. In that order.
Discovery (1–2 weeks): we sit with the workflow, watch where time actually goes, and decide together what's worth a prototype and what isn't.
Prototype (2–4 weeks): a working pipeline against real inputs in your stack. Measurable, testable, throwaway-able if it doesn't earn its keep.
Production (ongoing): once it works, we wire in monitoring, evals, cost guardrails and the iteration loop — because a model that worked on Monday isn't guaranteed to work on Friday.
Where we've done this.
We're shipping AI work for ourselves and for partner engagements before publishing the case studies. Talk to us — we'll walk you through what's running today, on a call.
Got a workflow you suspect is worth automating?
Tell us what it is. The first conversation is free, and you'll leave it with a clearer answer on whether it's worth the build — even if the answer is no.
Let's talk!