AI agents that don't just answer. They get work done.
We design, build and deploy production-grade AI agents that plan, call tools, read your data and take action, with guardrails, evaluation and human-in-the-loop controls built in from day one.
The problem. Our approach.
Most teams have tried a chatbot that answers questions and then stops. The real cost sits in the steps after the answer: looking up the order, checking the policy, updating the ticket, drafting the reply. An agent is useful when it can do those steps itself, inside clear limits.
We start from one well-defined job and the tools a person uses to do it today. We give the agent narrow, permissioned access to those tools, build an evaluation set from real examples, and measure it against that set before anything reaches a customer. Sensitive actions go through a human approval step until the evidence says otherwise.
Doing this well is mostly engineering discipline, not prompt tricks. Every run is logged, every tool call is traceable, and changes to prompts or models are tested against the same evals. That is what lets you trust the agent with more work over time.
Ideal for
- Support teams drowning in repetitive tickets
- Sales teams spending hours on lead research before every call
- Operations teams copying data between CRMs, sheets and helpdesks
- Companies whose answers live in scattered docs, wikis and databases
Customer support agents
Resolve tickets using your docs, order data and policies, and hand off to humans when needed.
Sales & SDR agents
Research leads, personalise outreach, qualify replies and book meetings into your calendar.
Knowledge copilots (RAG)
Answer questions over your internal docs, wikis and databases with citations.
Multi-agent systems
Specialised agents that collaborate on research, operations and back-office workflows.
Where it makes an impact.
- 24/7 support that resolves, not just deflects
- Lead research and personalised outreach at scale
- Internal assistants over company knowledge
- Operations agents that update CRMs, sheets and tickets
What you get
- 01Agent architecture & tool design
- 02Retrieval pipeline (RAG) over your data
- 03Guardrails, evals and monitoring
- 04Web, Slack or WhatsApp interface
- 05Deployment, docs and handover
Tech we use
From first call to production.
A first production agent usually takes 6–10 weeks, depending on integrations and review requirements.
Map the job
We pick one task, write down how a person does it today and list the systems and decisions involved. This becomes the agent's scope and the first draft of its tools.
Prototype with evals
We build a working agent against real examples from your data and score it on an evaluation set. You see where it succeeds and where it fails before any launch decision.
Harden & integrate
We add guardrails, approval steps, permissions and logging, then connect the agent to your production systems and the channel your team or customers use.
Launch & monitor
We roll out gradually, review transcripts and failures, and extend the eval set as new cases appear. Scope grows only when the results support it.
Outcomes
What changes for your team.
- 01
Routine requests handled end to end, with people focused on the cases that need judgement
- 02
A clear record of every action the agent took and why
- 03
An evaluation set that makes model and prompt changes safe to ship
- 04
An agent your team understands, owns and can extend
Agentic AI, in depth.
Agentic AI FAQ
Can't find what you're looking for? Send us a message and we'll get back to you within one business day.
Which AI models do you use?
We are model-agnostic and pick per task: OpenAI, Anthropic Claude, open-source models or a mix, balancing quality, latency, cost and data-privacy requirements.
How do you stop agents from making mistakes?
Every agent ships with guardrails, constrained tools, evaluation datasets and human approval steps for sensitive actions, plus logging so you can see exactly what it did.
Can the agent use our existing systems?
Yes. Agents connect to your CRM, helpdesk, databases, email and internal APIs through secure, permissioned tools.
What is the difference between an agent and a chatbot?
A chatbot answers. An agent can also act: look up records, call APIs, update systems and decide the next step. That extra power is why we put so much work into permissions, approvals and evaluation.
What happens to our data?
We use model providers and settings that match your privacy requirements, keep data access limited to what each tool needs, and can run on your own cloud account. We agree on retention and logging rules before anything goes live.
Pair it with the rest of the stack.
Start a project
Have a project in mind? Let's build it.
Tell us what you're working on. We'll reply within one business day with ideas, a plan and next steps.