Ascend Series-A

This is the Ascend pitch deck to raise a $15m series-a round in 2019.

About

Ascend provides the world’s first Autonomous Dataflow Service, enabling data engineers to build, scale, and operate continuously optimized, Apache Spark-based pipelines with 85% less code. Running natively in Microsoft Azure, Amazon Web Services, and Google Cloud Platform, Ascend combines declarative configurations and automation to manage the

underlying cloud infrastructure, optimize pipelines, and eliminate maintenance across the entire data lifecycle. Ascend is backed by the world’s leading venture capital firms, including Accel, Sequoia Capital, Lightspeed Venture Partners, and 8VC; and supported by notable advisors, such as Kevin Scott, CTO of Microsoft; Maynard Webb, Board Member Salesforce, Visa; Scott McNealy, former Sun Microsystems CEO; Luanne Dauber, former CMO at Confluent and VP Marketing at Pure Storage; and Deep Nishar, Senior Managing Partner of Softbank Vision Fund. For more information about Ascend, visit www.ascend.io.

The tools for wrangling big sets of data and making sense of the information within them are changing faster than most businesses can keep up with, according to Sean Knapp, whose startup Ascend helps companies build and scale their data infrastructure with less code.

“The technologies are so insanely powerful, but they’re also hard to use,” Knapp told Business Insider. “It’s like trying to drive a F1 sports car. If you know exactly what you’re doing, and you know all the lines on the track, you’ll be good. But most people aren’t race-car drivers.”

Ascend is essentially autopilot for data architecture. Its service lets customers create pipelines for extracting data from various sources and loading it into other databases. They might be moving their technology stack from servers on the premises to the cloud, or gathering user data that they want to process and parse in separate analytics tools. The company says it builds these pipelines using 85% less code, reducing the number of bugs for data engineers to fix.

The startup has raised $19 million in financing from top-tier venture-capital firms, including Accel, Sequoia Capital, and Lightspeed Venture Partners. Knapp said he has been courting these partners from the company’s earliest stages, before there was a pitch deck to share. The company breezed through its latest round of funding by choosing investors who share their conviction, according to Knapp.

“Our take is that the next massive wave of innovation will require some really hard technology,” Knapp said. “The investors that this attracts are the ones who are really well-educated on the industry, know how hard these problems are to solve and how much demand there is in the market from people who can’t solve these problems.”

The two-time founder met with investors at Sequoia and Lightspeed as part of his market research to determine the demand for data-pipeline technology. After the company raised a seed round in 2015, a team of eight employees hunkered down in a spare office it sublet.

“Why we stayed stealth for so long is we just kept our heads down, and we built really hard technology,” Knapp said. Ascend also added a couple of developer partners to test the product and prove out the data efficiencies that the company promised.

The slide that hit home with the VC firm

When the time came for Ascend to raise its latest round of funding, Sequoia, Lightspeed, and 8VC — a boutique firm created by Palantir cofounder Joe Lonsdale — signed on to reinvest. Knapp said he pitched only one other firm, Accel, and received a term sheet by the end of the day. The Palo Alto, California-based Accel led the round.

Knapp pointed to the third slide in the series A pitch deck, which an employee researched and “specifically put together for Accel,” he said. It shows all the data companies that Accel has previously invested in, which Ascend sees as potential customers or partners who could bundle its data-pipeline service into their offerings. The slide also listed how much venture-capital funding each has raised.

“The whole idea was, ‘Hey, we hope we’re all in this room because we deeply believe in the data opportunity, and we believe that there’s huge, huge gains to be had,'” Knapp said. “And to convey that point, here’s all the money you’ve already invested in the data ecosystem.”

It worked. Knapp said he completed the fundraise in three weeks, the shortest cycle that the entrepreneur said he has ever done.

Funding rounds

Pitch deck review summary

Slide by slide review

Cover is fine. I would prob lean more into the product image rather than the mountains.

So they are starting with the team. Not many people do that. Normally you do that when the team is solid and you want investors to take note.

Headers should go in one place and that is in the header, like the top.

I like lovely photos of the team so you humanize things. It’s like the people on conference calls that always want me to turn on video so they can see me. WTF, I had a lady who kept insisting on some random call today and she was so excited to see me. I refused lolz (I walk and talk).

What’s the point? A crapple tonne of companies are raising? I don’t get it which is ironic since this slide clearly took ages to make and I can tell they actually made it natively. If you are going to spend a lot of time making a slide, make sure you make it clear to investors.

I hate quotes. Don’t do it. It’s super lazy and it’s not inspiring like you might hope.

I hate the headers. Write the point you want to make.

Ok, so if you are going to write a hodge-podge of quotes, make some effort to structure the slide. Aggregate them into three points. Investors can read the headers rapidly and choose to dive deeper.

When I see a slide like this, my first impression is that there is something to read.

My second feeling is ummm, you actually want me to read all this?

See, it’s one thing to write content, it is another for people to read it. Focus on the later.

This is a crappy slide and I don’t want to read it.

The image looks like it might be interesting, but it needs to be scaled more to be legible. A large piece of advice is to focus on how long it takes to read a deck, not how many slides there are.

The black box might be interesting but the structure of it is not good.

Ok, the image on the leftr is just the same as the slide before, so it is not needed. The black code box isn’t legible, so it’s pointless.

I would just structure up the slide as 4 steps on the left and use the whole slide to illustrate it. Simples.

Honestly, so much about a good deck is structure. It sounds like really lame advice but it’s so powerful.

Whenever i see a competitive advantage slide I die a little inside as they are almost always rubbish and most people don’t understand academic literature on the topic.

The points seem solid on the slide, I’m not clear if they are features of actual defensible points.

Reducing project times by 90% (depending what it is compared to) is pretty cool. That is 10x better.

This is what the competitive landscape?

I do very much like the fact that they have characterized the competition into 4 buckets firstly, and then they have a column that details their comments. I wonder if this is a decent template slide I should teach? What do you think?

Big data is such a cluster. I love jokes.

I don’t like end slides in general, but at least this is funny.

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