San Francisco, November 13, 2024
Connecty AI, a startup redefining enterprise data management, has emerged from stealth with $1.8 million in pre-seed funding. The round was led by Market One Capital, with participation from Notion Capital and industry veterans, including Marcin Zukowski, co-founder of Snowflake, and Maciej Zawadzinski, founder of Piwik PRO.
Tackling Enterprise Data’s Three-Dimensional Complexity
Connecty AI addresses the fragmented and chaotic pipelines characteristic of enterprise data, which have traditionally burdened data teams with manual preparation and analysis. The platform introduces a groundbreaking context engine that:
1. Extracts and connects data contexts across diverse systems and use cases, creating an enterprise-specific context graph.
2. Automates data tasks with a dynamic semantic system tailored to roles within the organization.
By integrating real-time human feedback and providing proactive recommendations, Connecty AI significantly reduces manual data prep time and accelerates decision-making processes.
Real-World Impact
During prototype testing, Connecty AI partnered with companies ranging from $5M to $2B in annual revenue, delivering measurable results:
• Reduced data prep time from weeks to minutes.
• Enhanced schema descriptions and semantic layers for seamless workflows.
“Connecty AI’s context graph bridges the gap between fragmented data, business goals, and the people behind them,” said Aish Agarwal, CEO. “Our mission is to augment human expertise with reliable, production-level AI tools that thrive in complex environments.”
Strategic Vision
Looking ahead, Connecty AI plans to expand its platform to integrate additional data sources and offer API services, enabling broader enterprise adoption. Backed by notable investors and armed with a unique approach to AI-driven data management, Connecty AI is positioned as a leader in solving enterprise data’s multidimensional challenges.
About Connecty AI
Founded by Aish Agarwal and Peter Wisniewski, Connecty AI combines decades of expertise in the data value chain. Its context engine leverages platform-agnostic data sources like BigQuery, Databricks, Snowflake, and Power BI to create a unified flow of data insights. The company’s approach emphasizes augmenting—not replacing—human expertise.
