Dec
02
2020
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Fylamynt raises $6.5M for its cloud workflow automation platform

Fylamynt, a new service that helps businesses automate their cloud workflows, today announced both the official launch of its platform as well as a $6.5 million seed round. The funding round was led by Google’s AI-focused Gradient Ventures fund. Mango Capital and Point72 Ventures also participated.

At first glance, the idea behind Fylamynt may sound familiar. Workflow automation has become a pretty competitive space, after all, and the service helps developers connect their various cloud tools to create repeatable workflows. We’re not talking about your standard IFTTT- or Zapier -like integrations between SaaS products, though. The focus of Fylamynt is squarely on building infrastructure workflows. While that may sound familiar, too, with tools like Ansible and Terraform automating a lot of that already, Fylamynt sits on top of those and integrates with them.

Image Credits: Fylamynt

“Some time ago, we used to do Bash and scripting — and then [ … ] came Chef and Puppet in 2006, 2007. SaltStack, as well. Then Terraform and Ansible,” Fylamynt co-founder and CEO Pradeep Padala told me. “They have all done an extremely good job of making it easier to simplify infrastructure operations so you don’t have to write low-level code. You can write a slightly higher-level language. We are not replacing that. What we are doing is connecting that code.”

So if you have a Terraform template, an Ansible playbook and maybe a Python script, you can now use Fylamynt to connect those. In the end, Fylamynt becomes the orchestration engine to run all of your infrastructure code — and then allows you to connect all of that to the likes of DataDog, Splunk, PagerDuty Slack and ServiceNow.

Image Credits: Fylamynt

The service currently connects to Terraform, Ansible, Datadog, Jira, Slack, Instance, CloudWatch, CloudFormation and your Kubernetes clusters. The company notes that some of the standard use cases for its service are automated remediation, governance and compliance, as well as cost and performance management.

The company is already working with a number of design partners, including Snowflake.

Fylamynt CEO Padala has quite a bit of experience in the infrastructure space. He co-founded ContainerX, an early container-management platform, which later sold to Cisco. Before starting ContainerX, he was at VMWare and DOCOMO Labs. His co-founders, VP of Engineering Xiaoyun Zhu and CTO David Lee, also have deep expertise in building out cloud infrastructure and operating it.

“If you look at any company — any company building a product — let’s say a SaaS product, and they want to run their operations, infrastructure operations very efficiently,” Padala said. “But there are always challenges. You need a lot of people, it takes time. So what is the bottleneck? If you ask that question and dig deeper, you’ll find that there is one bottleneck for automation: that’s code. Someone has to write code to automate. Everything revolves around that.”

Fylamynt aims to take the effort out of that by allowing developers to either write Python and JSON to automate their workflows (think “infrastructure as code” but for workflows) or to use Fylamynt’s visual no-code drag-and-drop tool. As Padala noted, this gives developers a lot of flexibility in how they want to use the service. If you never want to see the Fylamynt UI, you can go about your merry coding ways, but chances are the UI will allow you to get everything done as well.

One area the team is currently focusing on — and will use the new funding for — is building out its analytics capabilities that can help developers debug their workflows. The service already provides log and audit trails, but the plan is to expand its AI capabilities to also recommend the right workflows based on the alerts you are getting.

“The eventual goal is to help people automate any service and connect any code. That’s the holy grail. And AI is an enabler in that,” Padala said.

Gradient Ventures partner Muzzammil “MZ” Zaveri echoed this. “Fylamynt is at the intersection of applied AI and workflow automation,” he said. “We’re excited to support the Fylamynt team in this uniquely positioned product with a deep bench of integrations and a nonprescriptive builder approach. The vision of automating every part of a cloud workflow is just the beginning.”

The team, which now includes about 20 employees, plans to use the new round of funding, which closed in September, to focus on its R&D, build out its product and expand its go-to-market team. On the product side, that specifically means building more connectors.

The company offers both a free plan as well as enterprise pricing and its platform is now generally available.

Oct
21
2020
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Secureframe raises $4.5M to help businesses speed up their compliance audits

While certifications for security management practices like SOC 2 and ISO 27001 have been around for a while, the number of companies that now request that their software vendors go through (and pass) the audits to be in compliance with these continues to increase. For a lot of companies, that’s a harrowing process, so it’s maybe no surprise that we are also seeing an increase in startups that aim to make this process easier. Earlier this month, Strike Graph, which helps automate security audits, announced its $3.9 million round, and today, Secureframe, which also helps businesses get and maintain their SOC 2 and ISO 27001 certifications, is announcing a $4.5 million round.

Secureframe’s round was co-led by Base10 Partners and Google’s AI-focused Gradient Ventures fund. BoxGroup, Village Global, Soma Capital, Liquid2, Chapter One, Worklife Ventures and Backend Capital participated. Current customers include Stream, Hasura and Benepass.

Image Credits: Secureframe

Shrav Mehta, the company’s co-founder and CEO, spent time at a number of different companies, but he tells me the idea for Secureframe was mostly born during his time at direct-mail service Lob.

