We don't sell autonomous AI agents. We walk businesses through the stages that lead there.
Most teams aren't where the AI hype is. They're a few stages back, working through inboxes, spreadsheets, chat, portals and manual hand-offs. We design and ship the systems that move them one stage forward at a time: structured workflows first, AI where it earns its place, automation as the integrations land. Autonomous comes last, not first.
The seven stages between manual reality and an autonomous system.
Every operations-heavy business sits somewhere on this journey. The work is to move forward by one stage at a time. The risk is in trying to skip to the end. Most realistic clients enter around stage three.
Prototype first. Then build only what proves itself.
Most AI projects start with too much talk and too little evidence. We work from the real workflow: the conversations, hand-offs, spreadsheets, emails, screenshots, edge cases, hidden rules and pain points operators deal with every day. The more reality we can see, the better the prototype. We work best where the workflow is repeated, operationally important, and likely to exist in more than one team or business unit. That's where a prototype turns into a system worth running for years.
Find the workflow
We map the workflow alongside the people who actually run it. The spreadsheets and chat threads are easy to capture. The judgement, edge cases and rules that live in people's heads only come out in conversation.
Make the future visible
We turn that real workflow into a working prototype of the next-stage system, so the team can see, test and challenge it before production work begins.
Build only what proves value
If the prototype lands, we move into a fixed-scope build. If not, you have learned something useful before committing to a full programme.
Three ways we work, priced for what they are.
Most engagements start with a prototype sprint. Some move into a fixed-scope build. A few stay on as managed AI operations. Each model has a defined shape so you know what you're committing to. We work best on repeated, high-value workflows; less so on one-off internal automations or systems that only make sense because of one team's historical quirks.
Workflow Prototype Sprint
Two weeks · one workflow · fixed priceFor teams that have a real operational problem but need to see what's possible before committing to a build. We focus on one workflow, build a working prototype of the next-stage system, and recommend the path forward.
- Workflow map of the current state
- Working prototype of the next-stage system
- Buildable architecture + integration notes
- Go / no-go recommendation
Stage Upgrade Build
Fixed scope · 6–12 weeks · agreed milestonesFor prototypes that prove value. We turn the prototype into a real operational system: proper data model, deterministic rules, workflow states, the right level of AI, and integrations into the systems already in use.
- A purpose-built workflow tool that replaces inboxes, spreadsheets, chat and manual hand-offs. Your team still leads; software handles the plumbing.
- Deterministic core + thin AI layer where it earns its place
- Integrations with the systems the workflow lives in today
- Initial deployment under human oversight
Managed AI Operations
Monthly retainer · senior-ledFor systems that need to keep improving after launch. We monitor, maintain and tune the workflow, prompts, integrations and feedback loops so the system remains useful as the operation changes.
- Hosting, monitoring and reliability
- Prompt and workflow tuning as patterns emerge
- Integration and tool updates as upstream systems change
- Quarterly review of value delivered and next-stage roadmap
Our client work informs our own products.
Studio work and product work feed each other. The studio finds repeatable workflow patterns; the products turn those patterns into assets that stand on their own.
Stellar Dental Notes
A clinical documentation system for dental teams, built from the same belief: structured workflow first, AI where it genuinely reduces burden. Voice in, structured notes out.
Visit stellarnotes.co.uk →Three principles that decide what we build and what we don't.
Deterministic core, thin AI layer
The decisions that matter (pricing, matching, selections) are software, not AI. AI sits as a thin layer for the parts where it earns its place: parsing messy input, drafting narrative, surfacing patterns. Pricing decisions left to a language model will go wrong.
One stage at a time
We don't skip ahead. If the workflow still runs through manual workarounds, the next move is a human-led workflow with proper tooling, not autonomous agents. The stages compound: each one validates the next. Going straight to "AI agents" without the stages between is how pilots fail.
Accuracy before production
A working prototype must be right before any production work begins. We ship narrow, accurate systems first, then widen and harden. Auditing, supportability and admin surfaces are a deliberate later stage, not the first thing built.
Senior, selective, the right shape for the work.
The shape of the studio is part of the offer. Senior people doing the work directly, with a network we scale into when a project needs it.
London
Working hours-aligned with UK and EU clients. Embedded with the operators where the work happens. We don't deliver from arm's length.
Delivery partners across the UK & EU
Trusted partners we bring in when a project needs engineering bandwidth beyond the core. Senior, vetted, working alongside the lead. Not a delivery layer.
~3 projects in flight at a time
Selective by design. Each project is run end-to-end by a senior lead. You talk to the people building it. We don't take work we can't run properly.