Our Work

Intelligence
in practice.

Six businesses. Six distinct problems. One consistent outcome — founders who finally understood what their data had been telling them.

01
E-Commerce · India
Kova & Co.
Skincare D2C · ₹4.2Cr revenue
Revenue grew 68% in 18 months. Gross margin fell from 54% to 38%. Nobody knew which decision had caused it.
Key finding: 42% of revenue came from SKUs generating below 21% margin.
02
E-Commerce · UK
Fieldhaven Home
Homeware brand · £1.8M revenue
£28K per month in ad spend across four channels. The founder had no idea which channel was acquiring profitable customers.
Key finding: Meta customers had 6x lower LTV than email-acquired customers.
03
Retail Chain · India
The Saffron Thread
Ethnic apparel · 11 outlets · ₹18Cr
Revenue up 41% after opening 5 new stores. EBITDA dropped from ₹3.2Cr to ₹2.1Cr. The new stores looked fine on paper.
Key finding: 2 original outlets funded the entire expansion. 3 new stores were structurally unprofitable.
04
D2C Brand · India & UK
Brightly Goods
Sustainable stationery · £620K revenue
3,400 new customers acquired in 12 months. The founder was proud of the number. Nobody had looked at how many were leaving.
Key finding: 71% churn rate. £448K recoverable from existing customers alone.
05
Cross-Border · India & UK
Aranya Collective
Artisan homeware · ₹9Cr + £480K
Two markets, one operation. The founder was treating them identically. The data said they were two completely different businesses.
Key finding: UK generated 61% margin vs 42% India — and received less than 20% of investment.
06
Hospitality · India
Mira Table
Restaurant group · 4 locations · ₹7.8Cr
Food cost running at 41% against a 32% target. The founder knew which restaurants felt busy. Not which were profitable.
Key finding: The most popular dish generated the lowest contribution margin.

Business names, individual identifiers, and certain operational details in these analyses have been changed to protect client confidentiality. The underlying data, analytical methodology, and strategic findings are real. Where specific figures are shown, they are indicative of the patterns identified — not verbatim extracts from client records.

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