Feb
24
2021
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Hydrolix snares $10M seed to lower the cost of processing log data at scale

Many companies spend a significant amount of money and resources processing data from logs, traces and metrics, forcing them to make trade-offs about how much to collect and store. Hydrolix, an early stage startup, announced a $10 million seed round today to help tackle logging at scale, while using unique technology to lower the cost of storing and querying this data.

Wing Venture Capital led the round with help from AV8 Ventures, Oregon Venture Fund and Silicon Valley Data Capital.

Company CEO and co-founder Marty Kagan noted that in his previous roles, he saw organizations with tons of data in logs, metrics and traces that could be valuable to various parts of the company, but most organizations couldn’t afford the high cost to maintain these records for very long due to the incredible volume of data involved. He started Hydrolix because he wanted to change the economics to make it easier to store and query this valuable data.

“The classic problem with these cluster-based databases is that they’ve got locally attached storage. So as the data set gets larger, you have no choice but to either spend a ton of money to grow your cluster or separate your hot and cold data to keep your costs under control,” Kagan told me.

What’s more, he says that when it comes to querying, the solutions out there like BigQuery and Snowflake are not well suited for this kind of data. “They rely really heavily on caching and bulk column scans, so they’re not really useful for […] these infrastructure plays where you want to do live stream ingest, and you want to be able to do ad hoc data exploration,” he said.

Hydrolix wanted to create a more cost-effective way of storing and querying log data, while solving these issues with other tooling. “So we built a new storage layer which delivers […] SSD-like performance using nothing but cloud storage and diskless spot instances,” Kagan explained. He says that this means that there is no caching or column scales, enabling them to do index searches. “You’re getting the low cost, unlimited retention benefits of cloud storage, but with the interactive performance of fully indexed search,” he added.

Peter Wagner, founding partner at investor Wing Venture Capital, says that the beauty of this tool is that it eliminates tradeoffs, while lowering customers overall data processing costs. “The Hydrolix team has built a real-time data platform optimized not only to deliver superior performance at a fraction of the cost of current analytics solutions, but one architected to offer those same advantages as data volumes grow by orders of magnitude,” Wagner said in a statement.

It’s worth pointing out that in the past couple of weeks SentinelOne bought high speed logging platform Scalyr for $155 million, then CrowdStrike grabbed Humio, another high speed logging tool for $400 million, so this category is getting attention.

The product is currently compatible with AWS and offered through the Amazon Marketplace, but Kagan says they are working on versions for Azure and Google Cloud and expect to have those available later this year. The company was founded at the end of 2018 and currently has 20 employees spread out over six countries with headquarters in Portland, Oregon.

Feb
18
2021
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Torii announces $10M Series A to automate SaaS management

Today, that software is offered as a cloud service should be pretty much considered a given. Certainly any modern tooling is going to be SaaS, and as companies and employees add services, it becomes a management nightmare. Enter Torii, an early-stage startup that wants to make it easier to manage SaaS bloat.

Today, the company announced a $10 million Series A investment led by Wing Venture Capital, with participation from prior investors Entree Capital, Global Founders Capital, Scopus Ventures and Uncork Capital. The investment brings the total raised to $15 million, according to the company. Under the terms of the deal, Wing partner Jake Flomenberg is joining the board.

Uri Haramati, co-founder and CEO, is a serial entrepreneur who helped launch Houseparty and Meerkat. As a serial founder, he says that he and his co-founders saw firsthand how difficult it was to manage their companies’ SaaS applications, and the idea for Torii developed from that.

“We all felt the changes around SaaS and managing the tools that we were using. We were all early adopters of SaaS. We all [took advantage of SaaS] to scale our companies and we felt the same thing: The fact is that you just can’t add more people who manage more software, it just doesn’t scale,” Haramati told me.

He said they started Torii with the idea of using software to control the SaaS sprawl they were experiencing. At the heart of the idea was an automation engine to discover and manage all of the SaaS tools inside an organization. Once you know what you have, there is a no-code workflow engine to create workflows around those tools for key activities like onboarding or offboarding employees.

Torii no code workflow engine.

Torii Workflow Engine. Image Credits: Torii

The approach seems to be working. As the pandemic struck in 2020, more companies than ever needed to control and understand the SaaS tooling they had, and revenue grew 400% YoY last year. Customers include Delivery Hero, Chewy, Monday.com and Palo Alto Networks.

The company also doubled its employees from a dozen with which they started last year, with plans to get to 60 people by the end of this year. As they do that, as experienced entrepreneurs, Haramati told me they already understood the value of developing a diverse and inclusive workforce, certainly around gender. Today, the team is 25 people with 10 being women and they are working to improve those ratios as they continue to add new people.

Flomenberg invested in Torii because he was particularly impressed with the automation aspect of the company and how it took a holistic approach to the SaaS management problem, rather than attempting to solve one part of it. “When I met Uri, he described this vision. It was really to become the operating system for SaaS. It all starts with the right data. You can trust data that is gathered from [multiple] sources to really build the right picture and pull it together. And then they took all those signals and they built a platform that is built on automation,” he said.

Haramati admits that it’s challenging to scale in the midst of a pandemic, but the company is growing and is already working to expand the platform to include product recommendations and help with compliance and cost control.

Jan
27
2021
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Pinecone lands $10M seed for purpose-built machine learning database

Pinecone, a new startup from the folks who helped launch Amazon SageMaker, has built a vector database that generates data in a specialized format to help build machine learning applications faster, something that was previously only accessible to the largest organizations. Today the company came out of stealth with a new product and announced a $10 million seed investment led by Wing Venture Capital.

