AI agents with a clear job and clear boundaries.
Knowledge and workflow agents connected to your business systems, with evaluation, permissions and human oversight designed into the approach.
A new idea, an existing product or a platform ready for its next chapter.
Start with what your business needs.
A useful agent needs more than a convincing answer. It needs reliable sources, a defined role and a safe way to handle uncertainty or hand work back to a person.
More about our approach
We begin with a specific workflow, assess data readiness and build retrieval, tool access and evaluation around it. The goal is a system you can inspect and improve, with autonomy matched to the risk of the task.
Explore what’s possible.
Select a capability to see how it can take shape. Your project brings the pieces together.
Support agents
Customer-facing agents grounded in your knowledge base, resolving, escalating and logging every conversation.
Operations agents
Internal agents that process documents, reconcile records and chase workflows across your systems.
RAG knowledge systems
Retrieval over your documents and data, answers with sources, hallucination-resistant by design.
Multi-agent workflows
Orchestrated agent teams for complex processes, planning, execution and review roles composed deliberately.
Voice & conversation
Telephony and chat agents with natural handoff to humans when the conversation deserves one.
Evaluation & observability
Test suites, regression checks and traces for every decision, agent behaviour you can audit and improve.
Scope and acceptance criteria are agreed for your project.
Good experiences need strong foundations.
Look beyond the interface. Explore how access, quality and ongoing care fit into the plan.
Read the technical approach
Knowledge sources, retrieval, model selection and tool access are designed as separate, inspectable parts. Permission boundaries, approval steps and traces help explain what an agent can do and how its behaviour is reviewed.
Access & data
Access controls, sensitive data, authentication and relevant abuse paths are considered in the technical plan. Regulatory obligations and any specialist audit or security review are scoped explicitly with your team.
Quality & review
Representative tasks and failure cases form the evaluation plan. Changes to models, prompts and tools are checked against those cases, with escalation and human review for situations where the system should not act alone.
After delivery
Handover, documentation and any ongoing monitoring or improvement work are planned with your team. Support responsibilities and service arrangements are agreed for the engagement, rather than assumed to be included indefinitely.
Connections that matter
- CRM
- Ticketing
- Product catalogues
- Databases & documents
- Email & messaging
- Telephony
Technical requirements, checks and responsibilities are agreed as part of the project scope.
From first conversation to what’s next.
Explore the stages. Each one creates the clarity needed to move forward.
Scope the job
The workflow, data, tools and escalation rules the agent will operate within, written down first.
Ground
Retrieval and integrations built over your real data, with permissions scoped to the agent's role.
Guard & evaluate
Guardrails, human-approval gates and test suites that measure quality before and after every change.
Run & improve
Observability, feedback loops and a cadence of tuning, the agent compounds instead of drifting.
The right tools for the job.
We select the stack around your requirements, existing systems and the team that will maintain the result.
Explore our technology approach
OpenAI
Anthropic- RAG
- Vector search
- Tool calling
- Evaluations
Explore the work and thinking behind it.
Case studies and practical perspectives on the decisions that shape a project.
View case studiesResources to support the decisions behind your project.
Your sector. Your systems. Your priorities.
The application should reflect the people, processes and requirements of the business it serves.
A few things you might be wondering.
Clear answers before you decide how to move forward.
Have another question?Cost depends on scope, complexity, integrations and the level of ongoing involvement. We review your requirements before proposing an approach. Deliverables, assumptions, pricing and change handling are agreed in the proposal and contract.
We consider requirements, dependencies, content and data readiness, review cycles and testing. A delivery plan is agreed once those factors are understood. Staged work can help make progress visible without treating an early estimate as a guarantee.
Grounding, source references, constrained tools and evaluation can reduce risk. They do not eliminate it. We define fallback and escalation behaviour, and assess whether AI is appropriate for the workflow at all.
Hosting, model providers, retention, access and provider training policies must be reviewed for your project. These choices are agreed before connecting sensitive data. An NDA and contractual data responsibilities can be discussed during scoping.
We evaluate options against task quality, latency, cost, data requirements and deployment constraints. Provider choice is part of the technical plan, rather than a promise to use one model for every job.
Handover, documentation, training, maintenance and further improvements can be included according to the engagement. Support coverage, response arrangements and responsibilities are agreed separately, so both teams know what happens after the initial work.
AI Agent Development frequently asked questions
Let’s build the right solution.
Tell us what you want to achieve, what you have today and where you need help. We’ll start with a conversation about the approach that fits.