AI-Driven Intent Data Gains Traction As Sales Teams Look Beyond Lead Lists
Lead lists are easy. Finding accounts that are ready to buy is hard. That gap is where intent data is winning, and why a new startup, Leadpoet, is getting attention from B2B sales teams.
Leadpoet at a glance
Leadpoet, a New York-based AI company, raised $750,000 in pre-seed funding and hit roughly $1 million in annualized run rate across 26 B2B customers. The company was founded by Pranav Ramesh and Gavin Zaentz, who met at Nasdaq after years in finance and cloud infrastructure roles.
The team is four people, hiring a fifth, and is part of the NVIDIA Inception program. Recent backers include DSV Fund and Astrid, a publicly traded company. Revenue today is a mix of customer subscriptions and token rewards from its participation in the Bittensor network.
Why traditional databases fall short
Incumbents like ZoomInfo and Apollo offer vast contact databases enriched with firmographic and technographic data. Useful for coverage, weak on timing. You still end up guessing who's actually in market.
Intent data isn't new-ZoomInfo acquired Clickagy back in 2019-and plenty of tools have tried layering behavior on top of static lists. The problem is most rely on centralized data aggregation and models that update slowly.
How Leadpoet works
Leadpoet analyzes open web signals-social posts, funding announcements, hiring patterns, and other public indicators-to surface companies with live buying intent. Each recommended account comes with the specific evidence that triggered it. "We actually provide them leads with genuine intent of buying their product and give them the specific signals that indicate that interest," said Ramesh.
Under the hood, Leadpoet runs on Bittensor, a decentralized AI network. Independent contributors compete to produce better model outputs; Leadpoet uses validators to filter and score those results before delivering them to customers. The company also supports private deployments, so you can score intent against your own prospect data without sharing sensitive info with a centralized vendor.
What customers are seeing
"Pranav and Gavin sit in a rare overlap, combining deep AI engineering expertise with a real understanding of day to day sales," said Siam Kidd, CIO at DSV Fund. "Leadpoet surfaces prospects who are actively showing buying intent, and that focus gives them a decisive advantage."
Teams measure value in efficiency, not sheer volume. "Leadpoet replaced two providers we were using to patch gaps," said Max Sebti, CEO of vision AI company Score. "Now we're talking to people who are actively in the market for our product."
What this means for your sales playbook
- Shift from volume to relevance. Fewer accounts, higher hit rates. Prioritize sequences on companies showing live signals (funding, hiring, tech changes).
- Lead with evidence. Reference the trigger in your opener-"Saw your Series A and new data hires"-to boost reply rates without sounding generic.
- Time your outreach. Funding and hiring spikes create 30-90 day windows when budgets and urgency exist. Build cadences around those windows.
- Protect your data. If you work with sensitive prospect lists, favor vendors that offer private scoring or on-prem options.
- Measure the right KPIs. Track meetings set and pipeline from intent-sourced accounts, conversion by signal type, and cycle time reduction.
Questions to ask any intent data vendor
- Which signals do you use, and can I see the evidence for each recommended account?
- How often do models update, and who validates output quality? Is there external competition or just an internal roadmap?
- Can we run private deployments so our data stays in our environment?
- How do you prevent stale records and false positives? What's the feedback loop from my outcomes back into the model?
- What integrations exist for CRM, SEP, and data enrichment, and how do you handle de-duplication and rate limits?
- How is pricing tied to outcomes, not just credits or contacts?
The bottom line
Intent data is moving center stage as sellers push past bloated lists and guesswork. Leadpoet's bet is that competitive model outputs, on-chain incentives, and private deployments will deliver fresher signals and better timing than centralized incumbents.
If your team is exploring AI for prospecting and sales ops, you can also browse practical training by job role here: Complete AI Training - Courses by Job.
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