Announcing Datalane: building the data layer for the local economy

If you're a B2B company selling to other enterprises, you've got it pretty good when it comes to understanding your market and reaching customers. ZoomInfo, Clay, and Apollo all work reasonably well. Your prospects are on LinkedIn, they have corporate email addresses, and there's a relatively clean digital trail to follow. Sales teams can build lists, run campaigns, and hit their numbers with tools that have been battle-tested over the past decade.

But most of the economy doesn't look like that.

Think about the businesses that actually make up the fabric of daily life. The restaurant where you overpaid for lunch, the auto shop that is still fixing your car, your dentist who finally installed TVs with Netflix, the salon where you get your hair cut, and the HVAC company you call when your heat goes out in January (hey, SF can get cold). These businesses represent an enormous part of the economy — restaurants alone number around 10 million globally, home services another 20 million — but they operate in a completely different world from the tech companies that dominate our attention.

And if you're trying to sell to these businesses? Good luck finding accurate information.

Data providers on the local economy are out to lunch

The problem with gathering data on this group of businesses is foundational: the decision-makers in these industries simply aren't where traditional data providers look for them. The franchise owner running 15 Burger King locations isn't posting on LinkedIn. The operator of a HVAC or plumbing business has their phone number on the side of their truck. These are offline businesses run by offline people.

The (predictable) result of this is that traditional data providers have, frankly, shitty data on this cornerstone of our economy. In conversations with companies selling into these markets, we’ve heard this story over and over again. One RevOps leader we spoke with described their sales team's experience with traditional data vendors as a "trauma response" — the data quality was so poor (hovering around 30-40% accuracy) that reps had simply stopped trusting it. SDRs were spending hours manually researching each prospect, calling wrong numbers, emailing addresses that bounced, and generally burning expensive headcount on data archaeology instead of actual selling.

A GTM data engine purpose built for the local economy

This is the problem that David Patterson-Cole and Ganesh Thirumurthi set out to solve when they founded Datalane. Both had seen it firsthand — the first company they built in college was a food delivery pooling startup, Potluck, to connect mom and pop restaurants with college students who couldn't afford to pay full delivery fees. They saw how broken local GTM was and realized that while everyone was focused on building better tools for the LinkedIn economy, nobody was tackling the much larger challenge of the local economy.

To build a data graph for the local economy requires fundamentally rethinking how to build a data asset for these markets. Traditional data vendors scrape LinkedIn and company websites and call it a day. Datalane starts there too, pulling from Google Maps, Yelp, Facebook, and review sites, but then they go much deeper. They're ingesting government licensing data, health inspection records, franchise disclosure documents, lease agreements, even physical government record books that they digitize in-house (yes, they literally ship paper to their office and scan it). They're stitching together online signals with offline records to build what is essentially a proprietary knowledge graph of the entire local economy. This might seem like a lot…but that’s what it takes to digitize this part of the economy.

The result is accuracy that blows customers away. When ShopMonkey switched to Datalane, their connect rates jumped from 30% to over 80%. Olo, which sells online ordering software to restaurants, saw their SDR connect rates triple—from 5% to 15%—while simultaneously cutting prospect research time (20% of rep time previously spent manually searching for contact info). DoorDash (no link required), which had built its own internal data infrastructure (because that's what companies with DoorDash's resources do), ultimately became a Datalane customer because even they couldn't maintain the same data quality at scale.

Beyond lists: building the GTM orchestration layer for AI

Datalane is constructing a real-time knowledge graph of offline business operations: who owns what, how franchise hierarchies work, when locations open and close, and how ownership changes hands. This goes well beyond the default static data you download once, and is instead a living system that continuously refreshes, flags triggers (new openings, ownership changes, operational status shifts), and syncs directly into your CRM and Data Warehouse with the relevant tags and scoring already applied. This self-reinforcing dataset enables lookalike-style discovery, allowing teams to consistently find the right needles across millions of entities through continuously improving data and feedback loops rather than better models alone.

Datalane is eventually building towards becoming a workflow platform. With it you’ll be able to build an audience of "independent dental practices in Texas with 2-5 chairs that opened in the last 90 days," and Datalane won’t just give you a CSV, it will automatically enrich those accounts, keep the contact information fresh, and trigger outbound campaigns when new prospects match your criteria. For RevOps teams, this is the difference between buying a dataset and having an operating system.

Datalane’s exceptional team

David and Ganesh move unbelievably fast. David is the kind of founder who internalizes feedback rapidly and adjusts course without losing conviction. Ganesh is the technical anchor — the kind of CTO who, when he needs to hire five engineers, will source them and close them in half the expected time. Multiple founders in our network who know them gave us strong references. But what impressed us most is that they've maintained almost fanatical operating discipline while quickly scaling top line.

David famously doesn't leave a three-block radius in Manhattan. That level of focus is rare, and in a market where data quality compounds over time, focus is the entire game.

So today we’re excited to announce that we’re leading Datalane's $22 million Series A, alongside Harry Stebbings and existing investors.

Datalane is attacking a market that's enormous (millions of businesses globally), underserved (legacy vendors are stuck in the 1990s), and increasingly critical (as vertical SaaS eats the world). As all of you know by now, we're quite bullish on companies that are building proprietary data moats in markets where AI creates new leverage. Datalane is the textbook example.

Underlying all of our investments is a pretty core belief that as AI models commoditize, durable value accrues to companies that enable the aggregation, governance, and activation of proprietary data at scale. Across the founders we’ve partnered with there’s a consistent focus on foundational infrastructure that helps organizations collect, transform, and operationalize their most valuable asset: their own data. Datalane is going straight for the heart of it, building a continually evolving  set of high-quality, compliant, and trustworthy data.

We're thrilled to be partnering with David, Ganesh, and the team!