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Services / AI consulting

AI consulting

Put AI to work in a real system.

We identify where AI can remove friction, test the riskiest assumptions, and build the software and operational controls needed for production. The work stays grounded in the workflow, the data, and the people responsible for the result.

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What the work creates

A prioritised use-case and delivery plan
Working AI-enabled products and internal workflows
Evaluation, human review, and escalation paths
Secure integration with trusted data and existing systems

How we can help

Generative AI and RAG

Connect assistants to approved documents and business data, with source references, access controls and a clear response when the evidence is missing. We assess document extraction, chunking, retrieval and model choices against your use case.

Agents and integrations

Design bounded workflows that use APIs and tools, with input validation, approval for consequential actions, audit history and safe failure handling.

Evaluation and operations

Define representative examples and acceptance criteria, then assess quality, latency, cost, monitoring and data handling before expanding a pilot.

A practical route from question to production.

01

Find the useful problem

Map the workflow, users, data, constraints, and failure costs before choosing an implementation.

02

Prove the risky parts

Test quality, latency, security, and operating assumptions with representative inputs.

03

Engineer for production

Build the surrounding software, evaluations, monitoring, permissions, and support model.

Read our practical guide to ai consulting →

Questions teams ask

Can you help us choose an AI use case?

Yes. We can begin with a focused discovery that compares opportunities by usefulness, feasibility, data readiness, risk, and the effort required to operate them.

Do you build the surrounding software as well?

Yes. BuildrLab covers the application, integrations, architecture, cloud foundations, delivery pipelines, observability, and operational handover around the AI capability.

How do you manage AI risk?

We define expected behaviour, representative evaluations, permissions, human review, failure handling, audit needs, monitoring, and a clear owner for production decisions.