Built for India
The metrics that move Indian margin don't exist in a US data model.
Indian omnichannel margin compounds differently. We modelled that first: pincode, COD, returns, festival, ₹, quick-commerce availability, B2B POs, marketplaces, fiscal targets. Then built the screens on top.
01
What you can't run on a US model.
01Pincode is not a zip code with extra digits.
02COD is not a payment method footnote.
03Returns are not a return.
04Diwali is not Black Friday.
05A Nykaa PO is not a Shopify order.
06A Blinkit dark store is not a fulfilment centre.
02
What's in our model that isn't in theirs.
Two years of semantic-layer work: every concept Indian omnichannel commerce actually runs on, modelled once.
- 01Pincode · A first-class dimension on every Fulfilment query, so the city average never hides the pocket that's costing you.
- 02COD / Prepaid · Treated as a measure on Sales, Fulfilment and Cohort, not a footnote in a settings menu.
- 03Returns & TAT · Returns rate and TAT breach modelled as measures. The line on the P&L finally has a chart behind it.
- 04Festival & occasion · Diwali, Pongal, Onam, EOSS, Rakhi, Republic Day, as a filter on every relevant model.
- 05Lakhs, crores, fiscal April–March · The default. Not a settings override.
- 06Quick-commerce availability · SKU × city × dark store with market data. Across the seven platforms that move Indian quick commerce.
- 07B2B PO accounts · Nykaa, FirstCry, Pepperfry, Reliance, DMart, modelled first-class with value, deadline, fulfilment status.
- 08Marketplace SKU graph · Amazon, Flipkart, Myntra and Ajio joined to your storefront SKU graph.
- 09Planning targets · A measure with variance built in, joined to actuals on every chart.
03
We've built this before.
Our team forked out of Fornax. Based in India. We've built the data backbone for some of the country's fastest-growing brands.
At Fornax, we worked with leading, fast-growing consumer startups, including The Sleep Company, Kapiva and Frido, to build their data foundation.
See your numbers in a model that speaks your stack.
Thirty minutes on your data, or ours. You'll leave with an honest number, not a quote-builder.