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AI · Consulting & strategy · AI proof of concept

Your AI proof of concept passes the demo and fails the real data two weeks later

A proof of concept that only passes on a curated sample is an expensive way to get the wrong answer. Banao designs AI PoCs to test the assumptions a build actually depends on — the messy data, the difficult integration, the accuracy bar your operation needs — before the full development budget is committed.

We run the same discipline on our own AI decisions: InterviewGod and Vikaas each started as an internal PoC that had to prove it could perform on the cases a demo would never show. The proof either earned the budget or killed the idea, and both outcomes saved money.

Banao— InterviewGod passed its internal PoC on Banao's own hiring queue, not a curated sample. That is the bar we set.

What a Banao AI proof of concept covers

A PoC is only valuable if it tests what the build depends on. Ours is designed from the start to answer whether to proceed — and what the build will actually take.

Hypothesis definition and scope lock

We pin the PoC to the one question the budget decision depends on, with a clear success bar, before any model work starts — so the proof answers that question rather than a more comfortable one.

Data audit and preparation

We audit the data the PoC will run against and identify the gaps, the quality issues, and the integration steps before we start — because a proof on cleaned, pre-selected data proves nothing about production.

Model selection and baseline

We choose the model and approach that fits the task, establish a baseline your team can interpret, and document the choice — so the PoC result is comparable and the full build starts with the right foundation.

Hard-case testing, not cherry-picked demos

We test the PoC against the inputs most likely to break it: ambiguous cases, noisy data, edge inputs, and the records a demo would filter out. Those are the ones that decide whether the system survives a week in production.

Integration feasibility test

Where the build needs to reach your systems — CRM, ERP, internal APIs — we test the actual connection and confirm the data path works, not just that it could work on paper.

Cost and performance modelling

We model the inference cost, latency, and infrastructure overhead the full build will carry, so the business case in the PoC report reflects what the system actually costs to run, not a benchmark on a free tier.

Go/no-go recommendation with build roadmap

The PoC closes with a clear recommendation — proceed, redesign, or stop — and, if proceed, a scoped build roadmap with the architecture, team, and cost your decision-makers need to commit.

Why most AI PoCs tell you the wrong thing

A proof of concept is supposed to answer one question: does this idea hold up under the conditions the production system will face? Most don't answer that, because they are designed to pass, not to test. The data is pre-selected. The cases are the ones that worked in the playground. The integration is stubbed. The accuracy bar is whatever the demo hit.

The result is a PoC that passes and a production system that fails three weeks after launch — on the noisy inputs, the missing fields, the integration that turned out to need a ticket queue. By then the budget is committed and the options are narrow. The correct time to find this out is during the proof, when changing direction costs two weeks, not after the build, when it costs six months.

Prove the assumption, not the outcome

We design the PoC around the single hardest question the build depends on — not the one most likely to give a good number — so the result is informative whether it passes or fails.

Test what production actually looks like

We use your real data, including the records with missing fields and ambiguous values, and test against the edge cases a live system will face in the first week, not the curated sample that makes the model look good.

A no is as valuable as a yes

We report honestly when a PoC doesn't clear the bar. An early stop saves the full build budget and redirects it to an idea that will pay — that is the outcome the PoC is there to enable.

From proof to a build your team can act on

The PoC is not the end of the decision process — it is the input to it. A proof that shows the idea holds up needs to convert into a scoped build plan, a real cost model, and a recommendation the people holding the budget can act on. Without that conversion, a successful proof still stalls.

We hand over a PoC report with a clear go/no-go, a build roadmap scoped from what we learned during the proof, and ROI maths that update to reflect actual performance rather than projections. The recommendation is written for your engineers and your board, and Banao's ~300-person bench can begin the build in weeks if you decide to proceed.

The build roadmap is scoped from the proof

Architecture decisions, team size, integration sequencing, and timeline are set after the PoC runs — not before — so the plan accounts for what we actually found, not what the brief assumed.

ROI maths grounded in measured performance

We update the business case to reflect the accuracy, throughput, and cost the PoC measured, so the number your CFO sees reflects a result you watched, not a projection made before any code ran.

Ready for a build — or a board presentation

The deliverable works either way: your engineers can start the build from it, and your board can fund the build from it. Both audiences get what they need from one set of outputs.

Proofs that gave honest answers

Metrics dotted (··) are being verified in our case-study metrics pack — published once confirmed. The decisions behind them are real.

Banao — internal AI PoC

Two internal PoCs that either earned the build budget or stopped spending before the build started

  • ··internal AI ideas taken to PoC
  • ··funded for full build after the proof

InterviewGod and Vikaas each started as a Banao-internal PoC run against the actual data the production system would face — Banao's own hiring queue and demand generation pipeline. Both passed the proof on the hard cases. The ideas that didn't clear the bar were stopped at PoC, before a full build was funded.

RAK Ceramics

A computer vision PoC on real production line footage before any capital was committed

  • ··wksfrom scoping to working proof
  • ··%inspection coverage on the system that followed

We ran the computer vision proof against actual RAK Ceramics production footage, including the lighting and motion conditions present at scale — not a studio sample. The proof passed on the hard material; the full build followed and now runs on the production line.

