Choosing an AI development company is not just a question of which team can demonstrate a chatbot. UK businesses need a partner that can connect models to real systems, evaluate reliability, manage sensitive data and maintain the software after launch.

1. Start with the business workflow, not the model

Define the decision, process or task you want to improve. Write down the current baseline, who will use the system, what data it needs and how success will be measured. A useful discovery process should identify cases where conventional software or automation is more appropriate than generative AI.

2. Ask for evidence of production delivery

Request a walkthrough of a relevant deployed system. Ask how it handles failures, permissions, observability, human review and changing requirements. A prototype is useful, but it does not establish production readiness. For a financial-services example, see WeUno’s mortgage underwriting AI case study.

Independent client evidence: In a verified Clutch review, the founder of SuperPowerMe, an AI copilot business, confirms WeUno built its frontend and backend services, delivered the MVP on time and within budget, and continued supporting new features. This is independently published client feedback about a delivered project, rather than an unverified agency claim.

3. Check RAG and agentic AI capabilities separately

Retrieval-augmented generation (RAG) helps an application retrieve relevant source material before producing an answer. Evaluate source attribution, document updates, retrieval quality and hallucination testing. See our RAG development services and RAG evaluation guide.

AI agents can take actions through tools and APIs. Ask how tool permissions, approvals, limits, audit logs and escalation work. Do not accept a demonstration of autonomous behaviour as evidence of safe production operation.

4. Verify integration and security design

Ask who owns the source code, prompts, evaluation datasets and infrastructure. Confirm how the solution connects to existing applications and databases, where data is processed, which external services receive it, and how access is controlled. Security and compliance requirements should be defined for your organisation and sector rather than assumed from a vendor badge.

5. Compare total delivery cost, not just build estimates

Request an itemised scope covering discovery, engineering, model usage, testing, deployment, monitoring and maintenance. Costs depend on data readiness, integrations, required reliability and review processes. For a starting framework, read our UK AI development cost guide.

6. Agree acceptance criteria before development

Examples include accuracy on representative test cases, latency, failure handling, security controls, cost per workflow and measurable staff time saved. Agree what happens when the model is uncertain. Require a plan for post-launch monitoring and iteration.

Questions to ask an AI development agency

• Can you show a comparable production system and explain what went wrong during delivery?
• How will you test outputs against our own business data?
• What happens when the model or an integration fails?
• Who approves sensitive agent actions?
• Which third parties process our data?
• Who owns the code, infrastructure and documentation?
• How will we measure return on investment after launch?

When to speak to WeUno

WeUno is a UK-based software engineering company working across custom AI applications, RAG, AI integration and enterprise software. Explore our AI development services and case studies to assess whether our experience fits your project.