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A full AI build is months and a large budget. An AI proof of concept tests whether the idea actually works — on your real data, against criteria you agree upfront — in two to six weeks and a fraction of the cost. You get a clear go/no-go, a cost-to-scale, and a prototype that becomes the build if the answer is go.
Enterprises, SMEs and fast-growing teams trust ZTS India
Most AI initiatives fail not on unclear use cases, poor data readiness and lack of validation. A proof of concept answers those questions cheaply, before the real money is committed. Click a panel to see the challenge, how we fix it, and what changes.
Leadership wants a move on AI, a dozen ideas are floating around, and not one of them has been tested against your actual data. Committing a full build budget to an unproven use case is how six-figure write-offs happen.
The last PoC was built to impress in a meeting — no evaluation, no real data, no path to production. It won the room, then had to be thrown away and rebuilt from scratch when the real project started.
A full AI build is months and a large budget. Starting one without validating feasibility, data readiness and ROI first means the risk lands entirely after the money is committed — when it is hardest to stop.
The prototype succeeded and momentum died anyway — no scaling plan, no cost model, no architecture for production, no idea what to do on Monday. A successful experiment that led nowhere.
The use case sounds great, but nobody knows whether your data is clean enough, complete enough or well-labelled enough to make it real. Find that out three months into a build and the build stops.
From idea to validated AI system, we help you test, measure and de-risk before making a full-scale investment — so every proof of concept answers a real question with evidence.
Before any code, we pressure-test the idea itself — is it feasible, is the data there, is the ROI real, and is AI even the right tool? Many use cases are validated or killed on paper, cheaply.
A time-boxed prototype built on your real data and production-shaped architecture, tested against the success criteria we agreed — so the result is evidence, not a sales demo.
Validate an LLM idea — a copilot, a RAG assistant, an agent — with grounding, evaluation and a real token-cost estimate, so you know if it holds up before committing to a product.
Prove an agent can complete your multi-step task safely before you wire it into production — with the tool access, guardrails and approval gates a real deployment would need.
Test whether a predictive or classification model can hit the accuracy your business case needs — on your data, benchmarked honestly against a simple baseline.
Validate a detection, inspection or OCR idea on your real images before investing in a full vision system — including whether edge deployment and latency targets are realistic.
Turn a successful PoC into a plan you can fund — production architecture, a cost-to-scale model, a phased delivery roadmap and the risks to manage on the way.
A fixed-scope sprint to put something interactive in front of stakeholders fast — enough to test appetite, gather feedback and decide whether to invest, in weeks not quarters.
More than a decade delivering measurable results for enterprises, SMEs and technology companies worldwide.
Three ideas that a short, evidence-based PoC either funded — or stopped.
Challenge: A health provider had two AI ideas competing for the same budget and no way to choose between them.
Solution: Two parallel two-week PoCs on real data with shared success criteria. One cleared the accuracy bar comfortably; the other exposed a data gap that would have sunk a full build.
Challenge: The team wanted an agent to action routine requests but risk and compliance would not approve an unproven system touching live accounts.
Solution: A sandboxed PoC with scoped tools, approval gates and a full action log, evaluated against adversarial cases — evidence risk could actually review.
Challenge: A defect-detection concept looked promising in theory, but nobody knew if it would work on the factory's actual images and latency budget.
Solution: A three-week PoC on real line images measuring detection accuracy and per-unit latency, plus an honest edge-deployment feasibility view.
Get a free 30-minute scoping call. We'll tell you whether it's PoC-ready, what a PoC would test, and roughly what it would take to answer — no pitch.
We test the technique that fits your problem — often proving the cheaper, simpler approach works before anyone commits to the expensive one.
Copilots, RAG assistants and agents — validated for grounding, accuracy and token cost.
Forecasting, scoring and classification tested against your data and a real baseline.
Detection, OCR and inspection prototypes measured on your real images.
Extraction, classification and sentiment validated on your documents.
Multi-step, tool-using agents proven safe before any production wiring.
Document processing and workflow automation feasibility on real inputs.
Personalisation and ranking ideas tested against your catalogue and behaviour.
Prediction plus rules and optimisation, validated on a real decision.
A fast, outcome-driven framework to validate feasibility, reduce risk and move from idea to decision in weeks — with a clear answer at the end, whichever way it points.
Agree the use case, the one question the PoC must answer, and what success looks like.
Check data readiness in week one — what exists and what it can honestly support.
Define scope, success criteria, architecture and evaluation before any building starts.
Build the PoC on production-shaped architecture using your real data.
Measure against the agreed criteria — accuracy, cost, latency, feasibility, fit.
Put it in front of real users where fit matters, and capture structured feedback.
A clear, evidence-based go / iterate / stop, with the reasoning shown.
If it is a go: production architecture, cost-to-scale and a phased build plan.
Book a free, no-obligation consultation. We'll pressure-test the idea, tell you honestly whether it's worth a PoC, and give you a fixed scope, timeline and cost to find out.
The depth to validate an idea properly — and to know which things a PoC must test because they are what decide whether the full build succeeds.
RAG, copilots and agents validated for grounding, accuracy, guardrails and real running cost.
Tool use, planning and safety — proving an agent can finish a task before it touches production.
Feasibility and honest benchmarking of predictive and classification models on your data.
Detection, OCR and inspection prototypes measured on real images and real latency budgets.
