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AI Agents

Build intelligent agents that can reason, communicate, perform tasks and interact with business systems.

  • OpenAI
  • Claude
  • Gemini
  • Node.js
  • Python
  • MongoDB

Agent Trace

Live
TRACE · REFUND REQUESTTHINK120msRefund request — needs order historyTOOL340msorders.lookup(order_id)TOOL210mspolicy.search(“refund window”)THINK150msOutside window — escalate, do not promiseTOOLS AVAILABLEorders.lookuppolicy.searchticket.createhuman.escalateNOTHING OUTSIDE THIS LISTGUARDRAILRefund outside policy — escalated instead of promisedREASON · CALL TOOL · CHECK POLICY · ESCALATE

Overview

What This Involves

An agent is only useful when it can act. We build agents with a narrow, well-defined remit, real tool access into your systems, and hard guardrails around what they are permitted to do. Every agent ships with evaluation cases, structured logging of its reasoning and tool calls, and a fallback path to a human. We favour several focused agents over one that tries to do everything, because focused agents are testable.

Deliverables

What You Receive

Every engagement ends with artefacts you own and can hand to another team.

  • Agent architecture

    Clear responsibilities, tool boundaries, memory strategy and escalation rules, documented before code.

  • Tool and system integrations

    Typed, permissioned access to your CRM, database, ticketing and internal APIs.

  • Evaluation harness

    A regression suite of real scenarios so prompt and model changes are measured, not guessed.

  • Observability

    Traced conversations, tool-call logs, token spend and quality scoring per agent.

Fit

Common Use Cases

If one of these sounds like your situation, this is the right starting point.

  • Internal assistants that query and update business systems

  • Multi-agent pipelines where each step has a specialist

  • Research, qualification and data-gathering agents

  • Customer-facing assistants with strict escalation rules

Process

How We Work

A predictable four-step engagement. You always know what stage we are at, what is being decided, and what exists at the end of it.

  1. 01

    Discover

    Understand the business, goals, users and existing workflows.

    • Stakeholder interviews and workflow walkthroughs
    • Audit of current tools, data and integration points
    • Constraints, compliance needs and success metrics

    Output: A written problem definition everyone agrees on.

  2. 02

    Plan

    Define the architecture, technology stack and automation strategy.

    • Solution architecture and data model
    • Build-versus-configure decisions per component
    • Scoped delivery plan with milestones and risks

    Output: An architecture and a plan you can hold us to.

  3. 03

    Build

    Design, develop and integrate the complete solution.

    • Interface design against a shared design system
    • Incremental delivery with a usable environment each cycle
    • Integrations, automated tests and evaluation of AI behaviour

    Output: Working software in a real environment, every cycle.

  4. 04

    Launch & Optimize

    Deploy, monitor and continuously improve the system.

    • Staged rollout with monitoring and alerting in place
    • Team enablement, documentation and handover
    • Measurement against the metrics agreed in discovery

    Output: A monitored system and a prioritised improvement backlog.

Proof

Related Work

Projects where ai agents did the heavy lifting.

View all projects
  • Abstract diagram of a multi-agent voice routing system with connected conversation nodes

    AI / Voice / Multi-Agent

    VeriVoice

    An intelligent multi-agent voice platform designed to automate business conversations and workflows.

    • React
    • Next.js
    • Node.js
    • AI
    • +1

    View Case Study

  • Abstract diagram of a support routing system with knowledge retrieval paths

    AI / SaaS

    AI Customer Support Platform

    A support platform that answers repeat questions from verified sources, drafts replies for agents, and escalates anything it cannot ground in documentation.

    • Next.js
    • Python
    • Claude
    • PostgreSQL
    • +1

    View Case Study

Also available

Other Services

Next step

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