Funding · AI & Deep Tech

Banza raises $1M pre-seed funding from Campus Fund

What they do

Banza builds a personal AI Twin that learns user habits to assist decisions on food and shopping

The company secured $1 million in pre-seed funding led by Campus Fund with participation from Avalanche

Founded in January 2026, Banza has over 200,000 users and plans to deepen its product experience

Banza raised $1 million in pre-seed funding announced on 4 May 2026 or 5 May 2026.
The round was led by Campus Fund with participation from Avalanche and a group of leading angel investors.
This is the first known funding event for the Indian startup as it focuses on building its core product.
The company aims to use the funds to accelerate product development and strengthen its foundation in the personal AI category.
Banza operates a remote-first team spread across different states in India.
The funding comes shortly after the company's founding earlier this year in January 2026.

The personal AI space is becoming increasingly competitive with various startups focusing on user-centric data models.
Campus Fund's lead investment signals strong interest in early-stage AI ventures with clear utility.
The focus on user ownership of data addresses growing privacy concerns among digital consumers.

Banza creates a personal AI Twin that learns from user preferences, habits, and context over time.
The product assists users in making proactive decisions about what to eat, watch, go, buy, and plan.
It aims to unify fragmented digital behavior data from siloed platforms into a single coherent experience.
The value proposition emphasizes user ownership of their personal data and digital identity.
The platform handles diverse categories including entertainment, commerce, and lifestyle planning.
Banza serves a large base of over 200,000 users who interact with the AI twin.
The company operates as a remote-first entity with team members distributed across various states.
The business model relies on providing a personalized service that adapts to individual user behaviors.
The technology focuses on learning continuously to improve the relevance of its recommendations.

Customers: Individual consumers seeking personalized AI recommendations across daily decisions
How it makes money: Direct-to-consumer personalized AI platform with cross-platform recommendations
What sets it apart: User-centric AI that works in user interest rather than platform interest