Most software gets built by people who read about a problem. This one got built because one of us was living it, and the other had spent close to a decade learning how to answer exactly that kind of question.
The problem Kumar Chaitanya lived
Kumar Chaitanya comes from pharma distribution. The business ran on a CRM with reporting built in, which is true of most small distributors. It could tell you what you sold. It could not tell you which products were quietly losing money, which accounts you had become dangerously dependent on, or which stock was going to expire before it moved.
Those answers existed. They were sitting in the exports the whole time. Getting them out needed either the technical skill to do it yourself, which was not realistic alongside actually running the business, or an analyst, which at small business scale is simply not affordable. So the questions went unanswered, and the opportunities in that data went unclaimed. Not through carelessness, but because there was no reasonable way to reach them.
That is also why this product does things a generic chart tool does not. Expiry risk and first-expiry-first-out ordering are in Dashlytics because one of the people building it has watched stock expire on a shelf while the revenue chart looked perfectly healthy.
The problem Nagajyothi Prakash kept solving
Nagajyothi Prakash came at it from the opposite direction. A master's in aeronautical engineering, then a pull toward industrial engineering during that study, which is where the work turns into reading a lot of data and finding what it is actually telling you. A data science program followed, and after that one form of data work or another, continuously.
Seen from that side, the pattern is hard to unsee. The analysis that small businesses need is not exotic. Margin by product, concentration risk, what is moving and what has stopped, what your acquisition actually costs. It is standard work. The gap is not difficulty. The gap is access.
Why the existing tools did not close it
Spreadsheets can do it, if you have the hours and the skill. Enterprise BI can do it, if you model the data first and learn the tool. Both ask you to become someone you were not trying to become.
The newer AI tools ask less of you and give back something worse: a confident number that can simply be wrong. For a report you are taking to a bank, a board or a supplier, that is the single most damaging property a tool can have.
The rule we built around
The engine does the arithmetic. The model only writes the explanation. Every figure in Dashlytics is computed from your rows, so you can trace any number back to your own file and it will reconcile. AI writes the summary of what those computed numbers mean, and nothing else.
And because real exports are messy, every dashboard carries a Data Readiness score: a plain audit of how complete and consistent your file was, shown next to the results rather than buried. It is how the product avoids bluffing. A dashboard that is quietly wrong is worse than no dashboard, because you act on it.
Where we are now
We are early, and we would rather say so than dress it up. There is no wall of customer logos on this page because it would not be true yet. What we can offer instead is the thing a good analyst offers on day one: showing the work, being precise about what the data supports, and being equally clear about how it is calculated and where it should not be trusted.
Who we are
Dashlytics is operated by Sentrix Intelligence Private Limited.
Built for the businesses that have the data and were never going to have the analyst.