Slintel scores $20M Series A as buyer intelligence tool gains traction

One clear outcome of the pandemic was pushing more people to do their shopping online, and that was as true for B2B as it was for B2C. Knowing which of your B2B customers are most likely to convert puts any sales team ahead of the game. Slintel, a startup providing that kind of data, announced a $20 million Series A today.

The company has attracted some big-name investors with GGV leading the round and Accel, Sequoia and Stellaris also participating. The investment brings the total raised to over $24 million including a $4.2 million seed round from last November.

That’s a quick turnaround from seed to A, and company founder and CEO Deepak Anchala, says that while he had plenty of runway left from the seed round, the demand was such that it seemed prudent to take the A money sooner than he had planned. “So we had enough cash in the bank, but investors came to us and we got a pretty good valuation compared to the previous round, so we decided to take it and use that money to go faster,” Anchala said.

Certainly the market dynamics were working in Slintel’s favor. Without giving revenue details, Anchala said that revenue grew 5x last year in the middle of the worst of the pandemic. He says that meant buyers were spending less time with sales and marketing folks to understand products and more time online researching on their own.

“So what Slintel does as a product is we mine buyer insights. We understand where the buyers are in their journey, what their pain points are, what products they use, what they need and when they need it. So we understand all of this to create a 360 degree view of the buyer that you provide these insights to sales and marketing teams to help them sell better,” he said.

After growing at such a rapid clip last year, the company expected more modest growth this year at perhaps 3x, but with the added investment, he expects to grow faster again. “With the funding we’re actually looking at much bigger numbers. We’re looking at 5x in our revenue this year, and also trying for 4x revenue next year.”

He says that the money gives him the opportunity to improve the product and put more investment into marketing, which he believes will contribute to additional sales. Since the round closed 6 weeks ago, he says that he has increased his advertising budget and is also hopes to attract customers via SEO, free tools on the company website and events.

The company had 45 employees at the time of its seed round in November and has more than doubled that number in the interim to 100 spread out across 10 cities. He expects to double again by this time next year as the company is growing quickly. As a global company with some employees in India and some in the U.S., he intends to be remote first even after offices begin to reopen in different areas. He says that he plans to have company gatherings each quarter to let people gather in person on occasion.


By Ron Miller

DataFleets keeps private data useful, and useful data private, with federated learning and $4.5M seed

As you may already know, there’s a lot of data out there, and some of it could actually be pretty useful. But privacy and security considerations often put strict limitations on how it can be used or analyzed. DataFleets promises a new approach by which databases can be safely accessed and analyzed without the possibility of privacy breaches or abuse — and has raised a $4.5 million seed round to scale it up.

To work with data, you need to have access to it. If you’re a bank, that means transactions and accounts; if you’re a retailer, that means inventories and supply chains, and so on. There are lots of insights and actionable patterns buried in all that data, and it’s the job of data scientists and their ilk to draw them out.

But what if you can’t access the data? After all, there are many industries where it is not advised or even illegal to do so, such as in health care. You can’t exactly take a whole hospital’s medical records, give them to a data analysis firm, and say “sift through that and tell me if there’s anything good.” These, like many other data sets, are too private or sensitive to allow anyone unfettered access. The slightest mistake — let alone abuse — could have serious repercussions.

In recent years a few technologies have emerged that allow for something better, though: analyzing data without ever actually exposing it. It sounds impossible, but there are computational techniques for allowing data to be manipulated without the user ever actually having access to any of it. The most widely used one is called homomorphic encryption, which unfortunately produces an enormous, orders-of-magnitude reduction in efficiency — and big data is all about efficiency.

This is where DataFleets steps in. It hasn’t reinvented homomorphic encryption, but has sort of sidestepped it. It uses an approach called federated learning, where instead of bringing the data to the model, they bring the model to the data.

DataFleets integrates with both sides of a secure gap between a private database and people who want to access that data, acting as a trusted agent to shuttle information between them without ever disclosing a single byte of actual raw data.

Illustration showing how a model can be created without exposing data.

Image Credits: DataFleets

Here’s an example. Say a pharmaceutical company wants to develop a machine learning model that looks at a patient’s history and predicts whether they’ll have side effects with a new drug. A medical research facility’s private database of patient data is the perfect thing to train it. But access is highly restricted.

