An agentic system reasons over your data, calls your tools, and carries a task through to a result. We design, build, and run those agents for enterprises–with the evaluations, guardrails, and human oversight that make them safe to put in front of real users.
A chatbot answers. An AI agent acts. Give an agent a goal and it plans the steps, retrieves what it needs from your systems, calls the tools required to make something happen, checks its own work, and escalates to a person when the decision is not its to make.
That difference is the whole point. Most enterprise value sits in multi-step work–reconciling a claim, preparing a report, qualifying a lead, triaging a ticket, drafting a procurement decision. Answering questions about that work saves minutes. Completing it saves days.
It is also where most agent projects fail. An agent with tool access and no guardrails is a liability. We build the boring parts–grounding, evaluation, observability, permissions, fallbacks–because they are what separates a convincing demo from a system your team trusts on a Monday morning.
Every engagement starts from a specific job to be done, not a technology. These are the patterns we are asked for most often.
Grounded support and sales agents that answer from your own content, stay inside their remit, and complete the next step – booking a meeting, raising a ticket, checking an order – instead of handing the user back to a form.
Agents that read contracts, claims, invoices, and research papers, extract what matters into your schema, flag exceptions for review, and push clean structured data into the systems downstream.
Agents that gather evidence across internal and external sources, reconcile conflicts, and produce a cited briefing an analyst can defend – with every claim traceable to a source.
Domain copilots for the teams doing the work: an agent that knows your codebase, your pricing rules, your clinical pathway, or your plant constraints, and is wired into the tools those teams already use.
Where one agent is the wrong shape, a supervised set of specialists – retrieve, decide, act, verify – with orchestration that makes the handoffs explicit, observable, and replayable.
Five stages. You get something working in production early, then we harden it – rather than a six-month build that meets reality on launch day.
We map the actual task end to end: who does it now, what systems it touches, where the judgement calls are, and what a good outcome looks like in numbers. Some of what comes out of this is better solved without an agent, and we will say so.
Agents invent things when they have nothing to stand on. We build the retrieval layer first – your documents, databases, and APIs – so every answer is anchored in your own information and can be traced back to it.
The agent gets the tools it needs to act and nothing more: scoped permissions, validated inputs, rate limits, and a clear refusal path. Human approval sits at exactly the points where a wrong action would cost you.
We build an evaluation set from your real cases and measure against it on every change – accuracy, groundedness, refusal behaviour, latency, cost. Without this you are shipping on vibes.
Observability on every step and tool call, drift detection on quality, and a feedback loop from real usage. We stay until it is adopted – building the agent is half the job, the other half is making sure your teams actually use it.
Three different tools that get confused with each other. Choosing wrong is the most expensive mistake in this space, so here is how we decide.
| Approach | Best for | What to watch |
|---|---|---|
| Rule-based automation (RPA) | High volume, identical every time, zero ambiguity | Breaks the moment the input varies or a screen changes. Cheapest and most reliable answer when the process really is deterministic – do not put an agent on it. |
| Retrieval chatbot | Answering questions from a known body of content | It informs, it does not act. Perfect for support deflection and internal search; the wrong shape when the user's actual goal is to get something done. |
| AI agent | Multi-step work with judgement, varied inputs, several systems | Needs grounding, evaluation, and guardrails to be trustworthy, and costs more per task than a rule. Worth it when the task is genuinely variable – wasteful when it isn't. |
We are not tied to one vendor. We pick the model and framework that fit your constraints on data residency, latency, and cost, and we build so that the model underneath can be swapped as the field moves.
Production systems, not prototypes. Each of these is an agentic system by the definition above – it retrieves, decides, acts, and checks itself.
The assistant in the corner of this page retrieves from our live company knowledge base, answers only from what it finds, declines everything outside its remit, and books a meeting on a real calendar without a human in the loop.
Answers grounded in live content, zero-touch booking Read the case studyAn event-driven agentic pipeline on Azure: live match events trigger model inference, a generation layer turns the output into editorial-quality narrative, and the system grades its own predictions against results to retrain.
In-match insights in under 5s, zero manual analysts Read the case studyA student-facing agent that coaches rather than answers, adapts to each student's mastery level, and works across question types through a plugin architecture.
87% student satisfaction, 40% fewer support queries Read the case studyA system that takes a goal, plans the steps, uses tools and data to carry them out, and checks its own work – rather than producing a single response and stopping. The practical test: if it can only tell you things, it is a chatbot; if it can change something in your systems, it is an agent.
We aim to have a scoped agent working against real data in 4 to 6 weeks, and hardened for production in 8 to 12. The variable is almost never the model – it is how accessible your data is and how many systems the agent needs permission to touch.
Three layers. Grounding, so the agent answers from your retrieved content rather than the model's memory. A relevance floor, so when nothing relevant is retrieved the agent declines instead of guessing. And scoped tools with validated inputs, so even a bad decision cannot take an action it was never permitted to take. The assistant on this site is built exactly this way – ask it something off-topic and watch it refuse.
No. We regularly deploy inside your own cloud tenancy – Amazon Bedrock or Azure OpenAI in a region you choose, so data stays within your boundary and is not used for training. Where policy rules out hosted models entirely, we build on open-weight models you run yourself.
A scoped pilot typically lands in the low tens of thousands; a production agent with integrations, evaluation, and monitoring is a larger programme. Running costs depend on volume and model choice, and we design to keep per-task cost predictable. We will give you a range in the first conversation rather than after a discovery invoice.
We build an evaluation set from your real cases before we build the agent, and measure every change against it: task success rate, groundedness, false-refusal rate, latency, and cost per task. In production we track the same metrics live, with drift detection, so quality regressions surface before your users report them.
That is usually the majority of the work. We integrate through whatever your systems expose – REST and GraphQL APIs, databases, message queues, Microsoft Graph, ERP and CRM connectors – and increasingly via the Model Context Protocol, which gives agents a standard way to reach tools without bespoke glue for each one.
In our experience they absorb the mechanical middle of a job and hand the judgement back. The agents we build are designed with a human at the decision points that matter, and the projects that stick are the ones where the team helped decide where those points are.
Workflow automation is the right answer when a process is deterministic and repeatable. Agents earn their cost when inputs vary and the work needs judgement. Most clients end up with both, and part of our job is telling you which half of your process belongs in which.
We are headquartered at Oxford Science Park in the UK, with a team in Kochi, India. We work with a global remote model and have delivered for clients in the UK, the US, and India across higher education, publishing, gaming, healthcare, and petrochemicals.
Bring us a task, not a technology brief. A 30-minute conversation is usually enough to tell you whether an agent is the right answer – and we will say so if it isn't.
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intelligence, integrated.
From strategy to deployment, your AI transformation begins here
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