In response to the rising challenge of managing fragmented enterprise data, Connecty AI has emerged from stealth with a groundbreaking solution. With $1.8 million in pre-seed funding, the company is unveiling its innovative context engine, which tackles the complexity of modern enterprise data systems. This solution is designed to optimize data workflows and reduce the time and cost spent on manual data analysis, allowing businesses to manage their data more effectively and efficiently.
Key Features of Connecty AI’s Context Engine
- Contextual Data Integration: The core of Connecty AI’s platform is its ability to connect and contextualize data from multiple sources across complex systems. This is achieved through an enterprise-specific context graph that continuously evolves and integrates real-time human feedback.
- Automated Data Tasks: By leveraging its context engine, Connecty AI automates key data tasks for various roles within an organization, from data engineers to business analysts. The platform updates documentation, generates recommendations, and uncovers hidden metrics aligned with business objectives.
- No-Code Deployment: Connecty AI is designed for ease of use, with a no-code setup that allows businesses to integrate the platform with data warehouses like Snowflake or BigQuery in under five minutes.
- Personalized Semantic System: The platform uses a dynamic semantic layer that adapts to the specific needs of each enterprise, enhancing the accuracy and relevance of data queries and insights.
Real-World Validation and Results
During prototype development, Connecty AI partnered with companies ranging from small enterprises to large organizations with annual revenues of up to $2 billion. Early adopters have seen significant improvements in data preparation times and the accuracy of insights.
- Faster Data Preparation: Businesses like Kittl have reported a drastic reduction in the time required to prepare and analyze data, cutting down wait times from weeks to just minutes.
- Enhanced Schema Descriptions: Mindtickle’s analytics team has praised Connecty AI for its ability to improve schema descriptions and enhance the semantic layer, providing a unified flow from data preparation to querying.
The Founders’ Vision
Connecty AI was founded by Aish Agarwal and Peter Wisniewski, who bring complementary experiences from across the data value chain. Agarwal’s background in addressing data inefficiencies at FL Studio and Wisniewski’s experience building data platforms at Point72 and a major European e-commerce player have enabled them to develop a solution that understands the complexities of modern data ecosystems.
“We understand that effective data management is about connecting the dots between data sources, business objectives, and the people who use them,” said Agarwal, CEO of Connecty AI. “Our platform does more than just automate workflows—it creates a cohesive, enterprise-specific context that drives actionable insights.”
Market Opportunity and Future Plans
The global AI analytics market is expected to grow significantly, with the demand for AI-driven data management tools rising in parallel. Connecty AI is poised to capitalize on this demand, offering a solution that not only reduces manual data analysis but also improves data workflows by providing a deeper, more contextual understanding of enterprise data.
In the coming months, Connecty AI plans to expand its context engine to support additional data sources and offer it as a service via API. This move will further enhance its utility for businesses looking to optimize their data workflows.
Investor Support and Growth
Jacek Łubiński, Partner at Market One Capital, expressed confidence in Connecty AI’s potential: “The platform’s ability to unify and contextualize data across fragmented systems presents a massive opportunity for businesses looking to use AI to automate data workflows.”
