Connecty AI, an enterprise agent company, announced that it has raised $1.8 million as part of its pre-seed funding round. The round was led by venture capital firm Market One Capital, with participation from Notion Capital and data industry experts including Snowflake co-founder Marcin Żukowski and Piwik PRO founder Maciej Zawadziński.
They aim to expand their context engine to support more data sources and offer it via API. The funds raised will help achieve this, and enhance additional data sources to improve contextual understanding.
Enterprise data teams today manage complexity across three key areas: multi-source data pipelines (including ingestion, cloud warehousing, lineage, and cataloguing), diverse consumption patterns (such as CRM systems, BI dashboards, and machine learning apps), and distributed knowledge across roles like data engineers, analysts, and managers.
When it comes to AI solutions automating data workflows, even 90% accuracy isn’t enough—LLMs need a constantly evolving, unified understanding across systems and teams.
Connecty AI aims to transform enterprise intelligence by equipping data practitioners with agents that deeply understand business context and seamlessly integrate across the organisation. It offers a context-aware platform that helps teams unlock hidden insights and reclaim up to 80% of the time spent on manual data tasks.
As per the press release, Jacek Łubiński, partner at Market One Capital, expressed excitement about backing Connecty AI, noting its potential to streamline data management and automate workflows with LLMs.
About Connecty AI’s Solution
“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, on how ad-hoc LLM experimentation may lead to pilot projects, but scaling to production will be much more challenging.
Connecty AI extracts contextual data from multiple sources, building an enterprise-specific context graph with real-time feedback. After that, it automates data tasks, generating recommendations, updating documentation, and uncovering key metrics aligned with business goals.
Reports estimate that the global AI Analytics market will expand at a compound annual growth rate (CAGR) of 22.6%, reaching $223 billion by 2034. As data complexity rises, organisations face higher costs, with data teams consuming an average of 12.5% of IT budgets—equating to $5.4 million per year, of which 87% is allocated exclusively to data and platform maintenance.
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