Custom AI & Systems

AI systems built around complex work

Design and build practical AI tools, connected workflows and knowledge systems that fit the way your organisation actually operates.

For expert-led businesses and technical teams, the useful question is not which AI tool to buy. It is what the system should help you do, what context it needs and what must remain under human control.

Discuss your system | See what we build

From AI experiments to useful systems

A chat window can be useful for a quick answer. It becomes more valuable when it has the right context, connects to the right workflow and gives people a dependable way to review and act on the result.

We combine content, technical build, automation and search thinking to create systems that support real work — from research and document handling to marketing operations and decision support.

What we can build around your workflow

The right system may be a focused integration, a custom interface, a structured content workflow or a combination of all three.

• Connected workflows: Connect the tools, forms, websites, dashboards and approval steps around the work you already do.
• Document intelligence: Turn PDFs, research, notes and internal knowledge into structured, searchable and usable inputs.
• Bespoke AI tools: Build focused interfaces, assistants and decision-support tools around a specific job rather than a generic chatbot.
• Workflow automation: Reduce repeated admin across content, research, customer journeys and operational hand-offs, with human checks where they matter.
• Knowledge and content systems: Give AI the context, examples and source material it needs to produce useful, consistent work.
• Controls and reliability: Design testing, review, traceability and sensible escalation into the system before it becomes business-critical.

Build, buy or connect?

Bespoke development is not automatically the right answer. We look at the job first, then decide whether an existing tool, a configured workflow, an integration or a custom build gives you the best balance of speed, control and long-term usefulness.

• Use an existing tool where it already fits the job.
• Configure a workflow when the gap is context, structure or review.
• Connect systems when information is trapped between tools.
• Build bespoke software when the workflow itself is your advantage.

Technical capabilities

Projects can combine AI-assisted interfaces, document and source handling, APIs and third-party integrations, structured content systems, dashboards and review queues, websites and progressive web apps, data organisation, testing, monitoring and governance.

Indicative investment bands

These starting bands help you understand the shape of an engagement. The final scope depends on the workflow, integrations, data and level of support required.

• Discover and design — from £1,500: Map the workflow, assess build-versus-buy options and define a practical technical route.
• Focused workflow build — from £4,000: Configure or build one useful AI-enabled workflow, integration or internal tool with clear review points.
• Connected custom system — from £10,000: Develop a bespoke system spanning interfaces, data, integrations, content workflows and operational controls.
• Ongoing improvement — scoped monthly: Monitor what is working, refine the workflow, maintain content quality and extend the system as needs change.

From useful question to working system

1. Understand the work: Define the people, source material, existing systems and outcome.
2. Choose the right level of build: Compare tools, configuration, integrations and bespoke development.
3. Build around review: Create the workflow, interface and controls, then test representative examples.
4. Improve with evidence: Use real usage, corrections and failure cases to refine the system.

Before you build

Most of what an AI project costs is decided before anyone writes code, in what the work really involves, what source material exists and whether a tool can be trusted with it. These are the checks we run at the start.

• AI readiness checklist — where the work actually sits, who owns it and what being ready means for a small team.
• What data do you need before AI can help — most projects stall on source material rather than models.
• AI due diligence — how to evaluate a tool or supplier before it becomes part of the workflow.

Once a system is live the question changes from whether to build it to whether it still works, which is the subject of our research on production AI reliability.

Supporting work

• Human-Engineered AI — a focused set-up for shaping AI around your workflow, voice, knowledge and judgement.
• HelixScribe case study — how we developed an AI content platform around real editorial workflows and review.
• AI for marketing — content systems, source-led drafting, repurposing and human review.
• LLM reliability research — what changes when AI moves from a demo into production.

What this looks like once it is running

• Keeping a client's voice at the centre of an AI content system
• Monitoring an AI system for drift
• Building an app around a live data feed

More of the work sits on our portfolio.

Have a workflow worth improving?

Tell us what is slow, disconnected or difficult to trust. We’ll help you identify the useful system and the sensible level of investment.

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