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See AI Work on Your Own Data Before You Build

The AI PoC (proof of concept) Sprint tests one use case on a sample of your data, against a measure we agree first. Then you decide whether to build or stop.

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An AI PoC Sprint at work: a use case, such as whether AI can answer order questions, is agreed at the discovery call with how success is measured. A sample of your data is shared in an account you control, the sprint builds one proof of concept, such as an agent, an MCP server or drafts your team checks, and tests it on real examples. Each use case ends in go, with a build plan, or stop, keeping what was learned.

Why AI plans stall before the first build

Most teams have a list of AI ideas and cannot say which one would work on their data. A demo on someone else's data proves little, and a full build is a big bet. A proof of concept answers the question on your own data first.

  • “We have a list of AI ideas and no way to choose.”

    We pick one use case with you, the one with a clear measure and data you already hold.

  • “The vendor demo looked great on their data, not ours.”

    The proof of concept runs on a sample of your data, tested on your own real examples.

  • “We cannot commit to a full build on a hunch.”

    You see the result first, then decide to build or stop. You are not committed to a build.

Start with the use case your team would use first

Each sprint tests one use case, the one your team would use first. These are the kinds we build, and the services a good result leads to.

  • An agent that finishes a task

    The AI plans the steps, uses your systems and checks its own work, handing off to a person when it is unsure.

    See AI agent development

  • Claude or ChatGPT on your data

    Your team asks in plain English and the AI app reads the answer through read-only tools you sign off.

    See MCP server development

  • Answers from your own documents

    Questions answered from your manuals, policies or product sheets instead of a search through folders.

  • Drafts your team checks

    Replies, summaries or descriptions drafted by AI and approved by a person before anything is sent.

  • Data out of PDFs and photos

    Fields read from invoices, orders or forms, and checked against what your team would have typed.

A working proof on your data and a clear go or stop

When the sprint ends you have a working proof of concept on your data, its test results and a team that has used it. You also get a clear recommendation to build or stop.

Book a discovery call →

  • An AI strategy note

    A short readiness check of your AI ideas. It ranks the use cases worth testing by value and data, and says why we start with the first.

  • A working proof of concept

    One use case built on a sample of your data.

  • Test results on real examples

    Each answer compared with what your team expects, against the measure we agreed.

  • A data note

    Which model was used, where your data went and what stayed inside.

  • A session for your team

    Hands-on training on what was built and how to use AI tools well at work.

  • Go or stop, with a build plan

    If go, an approach, a rough timeline and a rough number for the build.

Pilot-first

What a PoC Sprint builds on your data

Pilot-first: the sprint is a small test on your own data, priced up front, before any full build. A good result can lead to an AI agent or an MCP server, built in full.

What the sprint builds
One use case, working on a sample of your data, tested on real examples against the measure we agree at the discovery call.
What you get
The proof of concept and its code, the test results, a session for your team, and a recommendation to build or stop.
Book a discovery call

How an AI proof of concept works

An AI proof of concept is a small working version of one AI use case, built on a sample of your real data and tested against a measure you agree in advance. It shows whether the idea works on your data, so the decision to build rests on evidence rather than a demo.

  1. Use case

    We agree one use case and how success is measured.

  2. Sample data

    You share a sample of real data, in an account you control.

  3. Build

    We build the proof of concept on that sample.

  4. Test

    It runs on real examples and is scored against the measure.

  5. Decision

    You decide to build, change course or stop.

PoC run: can AI answer order questions?
  1. Use case “Can AI answer customers' order questions from our own records?”
  2. Use case The measure: answers your team would send as they are.
  3. Sample data A sample of orders and past questions, in an account you control.
  4. Build Looks up the order: status, items and delivery date
  5. Build Drafts a reply from those records, for your team to check
  6. Test Real past questions run through it, each answer compared with the one your team gave.
  7. Decision Go: an approach, a rough timeline and a rough number for the build.

Had the answers fallen short, you would stop there, with the results and what it would take.

An example sprint, step by step. The proof of concept runs apart from your live systems.

Proved on real data, the way we build our own products

We test AI the way we build it for our own products, on real data and against a clear measure. When something does not work, you get an honest answer.

View our work

  1. We ship live AI products

    Gyanee.ai, which we built, and our own Chitra and ProvisCommerce are live on the App Store, Google Play and the web.

  2. Built around your systems

    The sprint runs on a sample of your own data and connects to the tools you already run, so nothing has to be replaced.

  3. Support after launch

    If you build, we monitor the AI after launch, keep it updated and improve it as new models arrive.

  4. Hours that overlap yours

    We work on India Standard Time (IST): 4.5 to 5.5 hours ahead of London and 9.5 to 10.5 ahead of New York.

