Sable AI Employee Aidan Raises $45M From Sequoia

Quick Reads
- Sable raised $45 million from Sequoia Capital and 8VC for its AI system, Aidan.
- Aidan runs live product demos, answers questions, and switches languages mid-conversation.
- The startup trains Aidan on a company’s best sales calls and internal documents.
- Notion and Decagon already use Aidan in production environments today.
- Sequoia’s Shaun Maguire compared the demo to what Stripe once did for payments.
Sable’s AI employee, Aidan, has raised $45 million from Sequoia Capital and 8VC, highlighting strong investor demand for automated sales roles. The startup, under a year old, built Aidan to replace the entire demo-to-onboarding pipeline, not just chat support. Unlike chat widgets, Aidan appears in a shared browser window and actively guides buyers through the product.
Sable trains the system by feeding it recordings of a company’s strongest sales calls, plus internal documentation and marketing materials. This builds what the company calls a reusable brain for each customer it serves. CEO Nim Ravid says Aidan feels more like a sales engineer than a scripted bot. It sees page changes and adjusts its pitch in real time. Sable describes Aidan’s design in detail on its own site, and Notion and Decagon already run it in production today.
Sequoia partner Shaun Maguire compared Aidan’s demo to Stripe’s early impact on payments. He praised Aidan for switching between English, Mandarin, and Spanish during one conversation. Palantir co-founder Joe Lonsdale joins Maguire on Sable’s board, and angel investors include HubSpot’s Brian Halligan and Dharmesh Shah. As The Next Web notes, the funding arrives as the broader agentic AI market races toward tens of billions in projected value by 2031.
Even so, real obstacles remain. Trust, job displacement fears, and competition from platforms like Notion’s own AI agents all threaten Sable’s pitch. Ravid himself acknowledges the gap between a compelling demo and a product that reliably replaces human sales teams at scale, a gap many AI startups have promised to close but few have managed.





