Dark-store stockouts and ad spend in one view for brands on quick commerce
TL;DR Stockmesh pulls a brand's sales, stock and ad reports from every major quick-commerce app into one place. It shows, dark store by dark store, where ads are running on products that are out of stock. Mid-sized D2C and FMCG brands pay a monthly fee to stop burning that spend.
Quick commerce is now a main channel for tonnes of consumer brands. Orders on two of the leading apps rose from ₹30,000 Cr in 2024 to ₹64,000 Cr in 2025[1]. In December 2025 the three biggest apps ran 2,100, 1,150 and 1,136 dark stores[2].
Each platform gives a brand its own seller portal, its own report format and its own ad console. A brand on three apps is tracking around 4,000 small warehouses, and any one of them can run out of a product on its own.
So the team downloads spreadsheets, stitches them together every week and finds out late that it paid for ads in pincodes where the item couldn't be delivered. The seed called this data silos. On quick commerce the cost is direct and you can measure it.
Stockmesh does one job well instead of trying to unify all retail data. It collects a brand's quick-commerce reports every day, through portal exports, connectors and partner APIs where they're offered, and maps every SKU to every dark store.
The main screen is a list of leaks - stores where the brand's ads are live while stock is zero, stores where a fast seller ran out three days running and cities where one platform keeps under-ordering compared with the others. Each leak shows the rupee cost, which is wasted ad spend plus an estimate of lost sales based on that store's normal run rate.
A forecasting model suggests the purchase order each platform should get, and the brand's key-account manager can send it with numbers behind it. Stockmesh is analytics only. It does not hold stock, take orders or move money.
The payer is a D2C or FMCG brand selling ₹5–100 Cr a year online, and the buyer is usually the growth or e-commerce head. Operations tools already charge brands for order and warehouse software[3][4]. My working assumption is ₹25,000–75,000 a month depending on SKUs and platforms. Agencies that run quick-commerce ads for lots of brands are a second channel.
Quick commerce grew at a 110–130% CAGR from 2021 to 2025 and reached US$7–8 billion in FY25 (about ₹68,000–77,000 Cr)[2]. One large marketplace is aiming for 1,500+ dark stores by end-2026 and another runs 450–500[2], so brands now juggle five platforms instead of two. Ad consoles on these apps have grown up, and spend has followed them.
There is no public count of brands selling on quick commerce or of what they spend on analytics. The narrow signal is the channel itself - ₹64,000 Cr of 2025 orders on two apps[1] and about 4,400 dark stores across the top three in December 2025[2].
One order-management vendor reports 8,000+ live clients across its tools[3]. Treat that as adjacent and not as your market.
Order-management vendors integrate the big quick-commerce apps for order and inventory sync[3]. Another lists quick commerce as a channel and serves 1,800+ brands and 3PLs, but describes no analytics module[4]. One analytics vendor offers decision intelligence across the big marketplaces and doesn't mention quick commerce at all[5].
The gap is a dark-store-level view that ties stockouts to ad spend, for brands too small to build it in-house.
| Difficulty | Moderate (37/100) |
| Starting capital | ₹55–95 L |
| Time to launch | 4–6 months |
The software isn't hard. Data access is. Each platform changes report formats without warning, and official APIs may be limited or need partner status. Selling is crowded too, because every brand already gets pitched a handful of new dashboards each month.
No sector licence applies. Stockmesh is analytics software, so the work is in contracts and data rules.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
Don't trust the lost-sales estimate as a hard number. It's a model guess on messy portal data, so show it as a range and let the brand check it.
Want to work out the AI layer for Stockmesh? Talk to Yash →
Here's what can go south -
I'd back a founder who has run quick-commerce key accounts at a brand and knows these reports first-hand. If you're hoping to sell a general retail data lake then this isn't it. First prove that five brands find ad waste worth at least three times your fee in their first month.
Plan for roughly ₹55–95 L, most of it on engineers, data connectors and early sales. Starting with a handful of brands keeps the first year lighter.
No sector licence. You need company registration, GST once over the threshold, DPDP compliance for any customer fields and a data processing agreement with each brand.
Yes, wherever brands sell through dark-store platforms. Check each platform's data terms and your local data protection law first.
The free Stockmesh 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 teams, and 5 assumptions to test. Download the kit →
Thinking about building Stockmesh or something close to it? The make-or-break work is securing reliable data access from each platform before you scale sales, and that's exactly the kind of thing I help founders with. 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.