Real data, shared on terms you set before we start

A proof of concept needs real data to mean anything, so before we start we agree what is shared, where it goes and who can see it. These are the rules on every sprint.

On every PoC Sprint

  • We work only in the accounts you invite us into, with an NDA first on request.
  • You choose where data goes: a hosted model, or an open model on your own servers.
  • A sample is enough, and the proof of concept runs apart from your live systems.
  • You own the code and the results, and can stop after the sprint.

One use case, one data sample, and the choice to stop

Each sprint covers one use case, a sample of your data and a measure agreed up front. We agree the scope and timeline at the discovery call, and you can stop after the sprint.

Book a discovery call

The models and tools a PoC Sprint uses

Standard, widely used tools, so another team could take the code over. We pick the model for the use case, on answer quality, cost and where your data may go, and build the proof of concept so a full build can grow from it.

  1. Models

    OpenAI and Anthropic Claude, and open models on Ollama when data has to stay on your servers.

    • Anthropic Claude
    • OpenAI
    • Ollama

  2. Backend and data

    FastAPI on Python, PostgreSQL with pgvector.

    • FastAPI
    • Python
    • PostgreSQL

  3. Connections

    MCP servers, which let AI apps read your systems, and n8n workflows, which connect your apps, when the use case needs your systems.

    • MCP
    • n8n

  4. Your data

    A sample from your store, CRM, helpdesk, documents or database.

Provis Technologies AI Integration services are phenomenal. We have been witnessing growth after the AI automation they have put into our business.

Madhup Bansal CEO & Co-Founder, TrueWholesale TrueWholesale

Bring us your list of AI ideas.

Tell us the AI ideas on your list and the data you hold. We will help you pick the one to test first, and how to measure it.

Book a discovery call WhatsApp
  • Gyanee.ai
  • Chitra
  • ProvisCommerce
  • TrueWholesale
Vivek Sharma, CEO and Founder of Provis Technologies

Vivek SharmaCEO & Founder, Provis Technologies

Questions about the AI PoC Sprint

What is an AI proof of concept?

A small working version of one AI use case, built on a sample of your real data and tested against a measure you agree in advance. It shows whether the idea works on your data before you pay for a full build, and what a build would take if it does.

What is in the sprint, and what is not?

In: one use case, a short AI strategy note, a working proof of concept on a sample of your data, test results, a data note and a session for your team. Not in: a production build, connections to your live systems, or support after launch. Those come with a build, if you decide on one.

How long does the sprint take?

It depends on the use case and how ready the data sample is. We agree the scope and timeline at the discovery call, before anything starts, so you know when to expect the result.

What does a PoC Sprint cost?

It depends on the use case and the data involved. Tell us what you want AI to do, and within two working days we come back with an approach, a rough timeline and a rough number. The sprint is priced up front.

What if the proof of concept does not work?

You stop there and keep what was learned: what worked, what did not, and what it would take. You are not committed to a build.

What do you need from us?

Someone who knows the task, a sample of real data in an account you control, and a few real examples with the results your team would expect. We tell you at the discovery call exactly what the sample should hold.

Where does our data go?

Your choice. With hosted models from OpenAI or Anthropic, data is processed by that provider. With open models on your own servers or cloud, documents and prompts stay inside your network. The data note says which was used.

Who owns the code and the results?

You do. You keep the proof of concept, its code, the test results and the build plan, whether you build with us, with another team or not at all.

What does the AI strategy part cover?

The AI strategy note, a short readiness check before the build. We list the AI ideas on your list, rank them by value, the data you hold and how clearly success can be measured, and agree which one the sprint tests. You keep the ranked list.

What training does our team get?

A hands-on session with the people who will use the result: how the proof of concept works, where it is reliable and where it needs a person, and how to use AI tools well and safely at work.

What happens after a successful sprint?

You get an approach, a rough timeline and a rough number for the full build. That may be an AI agent or an MCP server on your systems. Each step is priced up front, and you decide whether to go ahead.

Will you sign an NDA, and do you work with UK and US teams?

Yes, an NDA before we start, on request, and we work only in the accounts you invite us into. The sprint runs in an account you control, and you own the code and the test results it produces.

Work with experts

Built for teams that want proof before a build

The AI PoC Sprint tests one use case on a sample of your data, so you decide to build or stop on evidence.

Prefer to talk directly?

We're on IST: 4.5 to 5.5 hours ahead of London, 9.5 to 10.5 ahead of New York.

  1. 1 The use case
  2. 2 Your details
  3. 3 Additional info

Tell us what you want AI to do

Describe the use case, the data you hold and how you would know it worked.

The use case
Your details
Additional info

We reply within two working days with an approach, a rough timeline and a rough number. No charge, no commitment.

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Prefer to talk directly?

We're on IST: 4.5 to 5.5 hours ahead of London, 9.5 to 10.5 ahead of New York.