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Cassidy Raises $3.7Million in Seed Funding to Build Automations Powered by Internal Company Data

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Cassidy, a business automation platform, announced $3.7 million in seed funding led by The General Partnership, with participation from Neo, Comma Capital, Spacecadet Ventures, Ride Ventures, and angels like Erik Goldman (Co-founder of Vanta), Jason Dorfman (CEO of Orum), Zach Sims (founder of Codecademy), and other leading angels and operators.

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“We’re making AI automations accessible to teams of all sizes and skill sets. To stay ahead in the age of AI, automation is no longer a nice-to-have; it’s a must-have,” said Justin Fineberg, CEO and Co-Founder, Cassidy. “Cassidy understands your business inside-out; the platform connects with all knowledge tools (Slack, Sharepoint, Notion, etc.) to give AI real-time context on your business, customers, competitors, brand voice, and much more.”

Here’s how it works: Cassidy’s knowledge base connects to a company’s existing knowledge tools and automatically cleans, prepares and makes business data “AI ready”. The platform allows teams to reliably build powerful AI workflows that make complex, human-level decisions, all with a nuanced understanding of the customer’s company.

“LLMs created a new era of automation. While previous workflow automation platforms could only automate tasks with basic if/then logic, LLMs unlocked complex reasoning in natural language,” said Ian Woodfill, Co-Founder & CTO, Cassidy. “However, the quality of LLM outputs and decisions is highly dependent on the specific context they’re given. Insufficient, disorganized, or outdated data can cause AI to respond inconsistently (or even hallucinate), making it hugely unreliable for business automation.That’s where Cassidy comes in.”

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Automations improve over time; each interaction cites its sources, allowing users to verify and exclude specific information through human-in-the-loop processes. This transparency ensures that data remains accurate and up-to-date. As more interactions occur, Cassidy’s performance is refined, incorporating added context and prompt adjustments to continually enhance the system’s effectiveness.

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