Brand Incubation Studio

We build Indian consumer brands that shoppers keep coming back for.

Palette reads what people actually reorder across Indian e-commerce before committing to a category. We build from demand, not from decks.

Pitch a Brand Idea How We Work

Most brands start with a category story. We start with repeat purchase.

What everyone else does

Most Indian consumer brands validate a category through market size, first-purchase demand, social response, and discount-led sales. The first basket gets measured closely. The second often does not.

What we do instead

Palette reads purchase sequences and reorder behaviour across Indian e-commerce before committing to a category. We look for consumer pull that holds beyond the first transaction.

The Data Edge

AI reads real reorder patterns across Indian e-commerce.

We read anonymised purchase-and-reorder streams across Indian e-commerce, looking at reorder rate, repeat-buy interval, basket co-occurrence, and shopper cohorts. This separates categories with genuine consumer pull from categories inflated by trial spend or discounting. It changes the starting point from “Can we sell this?” to “Will this buyer come back?”

Two ways Palette partners with brand builders.

Build

From validated demand up

When a category shows strong reorder data, Palette can build or co-found a brand from scratch. We define the product, positioning, and route to shelf around the demand signal. This is an early-access incubation program, not a catalogue of finished brands.

Back

Accelerate brands with real pull

Some founders already have a product and measurable repeat-buy behaviour. Palette brings category analysis, reorder benchmarks, positioning, and focused brand-building support around the pull they have started to prove.

Read about our approach
58%
Average early-program reorder rate
Based on our internal pilot tracking across three Indian consumer categories, the average reorder rate was 58%, against a 31 percent category baseline.
"We thought our strongest signal was the size of the first launch wave. Palette showed us that the more useful pattern was a smaller shopper cohort returning within a consistent repeat-buy interval, especially without the same discount. That changed how we thought about the product and the customer we were building for."

brand founder from our early incubation cohort, personal care category (early-access pilot)

A small team with deep roots in Indian consumer data.

Siddharth Gadodia

Siddharth Gadodia

CEO & Co-Founder

Builds consumer growth models across Indian packaged goods and modern trade, with a focus on repeat-purchase data and sustainable brand pull.

Arjun Mehta

Arjun Mehta

Head of Data Intelligence

Builds ML systems for consumer transaction data and models reorder patterns across Indian e-commerce channels.

Priya Krishnamurthy

Priya Krishnamurthy

Brand Strategy Lead

Works across Indian FMCG brand strategy and new product development, translating demand signals into positioning.

Meet the full team

Thinking on Indian consumer markets and brand building.

Why Reorder Rates Are the Metric Brands Ignore

Why Reorder Rates Are the Metric Brands Ignore

Launch metrics tell you what caught someone's eye once. Reorder rates tell you whether what you built is actually worth coming back for.
Building Consumer Brands for India Using Purchase Data

Building Consumer Brands for India Using Purchase Data

Data-first brand building in India means reading transaction sequences before committing to a category, not after the launch budget is spent.
What AI Can Read in Shopper Reorder Patterns

What AI Can Read in Shopper Reorder Patterns

Machine learning does not predict which brand will win. It identifies which products already have enough repeat demand to justify building a brand around.
View all insights

Have a brand idea with a demand signal behind it?

Tell us the category and what you have seen in the data, from reorder behaviour to repeat-buy intervals. We will start with the signal, not the pitch deck.

Pitch a Brand Idea