AI Agent Development that works for you
We build autonomous agents that reason, plan and act. Beyond chatbots: intelligent systems that automate entire workflows and make decisions for your business.
What are AI Agents?
AI agents are autonomous systems that go far beyond answering questions. They reason about complex problems, plan sequences of actions, use external tools and execute tasks end-to-end without human intervention. They are the next evolutionary leap after chatbots and conversational assistants.
AI Agents for every area of your business
From customer support to multi-agent orchestration. We identify where an AI agent delivers the most value and build it to measure.
Autonomous customer support
Beyond chatbots that answer: agents that resolve. They process returns, modify orders, apply discounts and escalate only when truly necessary. 80%+ autonomous resolution of incidents.
Back-office automation
Agents that process invoices, classify documents, triage emails, enter data into your ERP and reconcile information across systems. They eliminate repetitive manual tasks from your team.
Sales agents
Automatically qualify leads, personalise follow-ups based on prospect behaviour, schedule demos and update your CRM. Your sales team focuses on closing, not chasing.
Analytics agents
Analyse data, generate periodic reports, detect anomalies and proactively alert when something deviates. They connect to your databases and dashboards to deliver actionable insights.
Code agents
Automate CI/CD pipelines, run code reviews, generate tests, monitor code quality and create pull requests. They accelerate your engineering team development cycle.
Multi-agent orchestration
Complex workflows where multiple specialised agents collaborate: one researches, another analyses, another drafts and another validates. Modular systems that scale with your business complexity.
Want to automate workflows with AI agents?
Free consultation →AI agent quality evaluation, before and after launch
Agent evaluation is one of our specialities. Every agent we deliver ships with its eval set, a judge that reviews real conversations and a process to diagnose and fix each failure. It matters because an agent can return HTTP 200, with zero tool errors and inside its latency budget, and still confirm seat 3A without asking whether it is free. Google opens its post on the agent quality outer loop with that example, and no error dashboard catches it. That is why every agent we put in production ships with its own evaluation system.
Four places a failure can come from
From the outside all four look the same, but each one has a different owner and a different fix. A trace shows the order of the steps, not the cause.
Model
It hallucinates an argument or drops a constraint the user gave three turns earlier.
Orchestration
It routes to the wrong sub-agent or loses state in a handoff: the "vegan" profile never reaches the agent that recommends restaurants.
Tool contract
The tool rejects an input because its valid values were never documented in the schema.
Instructions
The prompt is missing a rule. It asks for a seat check when showing the seat map, but not when the user names a seat.
How we evaluate it
- 01
Eval set before launch
Real cases from your process that run on every prompt, model or tool change. If a change makes results worse, it does not ship.
- 02
Sampling real conversations
We read a sample of production sessions from their traces, on a schedule and after every deployment.
- 03
A judge with your business checklist
A model reviews each session against a list of failure types (procedure, tool use, made-up data, task completion) and the goals we define with you. It records what the agent did and what it should have done.
- 04
Cluster and verify
We group findings that share a mechanism, and a second model checks them against the full transcripts. Anything the evidence does not support is dropped before it reaches your team.
- 05
Diagnosis against the deployed code
Each verified failure is read next to the exact revision that produced it, and we point to the file and line, or to the external step at fault when the problem is not in the code.
- 06
Close the loop
The failing conversation goes into the eval set. The fix is validated by replaying it and stays as a regression test; the issue is tracked as new, recurring or resolved.
Tools we work with
A question to ask any agent vendor: how would you find out that your agent confirms bookings without checking availability? If the answer is error alerts, you would not, because that request returns 200.
Evaluate your agent with us →Our AI agents stack
We combine the best foundation models with agent frameworks, vector databases and the MCP protocol to build agents that hold up under production traffic.
From idea to agent in production
An iterative process that validates fast and scales with confidence. Each phase has clear deliverables and defined success metrics.
Discovery & Design
We map your current workflows, identify automatable tasks and design the agent architecture: what tools it needs, what decisions it will make and what the safety guardrails are.
Functional prototype
In 2-3 weeks we build a prototype that demonstrates value. We test it with real data and adjust agent behaviour until it meets precision and reliability expectations.
Production & scaling
We deploy the agent with retries, fallbacks, structured logging and real-time monitoring. We integrate with your existing systems via APIs and MCP.
Continuous evaluation in production
A judge reviews samples of real conversations, groups failures by mechanism and diagnoses them against the deployed code. Every verified failure goes into the eval set as a regression test.
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Related case studies
Real projects where we applied artificial intelligence and autonomous agents
Invisible Homes
PropTech platform with AI that connects buyers with off-market properties in London through intelligent matching.
80K+ registered buyersOrquest
AI platform that optimises retail shift planning with autonomous planning agents.
42 international marketsXceed
Nightlife platform with a recommendation engine powered by personalised AI agents.
10M+ annual attendeesFrequently asked questions about AI agents
How is an AI agent different from a chatbot?
What types of tasks can an AI agent automate?
Is it safe to give autonomy to an AI agent in my company?
What is the MCP protocol and why is it important?
How long does it take to have an AI agent in production?
How do you know whether an AI agent is working well in production?
Ready to put AI agents to work in your business?
We help you identify the workflows with the highest automation potential and build AI agents that deliver ROI from day one.
Tell us your challenge. We'll propose a solution.
No commitment. Within 24 hours, you'll receive a proposal with scope, timeline and budget. No fine print.