Bharat in BetaMachine-readable product discovery
Source
- Titan Capital (opens in a new tab)LinkedIn post, recommendation 1 of 5Published: 25 Sep 2026
The idea
Titan Capital advises Indian consumer founders to prepare for shoppers who ask AI assistants what to buy. It recommends keeping catalogue, pricing and review information machine-readable, and tracking referrals from AI assistants as closely as paid acquisition channels.
Why this is interesting
- If more shoppers ask an assistant rather than browse, a brand’s own product data becomes part of its distribution. How assistants select, rank and cite products remains outside the brand’s control and can change without notice.
- A venture thesis describes where an investor sees possibility. It does not by itself identify a narrow buyer, an entry wedge or evidence of demand.
Possible starting points
Product data readiness check
Test whether one category’s catalogue, prices, stock and reviews are read accurately by AI assistants, and show where the data is incomplete or inconsistent.
AI referral measurement
Help consumer brands attribute visits and orders that begin in AI assistants, starting with the referral signals they can already observe.
Questions to answer
- In which categories do shoppers already arrive from AI assistants, and can that traffic be measured reliably?
- Does cleaner structured data change what assistants recommend, or does selection depend on signals a brand cannot influence?