Bespoke AI Agent Development

A bespoke agent is a system designed around your business. Not a Copilot wrapper with your logo.

When AI tooling that earns its keep doesn't exist off the shelf, we build it. Senior engineering, governance built in, evaluation harness from day one.

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Use cases

Where a bespoke agent earns its keep

Four sector-specific examples. Each is anonymised; each is real.

Legal

Matter-intake agents that interview clients in plain English, capture conflicts data, and route to the right partner with a structured brief.

Healthcare

Clinical-decision-support agents wired into PAS/EPR data with audit trails by design. The clinician is always in the loop; the agent surfaces options.

Finance

Compliance-monitoring agents that watch transaction streams and route anomalies to named reviewers — with the reasoning trail attached.

Professional services

Proposal-drafting agents that pull from your past wins, current capacity, and pricing logic to draft 80% of a proposal in minutes.

Methodology

How we build them

Six steps. Discovery before code. Evaluation before deployment.

01

Discovery interview

What work does the agent actually do? What's the human in the loop? What's the cost of a wrong answer?

02

Task and tool mapping

What tools does the agent need access to? What data? What guardrails?

03

Guardrail design

Before we write the first line of agent code. What must the agent never do? What requires human approval?

04

Build sprint

Two-week iteration cycles. Working agent at the end of each, even if narrow.

05

Evaluation harness

A test suite the agent runs against. Regression tests for every edge case we find. Quality measurable, not vibes.

06

Ongoing operations

Monitoring, model updates, scope expansion. We don't ship-and-leave.

Governance built in

What governance gets right

Bespoke agents are sold to firms where "we'll figure compliance out later" isn't a defensible answer. Three pillars.

Data residency

We design where data flows from the start. No accidental egress to model providers; no surprise data crossings.

Model selection criteria

Licence terms, training-data provenance, evaluation rigour. We choose models for sustainability, not press-release excitement.

Audit trail design

Every agent decision logged with reasoning, inputs, and outputs. Audit trail is a feature, not an afterthought.

Engineering detail

We build on stable open-source frameworks. The specific stack — LangGraph, AutoGen, custom orchestration — is matched to your environment during scoping. See our research dossier on the current state of the agent framework market (May 2026).

The AI engineering market is moving fast. Specific framework recommendations on this page reflect our May 2026 market view. We re-evaluate every six months.

Free consultation

We build agents that
earn their keep.

A 30-minute call with a senior consultant. We'll listen to the problem you're trying to solve and tell you whether an agent is the right answer.

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