Chomp Raises $3.6M to Turn Anonymous Q&A into Valuable Data

The anonymous social prediction game Chomp has closed a $3.6 million funding round. The investment was co-led by Jsquare and Blueyard, with participation from Accomplice, Big Brain Holdings, and several other venture firms. The capital will fuel product development and expand the use cases for its unique data output.

Gameplay: Revealing Personal and Collective Perspectives

At its core, Chomp is built on anonymous questions and predictions. Users answer prompts and then guess how others will respond. This creates a revealing feedback loop where players can see the gap between their own assumptions and the actual opinions of the crowd.

This “predict-and-check” mechanic transforms entertainment into a lighthearted social experiment. It continuously highlights the discrepancies between individual perception and group consensus.

Beyond Play: Data as a Core Asset

What sets Chomp apart is its treatment of gameplay data as a primary resource. All interactions are anonymized and richly contextualized, forming a distinctive dataset of human perspectives.

The team sees dual applications for this information:

  • For Consumer Insights: It captures candid preferences and attitudes that people might not share openly, offering researchers a more authentic view than traditional surveys.
  • For AI Development: This contextual, comparative human judgment data serves as high-quality training material for language models, helping AI better understand nuance and subjectivity.

Team and Trajectory

Chomp’s founder and CEO, Kiko, previously served as COO at the decentralized exchange Orca, bringing operational expertise to the venture. The team’s vision extends beyond creating an engaging app—it aims to pioneer a sustainable, gamified model for generating useful data.

With new funding secured, Chomp plans to enhance its data infrastructure, strengthening the link between its consumer-facing game and its enterprise data potential. This investment underscores growing market interest in innovative data collection methods and high-context AI training datasets.