AI software development

Four things, reliably. It reads your paperwork — invoices, purchase orders, KYC sets, reports — and enters the data instead of a person retyping it. It answers the questions your team answers all day, on WhatsApp or your website, from your live data. It predicts numbers you currently estimate, like what to stock or which receivable is about to go bad. And it does the coordination work between steps: reading an email and creating the order, matching a payment to an invoice, routing an approval to the right person.

What it does not do is run your business or replace a system you do not have. If your process is still on paper, the first step is usually software, not AI.

Three questions. Is the task repetitive and high volume? Is the information already in a digital form, or capturable as one? And can you live with it being right most of the time rather than always, with a person checking the rest?

If all three are yes, it is usually a fit. If the third is a no — anything where a single error is unacceptable and undetectable — then AI belongs in a supporting role at most. We will tell you which of your processes fall on which side before you commit to anything.

One narrow process, measured. Pick the task that is highest volume and most repetitive — usually document entry or first-line customer questions — and run it on your real material alongside the existing manual process for a few weeks. You get an accuracy figure from your own data rather than a claim from a vendor, and the cost of finding out is small.

What we would advise against is an open-ended “AI transformation” programme. Those are expensive, slow to show anything, and usually end with a pilot nobody deployed.

Neither, and the distinction matters. We do not train our own models — almost nobody outside a handful of large labs does, and any Indian software company claiming otherwise is worth questioning. We build on OpenAI’s API.

What is ours is everything around it: how your documents and data are prepared and fed in, how the output is validated before it reaches your system, what happens to the cases the model is unsure about, and how the whole thing connects to the software you actually run. That application layer is where an AI project succeeds or fails, and it is the part we build.

Nobody honest will quote you a single accuracy figure before seeing your actual documents and data. What we do instead is measure it on your real material during a pilot, and tell you the number we got.

The design matters more than the number. Every system we build sets a confidence threshold: anything below it goes to a review queue for a person rather than being posted silently. Anything touching money gets checked. You will always be able to see what the system decided, what it was unsure about, and what a person changed.

AI processing is performed through OpenAI’s API. Your data is sent to that API for the specific task, and the result comes back into your system.

Before any project involving your data begins, we confirm the current provider data-handling terms in writing — retention, whether anything may be used for model training, and where processing takes place — so that it forms part of your agreement rather than an assurance in a sales call. If those terms do not work for your business, the honest answer is that this is not the right project for you, and we will say so at that stage rather than later.

For document reading and for agents, no — those work on documents and conversations as they arrive.

For forecasting and scoring, yes, and this is where we would rather lose the project than take it. Prediction needs one to two years of reasonably consistent transaction history. If your data is scattered across systems, full of duplicates, or was entered inconsistently, the honest first step is fixing that. A forecast built on bad history is worse than no forecast, because people act on it.