AI Development / AI prototype to production
Move your AI prototype to production.
The demo worked, but it never reached real users. We audit the prototype you already have, fix what blocks production, and ship it connected to your systems, tested on real cases and monitored, without starting again from scratch.
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From stalled prototype to production system. We keep what works and fix what blocks launch.
Technical audit
A review of the code, prompts, data and infrastructure, ending in a prioritised list of production blockers.
Remediation plan
An architecture and fix plan, with a fixed-scope first milestone and agreed acceptance criteria.
Evaluation suite
Tests built from representative real inputs, so accuracy is measured instead of assumed.
System integration
The prototype connected to your CRM, ERP, databases and internal tools through their APIs.
Permissions and approvals
Scoped access for every tool, and human approval before high-stakes actions.
Reliability engineering
Retries, fallbacks, failure recovery and duplicate-action protection.
Cost and latency control
Model choice, caching and limits so running costs stay predictable.
Monitoring and audit trails
Traces, logs and alerts in production, so problems surface before users report them.
Handover
Documentation, deployment instructions and ownership of code and cloud accounts, plus a defined support window.
AI systems we have taken to production. Each result comes from a delivered engagement.
AI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · Workflow automationWorkflow automation platform
AI extraction feeding a deterministic rules engine, with consistency checks built in.
~70% faster document prep AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processingHave an AI prototype that never made it to production?
From idea to production. Four stages, with a working version reviewed at each one.
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Discovery
Map the prototype, the systems and data involved, and what good looks like.
Discovery notes
- What the prototype does today
- Where it breaks on real data
- Systems it must connect to
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Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Prioritised production blockers
- Fix plan and architecture
- Fixed-scope first milestone
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Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Fixes built and tested
- Evals on real cases
- Review at each milestone
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Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring and traces
- Handover and support window
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
AI platform for an insurance broker
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
AI prototype to production FAQs.
Why do AI prototypes stall before production?
Usually because the demo was never connected to real systems, tested on real cases, given permissions and approval steps, or monitored. The model is rarely the problem. The missing engineering around it is.
Do you rebuild the prototype from scratch?
Not by default. The audit shows what can be kept and what must change. We fix the blockers first and rebuild only the parts that cannot be made reliable.
What do we get from the audit?
A prioritised list of production blockers, an architecture and fix plan, an evaluation approach using your real inputs, and a fixed-scope first milestone with acceptance criteria.
Can you work with code another team wrote?
Yes. We review the existing repository, prompts and infrastructure and work in your codebase and cloud accounts, so you keep ownership.
Do you work with US and UK companies?
Yes. We are a Dubai-based company serving US and UK clients, working in US time zones and invoicing in USD.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.