Company Overview
Snapshot
Founded in May 2024 by Nir Weingarten, Tamir Tiomkin, and Omer Hacohen, Eikona operates with 11–50 employees. The company has raised $5 million across one funding round from 4 investors, with StageOne Ventures as a lead investor. In December 2025, Eikona secured a $5 million Seed round to advance its generative AI-powered marketing automation platform.
Business overview
Eikona is a Business Software company that empowers lifecycle marketers to deliver adaptive engagements at scale by harnessing user behavior to drive generative AI. Its core technology leverages Artificial Intelligence, specifically Generative AI, to create marketing campaigns that automatically improve based on individual user responses. The company operates in the Sales & Marketing Solutions and e-Commerce Tools sectors, primarily serving the Commerce & Retail and Distribution Channels markets with its B2B product.
Strategic signal
In December 2025, Eikona announced a $5 million Seed funding round, highlighting investor confidence in its generative AI approach to marketing automation. This investment signals a growing market interest in AI-driven solutions that move beyond traditional A/B testing, offering adaptive engagements based on real-time user behavior. For investors, this indicates Eikona's potential to disrupt the marketing technology landscape with a scalable, AI-native platform.
Company Intelligence Q&A
- When was Eikona founded?
- Eikona was founded in May 2024.
- Who are the founders of Eikona?
- The founders of Eikona are Nir Weingarten, Tamir Tiomkin, and Omer Hacohen.
- What is Eikona's primary business model?
- Eikona operates on a B2B business model, providing its marketing automation solutions to other businesses.
- What was Eikona's most recent funding event?
- In December 2025, Eikona completed a $5 million Seed funding round. The round was led by StageOne Ventures.
- What technology does Eikona utilize in its marketing automation platform?
- Eikona utilizes generative AI to create marketing campaigns that adapt and improve based on individual user responses, moving beyond traditional A/B testing methods.