POMO AI

What POMO AI is building

Marketing teams have a decision problem, not a data problem. The average mid-market brand today has more performance dashboards, competitor tracking tools, creative testing platforms, and channel analytics than any team can meaningfully synthesise. The result is what Pomo's founders call a "decision crisis": execution has accelerated, but judgment has not. Teams spend their days reacting to what happened yesterday rather than acting on what matters today. Pomo's architecture is built around a different premise. Rather than adding another dashboard or copilot that waits to be asked questions, Pomo operates continuously in the background, monitoring competitor ad movements, demand signals, creative trends, and channel performance simultaneously. It pulls first-party data from CRM, commerce, and advertising platforms (Google Ads, Meta, TikTok, HubSpot) and combines it with external market context to model each company's specific constraints and strategic intent. The output is not a report. It is a ranked, context-aware list of the few priorities that actually matter that day, along with recommended actions and ready-to-use deliverables that slot directly into a team's existing workflow. Execution can be automated within brand-defined guardrails. Every outcome feeds back into the system so recommendations sharpen over time. Pomo's design partners report that competitive and demand signals surface days before they appeared in their existing tools, and that hours of manual research are replaced with a prioritised morning brief.

Why this matters

The name Pomo is a play on "post-modern advertising", the team's thesis that the category of marketing tooling is being rebuilt from scratch. The investor syndicate is the sharpest signal of all. Databricks Ventures investing in a marketing AI company is unusual, but it makes complete technical sense: Pomo's core capability is continuous multi-source data synthesis and pattern recognition at speed, which is exactly the infrastructure problem Databricks has spent a decade solving at enterprise scale. Their participation signals that the technical architecture has credibility at the data engineering level, not just the UI layer. The angel roster adds further texture. Mehdi Ghissassi previously headed product at Google DeepMind and Google Brain, meaning the people who built frontier AI models are personally backing this application of those models to marketing. Scott Belsky, creator of Behance and former Chief Product Officer at Adobe, is the rare investor who understands both creative and product workflows from the inside. Massimo Mascaro is a Google AI principal. These are not brand-name checkwriters. They are technical practitioners who built the category of tools Pomo is competing with, and they are betting that the agentic approach Praneet and Joe are taking is genuinely different. Early clients include Ai4 (the global AI conference series, whose founder Marcus Jecklin invested personally after using the product) and the Confederation of Indian Industry, which represents tens of millions of businesses and constitutes a distribution channel of remarkable scale for a company that launched one week ago.

Investors: Kindred Ventures (lead, Steve Jang), Databricks Ventures, Seven Stars, SV Angel, Timeless Partners, 645 Ventures, Angels: Scott Belsky (Adobe), Mehdi Ghissassi (Google DeepMind/Google Brain Head of Product), Massimo Mascaro (Google AI), Marcus Jecklin (Ai4 founder)