A demo answers one question
It shows that an idea can work. It does not establish that users can only access their own data, that retries cannot duplicate a payment, or that someone can restore the service after a failed deployment.
AI coding tools can accelerate implementation. The production responsibilities remain: security, data governance, testing, monitoring and a clear account of who owns the code.
What we check
- Authentication, authorisation and separation between tenants.
- Input handling, secrets, dependency risk and licence provenance.
- Personal data flows, retention and external model providers.
- Evaluation, guardrails and monitoring for model-driven behaviour.
- Billing correctness, retries and failure recovery.
- Tests, deployment controls, documentation and handover.
A readiness report, then a practical backlog
The assessment separates launch blockers from work that can follow release. You receive a readiness report, a prioritised hardening backlog and a costed route to production. Scope and fee are agreed before the assessment starts.
Readiness specimen
An illustrative structure for organising findings:
| Area | Evidence required | Typical next action |
|---|---|---|
| Identity and tenancy | Access-control tests | Close cross-tenant access paths |
| Data governance | Data-flow and retention map | Remove unnecessary personal data |
| Model behaviour | Evaluation set and failure logs | Define acceptable failure behaviour |
| Operations | Deployment and restore evidence | Test recovery before launch |
Built on operating experience
We build and run deepcall.ai, an AI contact-centre product. Our assessment asks the questions that continue to matter after a successful demonstration: what happens under load, on a bad input, or when an upstream service fails?
Production is the start
If you need a team to own the continuing work, managed IP covers engineering stewardship after the initial hardening engagement.