City-by-city restock plans for D2C brands selling on quick-commerce apps
TL;DR Fillcast is planning software for mid-size D2C brands that sell through four or five quick-commerce apps at once. It forecasts demand by city and platform, then drafts the weekly purchase-order and stock-transfer plan. Brands pay a monthly fee. It works if it cuts lost sales without piling stock in the wrong city.
Quick commerce has turned a D2C brand's stock into hundreds of tiny piles. By December 2025 the three largest quick-commerce apps ran about 2,100, 1,150 and 1,136 dark stores, with a big marketplace aiming for 1,500 or more by the end of 2026[1]. Each store serves a radius of two or three kilometres and carries a few thousand SKUs[2].
If you sell on all of them then you're answering several platforms' purchase orders every week, each with different lead times and city splits. Demand is lumpy too - one vendor blog says snack and drink brands take roughly 40–50% of their daily orders in the evening window[2]. Most brands we know of still plan this in spreadsheets. We found no public figure on how often their products go out of stock.
Fillcast reads sell-through and inventory reports from each platform's seller portal, plus the brand's own warehouse stock, and produces three things each week -
The planner approves or edits every line. Fillcast never places orders on its own, because a wrong forecast with automatic ordering is an expensive way to learn.
I narrowed the seed. It pitched hyperlocal AI forecasting for retail in general, which enterprise planning vendors already sell to big chains[3]. The gap is smaller and sharper - brands with ₹20–200 Cr in revenue (an assumption) that live on quick commerce but can't justify an enterprise planning suite.
The user is a supply-chain or e-commerce manager at a food, personal-care or home brand, and the payer is the founder or COO. A planner in India costs a salary, so Fillcast has to cost less than one and save more than it costs in lost sales and write-offs. We found no published prices for planning tools in this niche, so pricing has to come from pilots. A monthly fee by number of SKUs and platforms is the simplest start.
The store count is still climbing fast. One big marketplace moved from 750–850 dark stores in early 2026 towards 1,500, and another expects 450–500 in 2026[1]. Every new platform adds another purchase-order stream for brands to manage. Quick commerce was already 40–50% of India's online grocery spend in 2023[4], so few consumer brands can sit it out.
There's no public count of D2C brands selling on quick commerce, and no published figure for spending on replenishment software in India. The firm figures are nearby ones. Quick commerce was worth about US$7–8 billion in FY25 (about ₹68,000–77,000 Cr), and D2C is projected at about US$60 billion by 2030, though the same source also quotes a conflicting, higher figure[1]. Treat both as context, since your real market is the number of brands with at least three active platforms.
One Indian planning vendor sells forecasting, allocation and replenishment to more than 700 brands, mostly large fashion and retail names[3]. One order-management vendor lists a quick-commerce module inside its order and warehouse software[5]. Fulfilment companies offer inventory planning, but tied to their own fulfilment networks. The gap is a stand-alone planner priced for mid-size brands that keep their own warehouses and want help with platform purchase orders specifically.
| Difficulty | Moderate (36/100) |
| Starting capital | ₹60 L–1.1 Cr |
| Time to launch | 6–9 months |
The forecasting maths is well known. The hard part is data plumbing, because each platform's seller portal exports reports in its own format, the formats change and some numbers arrive late. You also need to show a measurable gain inside one quarter, because brands switch tools quickly when the first forecasts miss.
No special licence here. Planning software for brands needs no sector permit, so the baseline plus contract terms covers it.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
Don't trust AI to place orders on its own. A wrong forecast with automatic ordering piles stock in the wrong city, so a planner approves every line.
Want to work out the AI layer for Fillcast? Talk to Yash →
Here's what can go south -
I'd call this a reasonable software business for a founder who has done supply planning at a consumer brand and knows how the platforms' purchase orders really behave. If you want to sell AI forecasting in the abstract then skip it. Before raising money, show that five brands cut stockout days by a clear margin over eight weeks against their own spreadsheet baseline.
Plan for roughly ₹60 L–1.1 Cr over 6–9 months, most of it on data connectors for each platform and the forecasting product.
No sector licence. You need company and GST registration, DPDP-ready processing terms and written authority from each brand to use its platform reports.
Yes, wherever brands sell through several quick-commerce apps. You'd have to rebuild the connectors for each local platform's seller reports.
The free Fillcast validation kit has the line-by-line capital breakdown, the effort scoring, the data and contract checklist, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for brand planners, and 5 assumptions to test. Download the kit →
Thinking about building Fillcast or something close to it? The make-or-break work is getting clean, regular data out of every platform's seller portal, and it's worth mapping that before you write a forecast. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.