Company co-founder Edo Liberty says that he started the company because of this fundamental belief that the industry was being held back by the lack of wider access to this type of database. “The data that a machine learning model expects isn’t a JSON record, it’s a high dimensional vector that is either a list of features or what’s called an embedding that’s a numerical representation of the items or the objects in the world. This [format] is much more semantically rich and actionable for machine learning,” he explained.

He says that this is a concept that is widely understood by data scientists, and supported by research, but up until now only the biggest and technically superior companies like Google or Pinterest could take advantage of this difference. Liberty and his team created Pinecone to put that kind of technology in reach of any company.

The startup spent the last couple of years building the solution, which consists of three main components. The main piece is a vector engine to convert the data into this machine-learning ingestible format. Liberty says that this is the piece of technology that contains all the data structures and algorithms that allow them to index very large amounts of high dimensional vector data, and search through it in an efficient and accurate way.

The second is a cloud hosted system to apply all of that converted data to the machine learning model, while handling things like index lookups along with the pre- and post-processing — everything a data science team needs to run a machine learning project at scale with very large workloads and throughputs. Finally, there is a management layer to track all of this and manage data transfer between source locations.

One classic example Liberty uses is an eCommerce recommendation engine. While this has been a standard part of online selling for years, he believes using a vectorized data approach will result in much more accurate recommendations and he says the data science research data bears him out.

“It used to be that deploying [something like a recommendation engine] was actually incredibly complex, and […] if you have access to a production grade database, 90% of the difficulty and heavy lifting in creating those solutions goes away, and that’s why we’re building this. We believe it’s the new standard,” he said.

The company currently has 10 people including the founders, but the plan is to double or even triple that number, depending on how the year goes. As he builds his company as an immigrant founder — Liberty is from Israel — he says that diversity is top of mind. He adds that it’s something he worked hard on at his previous positions at Yahoo and Amazon as he was building his teams at those two organizations. One way he is doing that is in the recruitment process. “We have instructed our recruiters to be proactive [in finding more diverse applicants], making sure they don’t miss out on great candidates, and that they bring us a diverse set of candidates,” he said.

Looking ahead to post-pandemic, Liberty says he is a bit more traditional in terms of office versus home, and that he hopes to have more in-person interactions. “Maybe I’m old fashioned but I like offices and I like people and I like to see who I work with and hang out with them and laugh and enjoy each other’s company, and so I’m not jumping on the bandwagon of ‘let’s all be remote and work from home’.”

Jul
08
2020
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SetSail raises raises $7M to change how sales teams are compensated

Most sales teams earn a commission after a sale closes, but nothing prior to that. Yet there are a variety of signals along the way that indicate the sales process is progressing, and SetSail, a startup from some former Google engineers, is using machine learning to figure out what those signals are, and how to compensate salespeople as they move along the path to a sale, not just after they close the deal.

Today, the startup announced a $7 million investment led by Wing Venture Capital with help from Operator Collective and Team8. Under the terms of the deal, Leyla Seka from Operator will be joining the board. Today’s investment brings the total raised to $11 million, according to the company.

CEO and co-founder Haggai Levi says his company is based on the idea that commission alone is not a good way to measure sales success, and that it is in fact a lagging indicator. “We came up with a different approach. We use machine learning to create progress-based incentives,” Levi explained.

To do that they rely on machine learning to discover the signals that are coming from the customer that indicate that the deal is moving forward, and using a points system, companies can begin compensating reps on hitting these milestones, even before the sale closes.

The seeds for the idea behind SetSail were planted years ago when the three founders were working at Google tinkering with ways to motivate sales reps beyond pure commission. From a behavioral perspective, Levi and his co-founders found that reps were taking fewer risks with a pure commission approach and they wanted to find a way to change that. The incremental compensation system achieves that.

“If I’m closing the deal, I’m getting my commission. If I’m not closing the deal, I’m getting nothing. That means from a behavioral point of view, I would take the shortest path to win a deal, and I would take the minimum risk possible. So if there’s a competitive situation I will try to avoid that,” he said.

They look at things like appointments, emails and call transcripts. The signals will vary by customer. One may find an appointment with CIO is a good signal a deal is on the right trajectory, but to avoid having reps gaming the system by filling the CRM with the kinds of positive signals the company is looking for, they only rely on objective data, rather than any kind of self-reporting information from reps themselves.

The team eventually built a system like this inside Google, and in 2018, left to build a solution for the rest of the world that does something similar.

As the company grows, Levi says he is building a diverse team, not only because it’s the right thing to do, but because it simply makes good business sense. “The reality is that we’re building a product for a diverse audience, and if we don’t have a diverse team we would never be able to build the right product,” he explained.

The company’s unique approach to sales compensation is resonating with customers like Dropbox, Lyft and Pendo, who are looking for new ways to motivate sales teams, especially during a pandemic when there may be a longer sales cycle. This kind of system provides a way to compensate sales teams more incrementally and reward positive approaches that have proven to result in sales.

Nov
18
2015
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Oak Labs, With $4.1M In Seed, Launches A Smart Fitting Room Mirror

main Though the world of fashion is trying desperately to catch up to the digital age, retail is still fundamentally unchanged. Oak Labs is looking to shake things up with a smart mirror to be placed in the fitting room of clothing stores and boutiques.
The company just raised $4.1 million in seed funding led by Wing Venture Capital, and is piloting the software with Ralph Lauren in its Polo NYC… Read More

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