We build AI agents that do real work.
Not strategy decks. Not proof-of-concepts that never ship. We design, build, and deploy agentic systems — agents that automate workflows, reason over documents, orchestrate multi-step processes, and integrate into the tools your teams actually use.
Capabilities
What we build
We focus on agentic systems — AI that takes actions, not just generates text. What that looks like depends on where you are and what needs to change.
Document intelligence
Agents that read, classify, extract, and act on unstructured documents at scale. Contracts, reports, intake forms, regulatory submissions.
Decision support pipelines
Systems that surface relevant context, flag risk, and structure recommendations for human review — without burying your analysts in noise.
Multi-step orchestration
Agents that chain tools: search, write, validate, route, escalate. Built with proper state management, retries, and observability.
RAG systems
Retrieval-augmented generation over your own knowledge base. Accurate, auditable, and maintainable — not just impressive in a demo.
Evaluation and safety layers
The infrastructure to know whether your AI is actually working: automated evals, human review queues, drift detection, guardrails.
Internal AI tooling
Custom interfaces and workflows for your internal teams. Built around the people who have to use it, not around what the model can do.
Our approach
What makes us different
End-to-end ownership
We're not advisors who hand off at the prototype. We design, build, deploy, and stabilise. If it doesn't reach production, it doesn't count.
Evaluation before everything
We define what success looks like before we write a line of code. Evals aren't an afterthought — they're how we know we're building the right thing.
Change management is not optional
An agent running in a team's workflow is a change problem first. We bring the people work in from day one.
We don't outsource the hard parts
Safety, observability, integration with your existing systems, data handling. These aren't someone else's problem — they're ours.
The business case
Where the economics shift
Illustrative — based on patterns we see across engagements, not guarantees.
| Workflow | Before | After |
|---|---|---|
| Document review | 2 FTE, 4-week turnaround | Agent handles 90% of volume in hours; humans review exceptions |
| Knowledge retrieval | Analysts spending 40% of time searching internal systems | RAG layer surfaces the right context on demand |
| Compliance reporting | Quarterly sprint to pull data, format, and chase sign-off | Automated pipeline; human review on exceptions only |
Honest constraints
What we won't do
- Build chatbot wrappers and call it AI strategy
- Deploy agents into production without evaluation infrastructure
- Create vendor lock-in by burying everything in a single cloud AI service
- Automate a broken process — we'll fix the process first, or tell you to
- Reach for fine-tuning when RAG or careful prompting is sufficient
- Promise transformation without addressing the people and process layer
Engagement shapes
How we engage
Discovery sprint
2–3 weeksWe audit your workflows, identify the highest-value agent opportunities, and define build scope and risk boundaries. Output: a prioritised roadmap and a go/no-go decision you can trust.
Production build
8–16 weeksWe design, build, and ship a production-grade agentic system. Includes evaluation framework, observability, integration, and a structured handover to your team.
Embedded AI team
OngoingA senior AI engineer and delivery lead embedded with your team, working your roadmap. Right for organisations building AI capability over 12+ months.
Common questions
FAQ
Do we need to retrain or fine-tune a model?
Rarely. Most production use cases are better served by well-structured retrieval, strong prompting, and rigorous evaluation than by fine-tuning. We'll tell you honestly when it's worth it.
How long does it take to get to production?
A focused, well-scoped agent on a clean data set can go from kick-off to production in 8–12 weeks. Complexity, data quality, and governance requirements are the main variables.
Do you need access to our data?
Depends on the engagement. For RAG systems, yes — we need to understand your corpus. For other work we can often use synthetic or anonymised samples. Data handling is agreed before we start.
Can you work alongside our existing engineering team?
Yes. We often embed alongside internal teams rather than replacing them. We bring AI delivery experience; your team brings domain knowledge and long-term ownership.
Ready to build something that ships?
Tell us what you're working on and we'll give you an honest view of whether we can help — and what it would take.