“We’re Not Looking to Replace Data Teams” — Connecty AI Secures $1.8 Million to Streamline Enterprise Data

Aish Agarwal and Peter Wisniewski founded Connecty AI in 2024 as a result of their mutual, obvious frustration with data chaos. The duo observed the inefficiencies that plague enterprises dealing with massive, scattered data stores—data that holds vast potential but remains largely untapped. Connecty AI aims to change that by transforming how businesses approach data management. Backed by a recent $1.8 million pre-seed funding round led by Market One Capital, with additional investment from Notion Capital and seasoned data experts Marcin Zukowski (co-founder of Snowflake) and Maciej Zawadzinski (founder of Piwik PRO), Connecty AI is setting out to streamline the data complexity for enterprises across industries.

“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,” explains CEO Aish Agarwal. This perspective drives Connecty’s vision of creating a data ecosystem where information flows meaningfully, with insights emerging organically from a robust, interconnected “context engine.”

A New Approach to Data Understanding

At the heart of Connecty’s offering is its context engine—a sophisticated tool that enables organizations to gather, link, and interpret data from various sources seamlessly. The engine functions as a real-time “context map,” actively drawing in insights across a company’s diverse data streams while incorporating user feedback. By blending machine learning with structured and unstructured data, Connecty constructs a personalized, comprehensive context layer for each client, vastly reducing the need for repetitive data prep work.

“We’re not looking to replace data teams,” Agarwal clarifies. “Instead, we aim to eliminate up to 80% of the manual and mundane tasks they face, freeing them to drive value in ways that impact the business.” This design also ensures that Connecty adapts to the unique complexities of each organization, unlike generic data tools that struggle with fragmented enterprise data.

To support this, Connecty’s context engine employs a combination of vector and graph databases to create a “context graph,” a dynamic layer that continuously updates and fine-tunes itself based on new inputs and human feedback. This contextual approach transforms how data teams analyze and process information, delivering insights through customized “data agents” that can interact with users in natural language.

A Commitment to Addressing Real Enterprise Challenges

In recent years, a surge of AI-powered tools has promised to simplify enterprise data management. However, these tools frequently fail to deliver on their claims due to the inherent challenges of interpreting large, disconnected datasets. “Data models trained on public data struggle to work effectively with the nuances of a specific organization,” Agarwal notes. Connecty’s technology addresses this by tailoring its context engine to each client’s specific needs and goals, ensuring relevant and actionable insights for various stakeholders.

“Large language models (LLMs) aren’t a one-size-fits-all solution,” Agarwal adds. “They need to be customized for each enterprise’s dataset, a process that requires significant human oversight and expertise.” Connecty’s approach, he explains, leverages human-in-the-loop feedback to enrich the context learning process, enabling the platform to generate and update documentation dynamically, spot inconsistencies, and empower business users with self-service data exploration capabilities.

Transforming Data Workflows for Partners

Connecty AI has already partnered with several organizations, including Kittl, Fiege, Mindtickle, and Dept, to deploy proof-of-concept (POC) trials of the context engine. Early results indicate substantial time savings and improvements in data workflow efficiency, with some partners reporting reductions in analysis time from weeks to mere minutes.

Kittl CEO Nicolas Heymann, an early partner of Connecty AI, describes the impact: “Our data complexity is growing fast, and it takes longer to prepare and analyze metrics. We used to wait two to three weeks on average to get actionable insights from our product, transactional, and marketing data. Now, with Connecty, it’s a matter of minutes.”

The platform’s data agents play a crucial role here, helping users extract insights according to their technical expertise, permissions, and role-specific needs. These agents simplify data exploration by providing natural language guidance, boosting productivity and minimizing the need for extensive technical training.

Expanding the Context Engine and Market Reach

Connecty AI is looking to expand its context engine’s capabilities, allowing it to support an even wider array of data sources. The company’s strategy includes offering the platform as an API service with flexible, usage-based pricing, enabling a broader range of companies to access its advanced data contextualization tools. The goal is to provide businesses of all sizes with a powerful tool that not only reduces time spent on data wrangling but also unlocks richer, actionable insights at scale.

“Our goal is to make data as accessible and integral to business operations as the office coffee pot,” Agarwal says. “We’re focused on stripping away the tedious aspects of data work so teams can get straight to the insights that drive strategic decisions.”