Connecty AI Raises $1.8M to Solve Enterprise Data’s Three-Dimensional Problem

SAN FRANCISCO, Nov. 13, 2024 — In the past two years, a wave of AI-powered data tools has flooded the market, each claiming to replace data analysts. The reality consistently falls short of the promise. These tools are unable to interpret the fragmented, chaotic data pipelines inherent in enterprise systems, leaving data teams still spending 87% of their time organizing data and enterprises spending an average of $4.6 million every year on manual data analysis — until now.

Connecty AI founders Aish Agarwal and Peter Wisniewski.

Connecty AI, emerging from stealth this week with $1.8 million in pre-seed funding, has developed a context engine that tackles the inherent complexity in enterprise data. The round was led by Market One Capital, with participation from Notion Capital and data industry experts including Marcin Zukowski, co-founder of Snowflake and Maciej Zawadzinski, Founder of Piwik PRO.

Today, enterprise data teams navigate complexity across three critical dimensions: horizontal data pipelines (including multi-source ingestion, multi-cloud data warehousing, data lineage tools, and cataloging systems), diverse consumption patterns (spanning CRM systems, BI dashboards, and machine learning applications), and distributed human knowledge across roles like data engineers, analysts, governance teams, and functional managers.

While early AI solutions attempted to automate data workflows by interpreting complex schemas, these models fall short in enterprise environments. Even 90% accuracy isn’t enough when dealing with real-world data complexity. Large Language Models need more than static schema files; they require a continuously evolving, cohesive understanding across systems and teams.

“Our experience has shown us that effective data management is about more than just technology—it’s about connecting the dots between data sources, business objectives and the people who use them,” said Aish Agarwal, CEO of Connecty AI. “Any ad-hoc ‘guerrilla style experimentation’ with LLM data agents can lead to a pilot application but it’s a lot harder to build a production level application that is reliable.”

At its core, Connecty AI does two things: first, it extracts and connects three-dimensional context from diverse data sources and use-cases while integrating real-time human feedback, creating an enterprise-specific context graph. Second, it leverages this context to automate data tasks across various roles, using a personalized dynamic semantic system. The engine operates continuously in the background, proactively generating recommendations within data pipelines, updating documentation, and uncovering hidden metrics aligned with business goals.

Founded by Aish Agarwal and Peter Wisniewski, Connecty AI emerged from their complementary experiences in the data value chain. At FL Studio, Agarwal encountered the inefficiencies caused by fragmented data systems delaying business insights, while Wisniewski’s experience building data platforms for Point72 hedge fund and a major European e-commerce player highlighted similar challenges from a data engineering perspective.

Looking ahead, Connecty AI will expand its context engine’s capabilities across additional data sources and offer it as a service via API. In a market flooded with AI tools that promise to replace human analysts but deliver unreliable results, Connecty AI is taking a fundamentally different approach – embracing the complexity of enterprise data environments and augmenting rather than replacing human expertise.

About Connecty AI

Connecty AI offers enterprise data agents that help organizations agentize complex data tasks, saving up to 80% of time on manual data prep and analysis while accelerating decision-making. Founded by seasoned executives with decades of experience and backed by leading voices in data, Connecty AI uses a context engine to connect and unify context from platform-agnostic data sources. This engine leverages a context graph to agentize data tasks across various roles through a personalized, dynamic semantic system. Integrating seamlessly with tools like Bigquery, Databricks, Snowflake, and Power BI, Connecty AI redefines enterprise data automation to augment—not replace—human expertise.

Source: Connecty AI