The questions buyers
ask first.

A buying-decision platform for Indian D2C womenswear brands. It pairs your own sales history with the Indian festive calendar[i]The 42 windows in the Indian commercial calendar, current runway[i]The 30 designers tracked across 4 seasons, fashion media, and competitor catalogues[i]The 24+ Indian D2C brands tracked dailyto tell you what to buy in the next 30 / 60 / 90 days, how deep to go on each SKU, and where the season's energy is moving. The buyer briefs are written in the voice of Anaita Verma — the senior-buyer character TrendSense was built around.

Anaita Verma — the senior buyer voice TrendSense was built around. She's a composite character drawn from twelve years of Indian D2C womenswear buying decisions: the ones that worked, the ones that left ₹80L of bandhani in the warehouse. Every brief she writes cites the signals it leaned on[i]How the engine actually works, names the price band, says how many SKUs to load, and explicitly flags what could go wrong. When the data is thin she'll say “early signal — watch, don't load” rather than make something up.

Forty-two windows in the Indian commercial calendar[i]The 42 windows in the Indian commercial calendar (Diwali, Karva Chauth, Eid, Onam, wedding season, EOSS, Republic Day sale, Independence Day sale, etc.) each have category multipliers — “festive 1.6×, party 1.3×, wedding-guest 1.2×” — baked into the forecast at the affected horizon. The lift is broken out per product on the Forecasts dashboard and per category on the Assortment Plan view.

Every account's accuracy is visible on the Forecasts dashboard: average error (Mean Absolute Percentage Error), revenue-weighted error (Weighted Absolute Percentage Error)[i]Buyer terminology, plain English, and what percent of actuals landed inside the 80% prediction band. Below 6 weeks of history the engine falls back to analog-based forecasts — other products in your catalogue that look most like the new SKU. Numbers are directional until 12+ months of operational data accumulate.

The global incumbents (built for European retail) sell a worldview at a per-seat price: their calendars centre Christmas, their fabric coverage is light on silk-blend banarasi and pure georgette, and their runway tagging stops at the four big international weeks. TrendSense is built to sit on the Indian buying floor. The festive calendar drives the forecast horizon. The fabric library was indexed against what Indian D2C brands actually load on[i]The full 247-attribute taxonomy. The runway feed watches the four current Indian designer seasons, not last September in Milan. We are not a cheaper version of the global tools — we are a different tool, made for a different desk.