AI PoC Development
Prove your AI idea before you build it
OmniLabs turns AI ideas into working proofs of concept in 4 to 6 weeks, so you can test the idea on real data and decide what to build next.
Discuss your AI PoCWhy AI ideas stall
- 01
You have an AI idea but no proof it works on your data.
- 02
Stakeholders won't fund a full build without evidence.
- 03
It's unclear whether GenAI, RAG, or an agent is the right approach.
- 04
Prototypes grow into products without clear success criteria.
A working PoC, not a slide deck
Narrow in scope, built on your real data and systems.
- Use case discovery and prioritization
- Functional AI prototype
- Generative AI, RAG, and AI agent PoCs
- Integration with selected data or systems
- User testing and quality evaluation
- Roadmap from PoC to production
AI PoC development services
Functional PoCs that test your AI idea in practice and prove its value.
Use case discovery and assessment
We find where AI delivers real value and pick the use cases worth testing.
What you get:
- AI use case assessment
- Feasibility and value analysis
- Prioritized use cases
- PoC roadmap
AI prototype development
A functional prototype to test the core idea with real users and data.
What you get:
- Functional AI prototype
- Core features and workflows
- Integration with selected data or systems
- User testing and feedback
Generative AI PoC
GenAI validated on your use case, from content generation to summarization.
What you get:
- Working GenAI solution
- Integration with your systems
- Business-specific workflows
- Quality and performance assessment
RAG proof of concept
AI connected to your documents, to test how accurately it answers from your data.
What you get:
- Document processing and indexing
- RAG pipeline
- AI search and answers
- Retrieval and answer quality evaluation
AI agent PoC
An agent that uses tools and completes multi-step tasks in your process.
What you get:
- Functional AI agent
- Tool and system integrations
- Automated task workflows
- Agent performance evaluation
From PoC to production
When the PoC proves out, we harden it for real users.
What you get:
- Codebase and architecture review
- Security hardening
- Testing and CI/CD
- Production deployment
What an AI PoC gives you
Evidence on your own data in weeks, not a guess.
A clear go, change, or stop decision before you commit budget.
A PoC built to scale, with a roadmap to production.
How we build an AI PoC
Define the use case
We agree on the business goal, the data, and how success is measured.
Pick the approach
We choose GenAI, RAG, an agent, or another approach based on your data and workflow.
Build and test
We build the PoC on real data and test it with real users.
Evaluate and plan
We review results against the criteria and map the path to production.
AI PoC questions
How long does an AI PoC take?
Typically 4 to 6 weeks, depending on the use case, data, and integrations.
What's included in an AI PoC?
Core AI functionality, integration with selected data or systems, a working interface, testing, and an evaluation of results.
How do you choose the right AI approach?
From your business goal, data, workflows, and requirements. It could be GenAI, RAG, an AI agent, or another approach.
Is the PoC production software?
It's built to scale but narrower in scope. Production adds security hardening, full integrations, testing, and monitoring.
What happens after the PoC?
If it proves out, we refine it, optimize the architecture, estimate production requirements, and plan the rollout.
Ready to test your AI idea?
Tell us the idea and the data behind it, and we'll show you how we'd prove it.
