Services / AI & Automation
AI that ships, not AI that demos.
Most AI features look great in a demo and fall apart in production. Cost spikes, latency drift, hallucinations on the one example the demo never tried. We build AI features that survive real users, real data, and real edge cases — because we build them the same way we build the rest of the software.
What we mean by AI & Automation
AI that ships, not AI that demos.
- AI-powered features in custom software (search, summarisation, generation)
- Workflow automation and document processing
- Internal copilots for operations teams
- LLM integrations with evals and guardrails
- Cost, latency, and failure-mode design
What you get
Outcomes, not deliverables.
Real AI features in shipped products, with evals and guardrails behind them
Honest answers about what AI changes — and what it doesn't
Cost, latency, and failure modes designed in, not bolted on later
How we approach it
4 stages, in this order.
- 01
Frame the problem
Most "AI projects" are really product projects with an AI ingredient. We start by working out what the right answer looks like — and whether AI is genuinely the way to get there.
- 02
Prototype against real data
Demos on cherry-picked examples are easy. We test against your data, your edge cases, and the volume you'll actually run at.
- 03
Build the production scaffolding
Evals, guardrails, fallbacks, observability, cost monitoring. The work that makes AI features reliable instead of impressive.
- 04
Ship and tune
Real users, real traffic, real failure modes. Tune the prompts, the model choice, and the guardrails until the production numbers match the prototype.
How we make sure the AI works in production
AI accelerates the team. Senior experience still decides what to build.
This is the one page where AI is the offer rather than the multiplier. Most AI demos hide the hard parts — what happens when the model is wrong, when costs spike, when a user finds the edge case. Production AI lives or dies on the scaffolding around the model, not the model itself.
- Evals against real examples, run continuously — not a one-shot benchmark
- Guardrails and fallbacks for the predictable failure modes
- Cost and latency budgets monitored in production, not estimated in a deck
Proof
Recent work in this area.
Questions we hear a lot
FAQs.
- Which models do you use?
- Whichever fits the job and the budget. We're not loyal to a vendor. The right answer is often a small fast model for most traffic with a larger one as a fallback — but we'll work that out from your data, not a pitch deck.
- How do you handle hallucinations and wrong answers?
- Honestly. We design for the failure modes from week one — what does the system do when the model is wrong, and how does the user know? Guardrails, evals, and a fallback path are part of the build, not an afterthought.
- What about data privacy and our customer information?
- Depends on the model and the deployment. We can run open models in your VPC, use enterprise tiers with zero-retention guarantees, or use lighter on-device models. The discovery call covers what your compliance position needs.
- How do you keep AI costs under control?
- Cost budgets monitored in production from day one. Caching where it makes sense, smaller models where they're enough, and clear visibility so you're never surprised by an invoice.
- Can you add AI to our existing product?
- Often that's exactly the brief. We integrate into your existing stack, scope the AI work tight, and ship the first feature in weeks rather than rebuilding the product.
Got a build in mind? Let's have a conversation.
Liverpool, UK. Available across the UK and remote.