The pharma company’s analyst creates a machine learning training program and drops it into DataFleets, which contracts with both them and the facility. DataFleets translates the model to its own proprietary runtime and distributes it to the servers where the medical data resides; within that sandboxed environment, it runs grows into a strapping young ML agent, which when finished is translated back into the analyst’s preferred format or platform. The analyst never sees the actual data, but has all the benefits of it.

Screenshot of the DataFleets interface. Look, it’s the applications that are meant to be exciting.

It’s simple enough, right? DataFleets acts as a sort of trusted messenger between the platforms, undertaking the analysis on behalf of others and never retaining or transferring any sensitive data.

Plenty of folks are looking into federated learning; the hard part is building out the infrastructure for a wide-ranging enterprise-level service. You need to cover a huge amount of use cases and accept an enormous variety of languages, platforms, and techniques, and of course do it all totally securely.

“We pride ourselves on enterprise readiness, with policy management, identity access management, and our pending SOC 2 certification,” said DataFleets COO and co-founder Nick Elledge. “You can build anything on top of DataFleets and plug in your own tools, which banks and hospitals will tell you was not true of prior privacy software.”

But once federated learning is set up, all of a sudden the benefits are enormous. For instance, one of the big issues today in combating COVID-19 is that hospitals, health authorities, and other organizations around the world are having difficulty, despite their willingness, in securely sharing data relating to the virus.

Everyone wants to share, but who sends whom what, where is it kept, and under whose authority and liability? With old methods, it’s a confusing mess. With homomorphic encryption it’s useful but slow. With federated learning, theoretically, it’s as easy as toggling someone’s access.

Because the data never leaves its “home,” this approach is essentially anonoymous and thus highly compliant with regulations like HIPAA and GDPR, another big advantage. Elledge notes: “We’re being used by leading healthcare institutions who recognize that HIPAA doesn’t give them enough protection when they are making a data set available for third parties.”

Of course there are less noble, but no less viable, examples in other industries: wireless carriers could make subscriber metadata available without selling out individuals; banks could sell consumer data without violating anyone in particular’s privacy; bulky datasets like video can sit where they are instead of being duplicated and maintained at great expense.

The company’s $4.5M seed round is seemingly evidence of confidence from a variety of investors (as summarized by Elledge): AME Cloud Ventures (Jerry Yang of Yahoo!) and Morado Ventures, Lightspeed Venture Partners, Peterson Ventures, Mark Cuban, LG, Marty Chavez (President of the Board of Overseers of Harvard), Stanford-StartX fund, and three unicorn founders (Rappi, Quora, and Lucid).

With only 11 full time employees DataFleets appears to be doing a lot with very little, and the seed round should enable rapid scaling and maturation of its flagship product. “We’ve had to turn away or postpone new customer demand to focus on our work with our lighthouse customers,” Elledge said. They’ll be hiring engineers in the U.S. and Europe to help launch the planned self-service product next year.

“We’re moving from a data ownership to a data access economy, where information can be useful without transferring ownership,” said Elledge. If his company’s bet is on target, federated learning is likely to be a big part of that going forward.


By Devin Coldewey

Talking venture, B2B and thesis-driven investment with Work-Bench’s Jon Lehr

Earlier this week, the Equity crew caught up with Work-Bench investor Jon Lehr to get his take on the current market, and how his firm goes about making investment decisions.

The conversation was a treat, so we cut a piece of it off for everyone to listen to. The full audio and a loose transcript are also available after the jump.

What did Danny and Alex learn while talking to Lehr? A few things, including what Seed II-level investments need these days to be attractive (Hint: It’s not a raw ARR threshold), and what’s going on in SaaS today (deals slowing, but not for select founders; relationships are key to doing deals today), and why being a VC is actually work.

But what stood out the most was how Lehr thinks about finding investment opportunities. While some VCs like to cultivate images of being gut-investors, cutting checks based on first meetings and the like, Lehr told TechCrunch about how he researches the market to find pain-points, and then the startups that might solve those issues.

You can listen to that bit of the chat in the clip below:

Extra Crunch subscribers, the rest of the goodies are below. (A big thanks to Danny for cleaning up the written transcript.)

The audio


By Alex Wilhelm