A proof of concept (PoC) is a focused, time-boxed build that answers one question: does this AI approach actually work for our problem? An MVP is the next step — a minimum viable product with enough functionality to put in front of real users and gather real feedback. DevByte delivers both, typically in 4 to 8 weeks, so organisations can validate an AI idea, demonstrate ROI on a specific workflow, and make the business case internally before investing in a full production system.
The most expensive AI mistake is building the full product before answering the question that should have been answered in week four. Can this AI approach handle the variability in our real data? Does the model perform well enough on our specific use case to justify the compliance investment? Will the workflow change actually deliver the efficiency we projected?
A PoC answers these questions cheaply and quickly — before the architecture is locked, before the integrations are built, before the compliance review is complete. The failure of a PoC is not a loss. It is the cheapest possible way to discover that a different approach is needed, and it costs a fraction of discovering the same thing after a full build.
The most expensive AI mistake is building the full product before answering the question that should have been answered in week four. Can this AI approach handle the variability in our real data? Does the model perform well enough on our specific use case to justify the compliance investment? Will the workflow change actually deliver the efficiency we projected?
A PoC answers these questions cheaply and quickly — before the architecture is locked, before the integrations are built, before the compliance review is complete. The failure of a PoC is not a loss. It is the cheapest possible way to discover that a different approach is needed, and it costs a fraction of discovering the same thing after a full build.
Every PoC starts with a single, well-defined hypothesis: ‘If we use a RAG architecture with our clinical notes dataset, the model will be able to extract the relevant billing codes with at least 85% accuracy on a held-out test set.’ This specificity is what makes the PoC evaluable. A PoC without a defined success criterion cannot tell you whether it succeeded.
We then build the minimum system needed to test that hypothesis — not the minimum system needed to impress a demo audience. This means skipping the UI, skipping the integrations, skipping the edge case handling, and building only the core AI component with representative sample data. The speed of a well-scoped PoC comes from discipline about what is in scope and what is not.
The evaluation phase is as important as the build phase. We test the system against your actual data — including the messy, incomplete, inconsistent data that reflects your real operational environment, not a cleaned dataset created for the test. This is where most PoCs reveal their real results: the approach works on clean data but struggles with the data quality in production. Finding this in week four is far better than finding it in week twenty.
Every product in DevByte’s portfolio — RCM Automation, PharmedPulse, MyLera, Nephrolytics, and the rest — began as a smaller validation build before the full investment was made. This is not an approach we recommend to clients — it is the approach we have used ourselves, for every product we have shipped.
The pattern is consistent: a focused PoC that answers the core feasibility question in 4 to 6 weeks, an MVP that validates the workflow with real users in 8 to 12 weeks, and a production build informed by what was learned at both earlier stages. The production build is faster, cheaper, and more reliable because the architecture decisions were already validated before the full build began.
A focused proof of concept takes 2 to 6 weeks depending on the complexity of the problem and the state of your data. We scope the timeline during discovery — week one of every engagement.
A PoC typically costs between $15,000 and $50,000 depending on scope and data complexity. An MVP runs from $40,000 to $120,000. These are indicative ranges — we provide a specific estimate after the discovery session. We do not quote before understanding the problem.
A specific problem you want to test an AI solution for, and access to the data that would power it — even if that data is messy or incomplete. You do not need a technical team, a detailed specification, or a budget approved beyond the PoC itself.
That is a successful outcome. The PoC answered the question it was built to answer before you committed to a full build. We document exactly what was tested, what the results showed, and what the implications are. Sometimes the PoC reveals that a different approach would work — those findings become the starting point for a redesigned solution.
Yes — and this is often where the most valuable PoCs happen. We assess your data in the discovery phase: quality, volume, structure, and whether it is sufficient to test the AI approach meaningfully.
No. Everything we build and document during the PoC or MVP is yours — the code, the architecture documentation, the evaluation findings, and the roadmap. That said, most clients continue with us because the team that built the PoC already understands the problem and the codebase.