AI agents that do real work inside your business.
We design and build AI agents that resolve tickets, process documents, answer from your knowledge and act in your systems — measured on outcomes, monitored in production, and kept inside the guardrails you set.
The problems this solves.
The demo works, the rollout doesn't
An assistant that impressed in a workshop falls over on real data, real edge cases and real users, and nobody can say how often it is wrong.
Agents with no hands
The model can talk about your systems but cannot act in them: no tool calling, no integration, no permissions model.
No way to know if it's working
There is no evaluation set, no monitoring and no review loop, so every change is a leap of faith.
Capabilities, in the order they're usually needed.
Not a menu to pick from — a sequence. Most engagements start at the top and stop when the business number moves.
AI agent architecture
Single-agent or multi-agent designs chosen for the job: planning, tool use, memory and hand-offs, with clear boundaries on what the agent may decide alone.
Multi-agent workflows
Workflows where specialised agents draft, check, enrich and escalate, orchestrated with explicit state so a failure is visible and recoverable.
Retrieval-augmented generation (RAG)
Retrieval over your documents, databases and knowledge graphs — chunking, indexing, ranking and citation designed for your content, not a generic template.
Tool calling and enterprise integration
Agents that call your CRM, ERP, ticketing, email and internal APIs through typed, permissioned tools, with idempotency and audit built in.
Evaluation and observability
Evaluation sets built from real cases, regression runs on every change, tracing of every step, and dashboards for accuracy, cost and latency.
Security and guardrails
Input and output policies, prompt-injection defences, data-leak prevention, rate and budget limits, and least-privilege access to tools.
Human-in-the-loop workflows
Approval steps, confidence thresholds and review queues where the stakes require them — designed so people stay fast, not buried.
Production monitoring
Alerting on quality drift, cost spikes and failures; feedback capture; a release process for prompts, models and tools.
How this fits the orchestration.
An agent is one instrument. Orchestration decides which agents exist, what they may touch, how they are evaluated and how they share retrieval, guardrails and monitoring — so the fifth agent costs a fraction of the first.
Orchestration is the layer between your business goals and your AI capabilities — the part that decides what to build, how it fits together and how you know it's working. See how orchestration works.
Deliverables you can use.
Every engagement ends with something that works without us: documents your board can read, systems your engineers can run.
- Agent designArchitecture, tool catalogue, permissions model and failure handling, agreed before build.
- Working agent in productionDeployed on your cloud, integrated with your systems, behind your authentication.
- Evaluation suiteTest cases from real traffic, automated scoring and a regression gate for every release.
- Guardrail and policy setDocumented controls for input, output, data and actions, with the evidence regulators ask for.
- Monitoring and runbookDashboards, alerts and the operating procedures your team uses to run it.
Usually played together.
Ready to bring your AI into tune?
Tell us the business goal. In a 30-minute call we'll show you how we'd score it — which instruments, in which order, measured how.