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 organising data and enterprises spending an average of US$4.6 million every year on manual data analysis — until now.
Connecty AI, emerging from stealth with US$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 personalised 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.
During prototype development, Connecty AI has partnered with enterprises ranging from US$5 million to US$2 billion ARR, validating its approach on real-world data rather than public datasets like Spider. The platform connects to data warehouses like Snowflake or BigQuery in less than five minutes with no-code deployment. Early results have been compelling: “Our data complexity is growing fast, and it takes longer to data prep and analyse metrics. We would wait two to three weeks on average to prepare data and extract actionable insights from our product usage data and merge with transactional and marketing data. Now with Connecty AI, it’s a matter of minutes,” said Nicolas Heymann, CEO of Kittl.