In the past two years, a wave of AI-powered data tools have 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, emerging from stealth 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 cataloguing 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.
During prototype development, Connecty AI has partnered with enterprises ranging from $5 million to $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 analyze metrics. We would wait 2-3 weeks on average to prepare data and extract actionable insights from our product usage data and merge them with transactional and marketing data. Now with Connecty AI, it’s a matter of minutes!” said Nicolas Heymann, CEO of Kittl.
“We were impressed with the accuracy of responses from day one. Additionally, Connecty AI generated excellent suggestions to improve the schema descriptions and enhance our semantic layer. It offers a unified flow from prep to querying, nothing like that we’ve seen anywhere else,” added Aditya Upadhyay, Director Analytics, Mindtickle.
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.
The timing of the duo’s emergence from stealth aligns with growing market demand. Businesses are demanding more from AI. According to Future Markets Insights, the global AI Analytics market is projected to grow at a CAGR of 22.6%, reaching $223 billion by 2034.
As data complexity grows, organizations face increasing costs, with data teams consuming 12.5% of IT budgets—an average of $5.4 million annually, with 87% dedicated solely to data and platform maintenance.
“We are thrilled to back Connecty AI as they redefine enterprise data management with their deep context learning,” said Jacek Łubiński, Partner at Market One Capital.
“The platform’s ability to unify and contextualize data across fragmented systems presents a massive opportunity for businesses looking to use LLMs for data workflow automation. The vision Aish and Peter have resonates with us and we’re excited to support them on the journey.”
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.