Enterprise firm (anonymized)

A document processing PoC that revealed the integration gap before the contract was signed

  • ··wksbefore the integration issue would have surfaced in a build
  • ··document types tested; three failed the initial pass

A document classification PoC surfaced that three of the document types the brief assumed were machine-readable required a pre-processing step the original scope didn't include. Finding that in the proof — not six weeks into the build — let the client rescope the contract before any money was committed to the wrong solution.

We proof our own AI decisions the same way

Banao has run an internal AI PoC for every system it now depends on. InterviewGod and Vikaas didn't get funded because the idea was convincing on a whiteboard — they got funded because a short proof on Banao's real data showed they could perform on the inputs that matter. The ideas that didn't clear that bar were stopped early.

Running that same discipline on a 300-person engineering operation means the approach has been tested on the messy, ambiguous data of a real business, not a controlled demo. When we run a PoC for you, we apply the same standard we apply when it is our own hiring pipeline or demand engine on the line.

  • InterviewGodPassed an internal PoC on Banao's own application data before the build was funded.
  • VikaasPassed an internal PoC on Banao's own demand pipeline before the build was funded.

Where we deliver AI proof of concept work

India

Bangalore and Chandigarh hold the engineering bench, so a PoC can start within days of scoping and run on your real systems without a long ramp. Under the DPDP Act we design data handling into the proof from the start.

UAE & GCC

From Dubai we deliver PoCs for enterprises across the free zones and the wider Gulf, with data kept inside UAE and GCC boundaries where the PDPL and client policy require it. RAK Ceramics is a live example of that delivery path.

US & UK

For US and UK enterprise clients we design PoCs to the SOC 2 and UK GDPR controls their procurement teams now ask for before any data leaves the firewall — the proof and the compliance story are built together, not sequenced.

When a PoC is the wrong next step

A PoC is not always the right answer. We will tell you before you book one:

  • The idea is already well-evidenced: if the same workflow has been proven in a comparable context at comparable scale, a PoC is paying to confirm what is already known. Go straight to a scoped build.
  • The data isn't available yet: a PoC on data you don't have access to produces a result about the data you curated, not about your operation. Fix data access first; the proof will cost less and mean more.
  • The organisation isn't ready to act on the answer: a PoC that gives a go/no-go only helps if someone has the authority and budget to act on it. If the decision-maker isn't in the room, wait until they are.
  • The scope is too large for a proof: if the brief requires every integration and every edge case before anyone will believe the result, that is a build, not a PoC. We will scope it down to the one question that matters.

How to run a PoC that earns its conclusions

We don't design PoCs off a brief. We start by pinning the question the proof has to answer.

  1. AI Discovery Sprint2 weeks · fixed price

    We scope the hypothesis, audit the data, run the proof on your hardest cases, and hand back a go/no-go with a build roadmap and ROI maths — yours to keep either way. If you proceed to the build, the Sprint cost is credited against it.

  2. Build

    If the proof passes, we build from it — using the architecture, data pipelines, and integration design the PoC established, so the production system continues the proof rather than restarting it.

  3. Production & evaluation

    We ship behind approval gates with an eval suite drawn from the PoC cases, widen autonomy as the numbers allow, and keep measuring against the bar the proof set.

Frequently asked questions

An AI PoC is a short, scoped build designed to test the assumption the full build depends on. It should answer one question — does this approach work on the data and conditions the production system will face — and give a clear go/no-go with enough evidence to justify a build decision. If it answers a different question, or passes only on curated data, it is not doing its job.

A well-scoped PoC runs in two weeks — enough to test the hypothesis, work the hard data, and produce a build recommendation. Longer proofs often signal a scope that is too wide and should be narrowed to the single hardest assumption before the clock starts.

Real data — specifically the records that represent the worst cases the production system will face, not the cleanest ones. We run a data audit at the start to identify gaps and quality issues, because a PoC on pre-selected data gives a number that will not hold in production. If your data isn't ready, the first job is access and preparation, not model work.

A PoC tests one assumption — does this approach work at all, on real data, under production conditions? A pilot tests the workflow in a limited production context with real users. An MVP is a minimal but deployable version of the full system. Banao's Discovery Sprint delivers the PoC and the build plan to get to an MVP; how far to go depends on what the proof finds.

You keep the report, the data audit, and the analysis — all useful for the next attempt or for explaining the decision to stakeholders. A failed PoC has saved the full build budget and pointed toward a better-scoped idea. We consider that a good outcome and will tell you what would need to change for a re-run to have a better chance.

Yes. We test the actual integration path with your CRM, ERP, databases, or APIs as they are, and we design the proof to answer whether the integration is feasible in your environment. If it isn't, that is the result — and you know before you fund a build that would have stalled on the same problem.

Our Discovery Sprint is a fixed price for a two-week PoC, a go/no-go recommendation, a scoped build roadmap, and ROI maths. The fixed price means your stakeholders know the cost upfront and the proof has a deadline that keeps it honest. If you proceed to the build, the Sprint cost is credited against it.

The proof is designed so the build continues from it rather than restarting. The architecture choices, integration decisions, and eval cases from the PoC become the foundation of the build — so the production system is built on what we learned, not on what the brief assumed. Banao's bench can start the build in weeks if the proof passes.

Put the hardest part of your AI idea in front of us

Bring the use case, the data situation, and the assumptions you're least certain about. In 45 minutes we'll tell you what a proof would need to test and how long it would take to get a real answer.

Book a 45-min scoping call