Connecty AI Raises $1.8M to Tackle Enterprise Data Fragmentation

As enterprises grapple with fragmented data ecosystems, Connecty AI has emerged from stealth with $1.8 million in pre-seed funding to redefine data management. Led by Market One Capital with contributions from Notion Capital and industry experts, this funding marks a pivotal step for Connecty AI’s context-aware platform, which aims to unlock hidden insights and save up to 80% of time spent on manual analysis.

Over the last two years, numerous AI-powered data tools have attempted to replace data analysts but consistently need to catch up, struggling with the fragmented, chaotic pipelines in enterprise systems. Data teams still spend 87% of their time organizing data, and enterprises allocate an average of $4.6 million annually to manual analysis. Connecty AI takes a different approach with its unique context engine, designed to overcome these real-world complexities.

Connecty AI tackles enterprise data through a three-dimensional lens: horizontal data pipelines, diverse consumption patterns, and distributed human knowledge. Horizontal pipelines span multi-source ingestion and cloud data warehousing; diverse consumption patterns cover CRM systems and BI dashboards; and distributed knowledge integrates roles from data engineers to governance teams. Connecty’s platform creates an enterprise-specific context graph that captures these complexities, transforming data tasks for greater efficiency.

Unlike early AI models that attempted workflow automation, Connecty’s context engine provides a continuously evolving, integrated understanding across systems. Connecty AI’s CEO, Aish Agarwal, explains, “Effective data management is more than technology—it’s about connecting the dots between data sources, business objectives, and people.” While quick experiments with LLM agents can yield pilot apps, Agarwal emphasizes that reliable, production-level solutions demand more robust integration.

Connecty AI’s engine connects data sources and integrates real-time human feedback to form a dynamic, enterprise-specific context graph. This engine operates in the background, proactively generating recommendations, updating documentation, and revealing hidden metrics aligned with business goals. Its no-code deployment lets it connect with data warehouses like Snowflake or BigQuery within minutes. Early users like Nicolas Heymann from Kittl reported that Connecty reduced their data prep time from weeks to minutes. Aditya Upadhyay from Mindtickle praised the platform’s accuracy, noting its seamless integration from data prep to querying.

Founders Aish Agarwal and Peter Wisniewski bring complementary experience to Connecty AI. Agarwal, with a background at FL Studio, encountered delays due to fragmented data, while Wisniewski’s work at the Point72 hedge fund highlighted similar challenges from a data engineering standpoint. Together, they bridge the gap between data complexity and actionable insights.

The launch of Connecty AI comes at a time of growing demand for AI-driven data solutions. The global AI analytics market is projected to grow at a CAGR of 22.6%, reaching $223 billion by 2034. This growth is accompanied by increasing costs, with data teams accounting for around 12.5% of IT budgets—or an average of $5.4 million annually.

Looking ahead, Connecty AI plans to expand its context engine’s reach and offer it as an API service. In a market filled with AI tools that often fail to replace human analysts, Connecty AI embraces the complexity of enterprise data environments and augments human expertise, helping companies extract valuable insights efficiently and effectively.