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.

Your last 180+ days of orders from your connected store, plus curated competitor catalogues from 24+ Indian D2C brands, runway lookbooks from 30 Indian designers across the four current seasons, fashion-media headlines, Pantone announcements, and search-interest trends. The forecast engine blends statistical and machine-learning models and reports its own accuracy[i]How the engine actually works — measured against historical data — so you can see exactly how trustworthy it is for your catalogue.

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.

Shopify is the only first-party integration today. WooCommerce and Magento connectors are on the roadmap; CSV upload is available on the Growth and Scale plans. The taxonomy, trend signals, and sustainability scoring all work standalone without your sales history — but the per-product forecast needs it.

Starter caps at 50 active SKUs and 1 store. Growth removes the SKU cap and lifts the store cap to 3. Scale lifts to 10 stores. We don't price per seat or per API call — the forecast engine, calendar, taxonomy, and narratives are all included on every tier. Tier differences are about catalogue size, refresh cadence, and whether you can customise competitor and designer rosters.

Yes. The data-sharing programme lets you toggle four categories — aggregated sell-through, category velocities, size curves, returns. Enable one or two and you drop to the standard tier with a small discount plus access to peer aggregates. Enable three or four and you drop to premium with a bigger discount plus the full anonymised peer dataset. All shared data is aggregated and anonymised before any other merchant sees it.

Forty fibres rated on global-warming potential, water use, eutrophication, chemistry, and fossil-resource burden — an open mirror of the Higg Materials Sustainability Index, normalised from publicly available life-cycle assessments (Textile Exchange, EU PEFCR, Indian academic LCAs). Every fabric attribute page shows the score with sub-scores and cited sources. Useful for buyer orientation, but not for verified ESG reporting — for that you need a licensed Higg account directly.

Monthly billing, no annual lock-in. You can cancel from your account settings — access continues until the end of the paid period. On termination we stop new charges, export your data on request within 30 days, and delete it within 60 days except where retention is legally required (tax invoices: 7 years).

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.