MarketplaceMedium Effortglobal

TasteSurface — Recommendation, Entitlement & Measurement OS for Assistant-Native Media Discovery

Most recommendation stacks were built for one of three destinations:

Score76/100
Apr 27, 2026
TAM
€684M — ~19K catalog-rich platforms globally × ~€3K/month equivalent spend on assistant-distribution, entitlement, and measurement infrastructure.
SAM
€144M — ~4K upper-midmarket and enterprise content / media / marketplace platforms actively testing assistant surfaces × ~€3K/month equivalent spend.
SOM
€1.2M — 18 customers at ~€5.5K/month blended subscription and implementation revenue in years 1-2.
AIMobileMarketplaceAPI

The Problem

Most recommendation stacks were built for one of three destinations:

  • the first-party app,
  • the website,
  • or a traditional feed/search result.

That assumption is breaking.

When a platform is surfaced inside an assistant, the product team suddenly needs to answer new operational questions:

  • which recommendations are safe and eligible to show in this assistant context?
  • what preview, save, play, or open actions are allowed by market, user tier, and device?
  • how should the system explain why this item was surfaced?
  • how does the platform preserve creator / rightsholder visibility when discovery happens outside the main app?
  • how does the team measure what an assistant recommendation actually drove downstream?

Generic APIs are not built for this distribution model. They expose raw content or actions, but not the control layer around recommendation, entitlement, explanation, and attribution.

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