AI Development / AI POC development
An AI proof of concept that answers one question well.
A focused build that tests your AI idea against real data, with success measures agreed before any code is written.
Astra Short on time? Ask about this page in plain words.
AI POC services. Learn what works before committing to a full build.
Question framing
Agree the one question the POC must answer.
Success measures
Define what a good result looks like before building.
Data readiness check
Confirm the data exists and is usable.
Rapid build
A working prototype focused on the core idea.
Evaluation
Results measured against the agreed success measures.
Findings report
What worked, what did not, and what it would take to scale.
Architecture outline
A path from POC to production if the results hold.
Cost estimate
Expected running costs at production scale.
Next-step plan
A clear recommendation: scale, adjust or stop.
Related AI work. Delivered engagements in neighbouring areas. None of them is a AI POC development project.
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 · HealthcareMulti-agent voice AI
Live voice agents for scheduling, verification and follow-up calls, escalating to a person when needed.
~50% fewer manual handling casesHave an AI idea you want to test against real data?
From idea to production. Four stages, with a working version reviewed at each one.
-
Discovery
Map the idea, the systems and data involved, and what good looks like.
Discovery notes
- Goals and constraints
- Data available
- How success is measured
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Approach and architecture
- Evaluation plan
- Delivery milestones
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Built against real data
- Results measured
- Review at each milestone
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Monitoring
- Defined support window
Why Avinya for an AI proof of concept. A clear answer on your own data, before you commit to a full build.
A decision, not a demo
A proof of concept ends with a clear go or no-go, tested on your own data.
People stay in control
Approval checkpoints, escalation paths and exception review are designed in from day one.
Traceable by default
Citations, audit trails and logs, so outputs can be verified rather than trusted blindly.
Production, not demos
Built to run against real systems and real data, with monitoring once live.
Full-stack delivery
Data pipelines, models, product and integrations from one team.
AI and Web3 under one team
One studio for AI systems and, where it helps, on-chain infrastructure.
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.
“Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
Guides on this topic. Plain answers to the questions buyers ask first.
How to Build an MVP: Steps, Timeline and Cost Drivers
Seven steps from one customer and one job to a first release.
Read the guide → GuideEnterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide → GuideAI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide →AI POC development FAQs.
What is an AI POC?
A short, focused build that tests whether an AI approach works on your real data before you invest in a full product.
What do we get at the end?
A working prototype, measured results against the agreed success measures, and a recommendation on next steps.
How long does a project take?
It depends on scope and integrations. We agree milestones up front and review a working version at each one.
How is an AI proof of concept different from an MVP?
An AI proof of concept answers one question, whether the AI can do the task on your real data. An AI MVP development project then wraps it in a usable product. We scope each stage separately and review risks with the NIST AI Risk Management Framework.
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.