Software a business actually runs on.

In private use

Kannuku

The problem

A lending business run on notebooks and one spreadsheet. Interest worked out by hand, monthly, and no dependable answer to the only question that matters: who owes what today.

What I built

A desktop ledger holding parties, loans, deposits, repayment schedules and receipts, with the interest rules encoded exactly as the business actually applies them, including the awkward ones nobody writes down. Cash and account movements stay separate throughout, because conflating them is what made the spreadsheet untrustworthy.

Worth knowing

It needs no internet and no server, because it has to work during a power cut in a town with patchy broadband. The database is encrypted at rest, since it holds other people's financial position. And overdue is a defined rule, not a feeling: unpaid more than ten days after a month closes.

Electron · TypeScript · React · encrypted SQLite · Mac and Windows installers

The interest engine, which is the part that took the longest: what is owed, by whom, for which month. Test workspace, invented names.

In use

Customer analytics

The problem

Years of orders sitting in exports nobody opened. The business knew its revenue and almost nothing else: not who had quietly stopped ordering, not how exposed it was to losing one big account.

What I built

Twenty screens over 11,054 customers and 87,673 orders: retention cohorts, concentration risk, a call list the office works down, geography, product mix, and a data-quality page showing what the numbers cannot be trusted on.

What it found

785 customers, under a fifth of the base, generate 80% of revenue, and no single customer is worth more than 3.6%. That second number is the reassuring one and nobody knew it. It also showed which cohorts stopped reordering and when, which is the question the business had actually been asking out loud.

Python · Streamlit · SQLite · per-user login · one-click launcher

Concentration risk, retention cohorts and product mix. Real data, and these three screens carry no rupee figures or customer names, which is why they are the ones shown.

In use

Map data scraper

The problem

A campaign needed every print operator in a city. Bought lists are stale, mostly wrong and expensive, and the free ones cover a fraction of what is actually out there.

What I built

Point it at any city and any category and it returns a verified list with names, addresses and phone numbers, plotted and filterable. It caches every response so a re-run is free, resumes after interruption, and stops itself at a spend cap. 16,216 shops on this run, across eleven trades. The same tool gives you every dental clinic in Kochi, or every hotel in Coorg.

Worth knowing

It runs on the official Places API, so the data is licensed and the numbers are real. The catch is that a search returns at most sixty results, so an oversized tile silently loses businesses you never learn existed. Tiles size inversely to density, and any query hitting the ceiling is flagged.

Python · Streamlit · Google Places API · SQLite cache · spend cap

Every shop found, coloured by trade, filterable by area and by whether they own a press. The filters are the point: the list changes depending on who you are selling to.