Fast, honest evaluation of whether your data can support the idea — and what to fix if not.
Confirming the PoC can reach the CRM, ERP and legacy systems production would depend on.
Golden test sets, success criteria and automated scoring, so results are evidence not opinion.
Production-shaped PoCs and realistic cost-to-scale estimates on AWS, GCP and Azure.
Turning a working prototype into a defensible number for running it at scale.
A rapid-prototyping stack that gets a credible PoC in front of your stakeholders fast — without cutting the corners that would make the result meaningless.
We validate ideas across industries, shaped around your data, your workflows and your compliance reality.
A PoC is where AI projects should start. When yours gets the green light, here's what comes next.
Readiness assessment, use-case prioritisation and a costed roadmap.
Bespoke ML, vision and decision systems trained on your data.
Custom LLM apps, RAG systems, chatbots and AI agents.
Production-grade LLM, RAG and agent systems.
Connect AI to your CRM, ERP and existing workflows.
Automate the workflows RPA cannot handle.
The reason clients trust us with the decision, not just the build.
"ZTS successfully built a well-designed website. The communication and effectiveness were great."
"I have to thank both Sharmila and Sourav for all their timely help in developing a World Class Website they were always ready to guide and help me and their support is really appreciated"
"Thanks to Zebra’s expertise Shopify developer who built my Shopify store. The user interface for managing tools is clear and concise, and easily accessible from the control panel. Also, they seamlessly transfered my old site from WIX to Shopify. I highly recommend ..."
"Thanks to ZTS developers, highly experienced and skilled to develop web applications with API integrations. I was expecting user-friendly options for both web app development and UI/UX designs and they provided me with that. And, the most impressive thing I found is ..."
They delivered beyond expectations — our project saw real growth thanks to ZTS Infotech's passion and commitment
Ms. Shikha Bhatia shares her positive experience with Zebra Techies Solution, where she talks about how our Organic Social Media Management Services helped her brand achieve consistent engagement and authentic growth!
They went above and beyond to support us during challenging times. The ZTS Infotech team delivered with dedication, professionalism, and care — truly a partner we can count on!
They helped us turn hurdles into wins — Sam's e-commerce transformed thanks to ZTS's committed support
Outstanding collaboration and flawless execution — ZTS Infotech made the entire process smooth and truly result-driven!
A partner that tells you the truth about your idea — cheaply, quickly, and with the evidence to back it.
We build PoCs to answer a question honestly — including when the honest answer is don't build it. That is the point of a PoC.
Tested on your data against criteria agreed upfront, so the result is evidence your board can act on — not a staged demo.
Production-shaped architecture means a successful PoC is a running start, not a throwaway you rebuild from scratch.
Every PoC ends with a real number for running it at scale, so the go/no-go is made on economics, not enthusiasm.
Time-boxed, fixed-cost engagements — you learn whether it works while walking away is still cheap.
We know what production takes, so our PoCs test the things that actually decide whether a build succeeds.
Tell us the idea you're weighing up. We'll come back with an honest view of whether it's worth a PoC, what one would prove, and a fixed cost to find out — free.
A small, time-boxed project that tests whether an AI idea is feasible on your data before you commit to a full build. It answers one question with evidence — will this work well enough to be worth building? — covering feasibility, data readiness, accuracy, cost and fit, so a go/no-go decision rests on facts rather than optimism.
A build assumes the idea works and delivers it to production. A PoC tests that assumption first, cheaply. Skipping it means the risk — feasibility, data, cost, adoption — only surfaces after months and a large budget are committed. The PoC front-loads that risk into a small, fixed-cost engagement while stopping is still easy.
Most take two to six weeks depending on complexity and data readiness. A focused ML or LLM feasibility test can be two weeks; a multi-system agent PoC takes longer. We agree the timeline and success criteria before starting, so you know exactly what you will get and when.
A clear go / iterate / stop recommendation backed by evidence: what we proved against the agreed criteria, the results on your real data, a production architecture if it is a go, a cost-to-scale estimate and a phased roadmap. And because it is built production-shaped, the PoC itself is a head start on the build rather than a throwaway.
Then it did its job — and it is the cheapest possible way to find out. Discovering a use case is not viable after a two-week PoC costs a fraction of discovering it after a six-month build. We will show you exactly why it failed, whether a different approach could work, and you will have lost weeks, not quarters.
Yes, and that is deliberate. We build PoCs on production-shaped architecture with real data, so a successful one is the foundation of the build rather than a disposable demo. It is one of the main reasons our PoCs are worth more than a quick prototype that has to be thrown away.
Not necessarily — assessing your data is part of the PoC, and often the most valuable part. If the data is not ready, we tell you in week one rather than three months into a build, along with exactly what would need fixing to make the idea viable.
Generative AI and LLM ideas (copilots, RAG, agents), machine learning and predictive models, computer vision, NLP, intelligent automation and decision systems. If you are not sure which technique fits, that is itself something the discovery stage answers.
If you want us to — we can take a validated PoC through to a production system, since it is built on architecture designed to scale. But there is no lock-in: the code and findings are yours, and you are free to build it in-house or elsewhere.
It is a small, fixed-price engagement — far less than a full build, and structured so the cost is capped and known upfront. The exact figure depends on scope and complexity, which we agree after a free discovery call. The point is to keep validation cheap so the big decision is well-informed.
