AI production readiness

Turn a promising AI prototype into a controlled production service.

Find the gaps between an impressive demonstration and software the organisation can trust, operate and improve in the real world.

The prototype gap

A successful demo answers only the first question

A prototype can show that a model produces useful output. Production software must also handle poor inputs, changing data, access control, cost, failure, auditability and the people responsible for the final decision.

Domville Tech combines conventional .NET and Azure engineering with practical AI experience to assess the complete workflow—not the model in isolation.

Unreliable or invented answers
Unclear success measures
Sensitive data concerns
No human approval route
Uncontrolled model cost
Fragile prompt-only logic
No monitoring or audit trail
Difficult system integration

Readiness dimensions

Test the whole service, not just the happy path

  • Business value and measurable success criteria
  • Representative test data and evaluation approach
  • Retrieval quality, grounding and source traceability
  • Privacy, security and access boundaries
  • Human review, escalation and override
  • Application and data integration
  • Latency, capacity and operating cost
  • Failure handling and service continuity
  • Logging, monitoring and auditability
  • Ownership, support and change management

Assessment route

Measure. Control. Integrate. Operate.

  1. 01

    Measure

    Define representative cases, current baselines and acceptable outcomes.

  2. 02

    Control

    Design grounding, validation, human review and safe failure behaviour.

  3. 03

    Integrate

    Connect identity, data and established systems through supportable services.

  4. 04

    Operate

    Add monitoring, cost controls, ownership and a disciplined improvement loop.

What you receive

A clear proceed, remediate or stop decision

The outcome may be an implementation plan, a bounded remediation stage or evidence that further investment is not justified.

  • Prioritised production-readiness findings
  • Quality, safety and operational risk assessment
  • Recommended evaluation and control framework
  • Integration and architecture recommendations
  • Indicative remediation sequence
  • Ownership and operating-model actions
  • Hands-on implementation proposal where appropriate

Starting earlier?

Validate the opportunity before building the prototype

The broader AI consulting service helps identify use cases, prepare teams and choose an evidence-led starting point.

Move beyond the demo

Make the next AI decision defensible.

Discuss the prototype