The problem sounds almost comically specific until you realize how universal it is: your prospect just hired a new VP of Engineering. You find out three days later. By then, four other vendors have already sent congratulatory notes, calendars filling up, deal momentum shifting elsewhere.
Autumn AI's approach centers on watching the entire public web, continuously, for the moments that matter.
The San Francisco startup—emerging from Y Combinator's Winter 2026 batch—went live in February with what amounts to a scanning engine for sales intent. The premise is simple enough. Define your ideal customer. Specify the signals you care about: GitHub activity from target accounts, new LinkedIn hires, incorporation filings, conference RSVPs. Autumn's platform monitors those channels around the clock, then filters what it finds into a condensed feed of what the company calls "intent-filtered alerts."
It's not subtle about the use case. The tagline on Autumn's website reads: "Deep people intelligence to stalk your prospects at scale and power high-precision outbound." Co-founders Vishnu Sampathkumar and Shiv Kampani appear unbothered by the surveillance-adjacent framing. Perhaps they've concluded that in 2026, most GTM teams have already made peace with those optics.
Beyond the Dashboard
The product itself functions as both a workspace and a delivery system. Teams can pull up a dashboard to review findings, but Autumn has built out multiple channels for getting intelligence to reps: email and Slack notifications when signals fire, CSV exports piped to inboxes or S3 buckets, a REST API with webhooks for custom workflows. There's also integration into Claude and Cursor via MCP plugins—a technical detail that hints at where the founders think this is heading. Sales intelligence, in their view, isn't just a tool salespeople use. It's data that AI agents will consume.
The company describes something it calls a "Deep Research Agent," positioned as "your best BDR, running 24/7." Details on what that agent actually does remain thin in public materials.
Three Workflows, One Bet
Autumn's launch documentation highlights three core applications. The first: incorporation prospecting. Newly formed companies surface in state filings and announcement posts before they populate traditional business databases. Autumn aims to catch them early.
Second is enterprise account monitoring—tracking organizational shifts and departmental signals across sprawling buyer hierarchies. The third workflow centers on conference preparation: scanning attendee lists to prioritize who to chase down before and during events.
The company lists Brex, Corgi, and Mintlify as customers, though these are self-stated logos without independent verification. Marketing copy on the homepage quotes a "4x more pipeline per rep" figure. Treat that one with caution.
The Technical Foundation
The architecture rests on what Autumn calls "long-context models," phrasing that appeared in co-founder Shiv Kampani's LinkedIn post announcing the team's YC acceptance back in January. Kampani brings the machine learning credentials—an AI researcher who presented at NeurIPS after a stint with SandboxAQ. Sampathkumar, the CEO, built sourcing technology during an internship at Sierra Ventures and worked as an ML engineer at Modulus, an AI security startup.
It's a two-person founding team as of March, according to Y Combinator's directory. LinkedIn lists the company size as 2-10 employees, though that likely reflects the platform's bucketing rather than actual headcount.
Crowded Territory, Narrow Wedge
Autumn's timing coincides with a broader shift among incumbent GTM platforms toward what the industry now calls "agentic" workflows. Apollo.io announced its AI Assistant on March 4, 2026, framing the tool as a fully agentic GTM operating system. RocketReach expanded into sequences and intent data in February. The velocity suggests the market has moved well beyond static contact databases.
Autumn isn't positioning itself as a full-stack platform. It's staking out a more focused claim: a specialized intelligence layer that plugs into whatever GTM stack a team already uses. The MCP integrations and webhook architecture reinforce that strategy.
Whether continuous web monitoring solves a problem meaningfully distinct from what 6sense, ZoomInfo, and the intent data incumbents already provide will come down to execution. And to how well the platform can filter signal from noise when you're scanning the entire public internet for triggers. That's not a trivial engineering challenge, even with long-context models doing the heavy lifting.
Pricing remains undisclosed. Teams interested in testing the platform can book a demo through Autumn's website or email the founders directly at [email protected].
The platform's value proposition is straightforward: in sales, timing beats perfect targeting. If you can reach the buyer at the exact moment intent crystallizes—before the noise floods in—you've already won half the battle. Autumn is wagering that companies will pay to be first.
