The questions you are actually thinking.

GrayMatter Technologies is a software and applied AI company in Hyderabad, India, founded by Sudheer Akella. It builds prototype-first: you describe a problem, see a working prototype you can click within two to three hours at no charge, and pay only for the production build you approve. It also sells ready-made products: the GrayMatter Intelligence Platform, business applications such as CRM, HRMS and procurement, and education technology such as GrayCampus and GrayLearn.

GrayMatter Technologies works from CoKarma, 5th Floor, 192, Masjid Banda Road, Masjid Banda, Camelot Layout, Kondapur, Hyderabad, Telangana 500084, India. Hours are Monday to Friday, 9:00 AM to 6:00 PM. GrayMatter is also a registered company in Cape Town, South Africa, for client work and partnerships there. The easiest way to reach us is the enquiry form on the Build my prototype page, or info@graymattertechnologies.co.in.

Something you can click. Real screens, your workflow, your terminology, enough working logic to test whether the idea holds. It is deliberately not the production application: it has sample data rather than your live systems, and it proves the shape of the thing rather than every edge case. You use it, you tell us what is wrong, and that feedback is what the build is scoped from.

Yes. The first prototype costs you nothing, and so does the conversation before it. You pay only when you approve the prototype and we build the production application, which is fixed-scope and fixed-price and runs to the 7-Day Build. If the prototype shows the idea was wrong, you have lost a coffee and gained an answer.

Then it has done its job at the cost of days instead of a year. That happens, and it is one of the most useful outcomes we can give you. You keep what was learned, we say plainly what we would do instead, and nobody has built the wrong application.

You should weigh it honestly. What you get in exchange is the founder on your problem rather than a delivery team three layers down, a scope small enough that being wrong is cheap, and influence over what gets built. What you do not get is a track record. If your procurement process requires three reference customers and a certification pack, we are not the right vendor for you this year, and we will say so on the first call rather than waste your time.

This is the right question and it deserves a contractual answer, not a reassurance. Our position: your data stays exportable in open formats at any time without asking us, the pilot is scoped so nothing critical depends on us within the first engagement, and exit terms are written into the agreement before work starts. For anything beyond a pilot we will discuss source escrow. If a vendor cannot tell you how to leave them, that is information about the vendor.

Messier than you think, and less messy than we would like. Inconsistent naming, gaps, duplicates and a decade of spreadsheet conventions are all normal and workable. What genuinely blocks a forecast is not having enough history for the thing you want to predict, or not recording the outcome you care about anywhere at all. We will tell you which of those you have in the first assessment, before you spend anything.

The first conversation is 30 minutes and costs nothing. Two to three hours after it, you have a prototype you can click. The build that follows runs to the 7-Day Build against one agreed success measure. We would rather cut the scope than extend the timeline, because a build that runs for six months has stopped being a build.

Not to start. Most assessments and early pilots run on exports or a read-only replica, which keeps your risk and your IT team's workload low. Write access to any system is a separate, later conversation with its own approvals, and it is never required for the predict and decide stages.

You own your data and the outputs produced from it. We do not train shared or public models on your information. Any model built specifically for your problem is covered in the agreement, and we will not claim ownership of something derived entirely from your operational history.

You may not. Business intelligence answers what happened and, done well, it answers it beautifully. If your problem is that nobody can see the numbers, buy a better dashboard. Our argument starts one step later: you can see the number, it is still taking three weeks to act on it, and that gap is costing you. If that is not your problem, we are not your purchase.

Pilots are fixed-scope and quoted against the specific problem, because a two-week forecasting pilot on clean data and a six-week one spanning four systems are not the same piece of work. We will give you a number before you commit anything, and we will tell you on the assessment call if we think the return does not justify it.

Less than a transformation programme and more than nothing. Realistically: someone who can explain how the process actually works rather than how the manual says it works, someone who can get us access to the data, and a decision-maker who will look at the output and say whether it is useful. A few hours a week, concentrated at the start.

Then it has done its job, at the cost of weeks instead of a year. We agree the success measure before we start precisely so that failure is legible rather than arguable. We would rather tell you that a problem is not worth solving with AI than sell you the next phase of something that is not working.

No, and we are not going to imply otherwise. We build to the practices those standards describe — least-privilege access, encryption in transit and at rest, audit logging, data minimisation — and we will pursue formal certification as engagements require it. If certification is a hard procurement gate for you today, we will not pass it.

We go deep in education and healthcare, because domain knowledge changes the answer in both and because the founder has operated in regulated, data-sensitive environments. The underlying approach is not industry-specific, so we will take on problems outside those two where the data and the decision are well defined, and we will tell you when a problem sits outside what we understand.

For anything consequential, yes, and that is a design position rather than a limitation we intend to remove. The system recommends and a named person approves, and the approval is recorded against everything that follows it. Anything touching clinical or student outcomes is advisory to a qualified professional and never a substitute for one.