“When I was at Lob, we dealt with a lot of issues around security and compliance because we were sometimes dealing with very sensitive data, and we’d hop on calls with customers, had to complete thousand-line security questionnaires, do exhaustive security reviews, and this was a lot for a startup of our size at the time. But it’s just what our customers needed. So I started to see that pain,” Mehta said.

Secureframe co-founder and CEO Shrav Mehta

Secureframe co-founder and CEO Shrav Mehta

After stints at Pilot and Scale AI after he left Lob in 2017 — and informally helping other companies manage the certification process — he co-founded Secureframe together with the company’s CTO, Natasja Nielsen.

“Because Secureframe is basically adding a lot of automation with our software — and making the process so much simpler and easier — we’re able to bring the cost down to a point where this is something that a lot more companies can afford,” Mehta explained. “This is something that everyone can get in place from day one, and not really have to worry that, ‘hey, this is going to take all of our time, it’s going to take a year, it’s going to cost a lot of money.’ […] We’re trying to solve that problem to make it super easy for every organization to be secure from day one.”

The main idea here is to make the arcane certification process more transparent and streamline the process by automating many of the more labor-intensive tasks of getting ready for an audit (and it’s virtually always the pre-audit process that takes up most of the time). Secureframe does so by integrating with the most-often used cloud and SaaS tools (it currently connects to about 25 services) and pulling in data from them to check up on your security posture.

“It feels a lot like a QuickBooks or TurboTax-like experience, where we’ll essentially ask you to enter basic details about your business. We try to autofill as much of it as possible from third-party sources — then we ask you to connect up all the integrations your business uses,” Mehta explained.

The company plans to use much of the new funding to staff up and build out these integrations. Over time, it will also add support for other certifications like PCI, HITRUST and HIPAA.

Oct
08
2020
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Headroom, which uses AI to supercharge videoconferencing, raises $5M

Videoconferencing has become a cornerstone of how many of us work these days — so much so that one leading service, Zoom, has graduated into verb status because of how much it’s getting used.

But does that mean videoconferencing works as well as it should? Today, a new startup called Headroom is coming out of stealth, tapping into a battery of AI tools — computer vision, natural language processing and more — on the belief that the answer to that question is a clear — no bad Wi-Fi interruption here — “no.”

Headroom not only hosts videoconferences, but then provides transcripts, summaries with highlights, gesture recognition, optimised video quality and more, and today it’s announcing that it has raised a seed round of $5 million as it gears up to launch its freemium service into the world.

You can sign up to the waitlist to pilot it, and get other updates here.

The funding is coming from Anna Patterson of Gradient Ventures (Google’s AI venture fund); Evan Nisselson of LDV Capital (a specialist VC backing companies building visual technologies); Yahoo founder Jerry Yang, now of AME Cloud Ventures; Ash Patel of Morado Ventures; Anthony Goldbloom, the co-founder and CEO of Kaggle.com; and Serge Belongie, Cornell Tech associate dean and professor of Computer Vision and Machine Learning.

It’s an interesting group of backers, but that might be because the founders themselves have a pretty illustrious background with years of experience using some of the most cutting-edge visual technologies to build other consumer and enterprise services.

Julian Green — a British transplant — was most recently at Google, where he ran the company’s computer vision products, including the Cloud Vision API that was launched under his watch. He came to Google by way of its acquisition of his previous startup Jetpac, which used deep learning and other AI tools to analyze photos to make travel recommendations. In a previous life, he was one of the co-founders of Houzz, another kind of platform that hinges on visual interactivity.

Russian-born Andrew Rabinovich, meanwhile, spent the last five years at Magic Leap, where he was the head of AI, and before that, the director of deep learning and the head of engineering. Before that, he too was at Google, as a software engineer specializing in computer vision and machine learning.

You might think that leaving their jobs to build an improved videoconferencing service was an opportunistic move, given the huge surge of use that the medium has had this year. Green, however, tells me that they came up with the idea and started building it at the end of 2019, when the term “COVID-19” didn’t even exist.

“But it certainly has made this a more interesting area,” he quipped, adding that it did make raising money significantly easier, too. (The round closed in July, he said.)

Given that Magic Leap had long been in limbo — AR and VR have proven to be incredibly tough to build businesses around, especially in the short to medium-term, even for a startup with hundreds of millions of dollars in VC backing — and could have probably used some more interesting ideas to pivot to; and that Google is Google, with everything tech having an endpoint in Mountain View, it’s also curious that the pair decided to strike out on their own to build Headroom rather than pitch building the tech at their respective previous employers.

Green said the reasons were two-fold. The first has to do with the efficiency of building something when you are small. “I enjoy moving at startup speed,” he said.

And the second has to do with the challenges of building things on legacy platforms versus fresh, from the ground up.

“Google can do anything it wants,” he replied when I asked why he didn’t think of bringing these ideas to the team working on Meet (or Hangouts if you’re a non-business user). “But to run real-time AI on video conferencing, you need to build for that from the start. We started with that assumption,” he said.

All the same, the reasons why Headroom are interesting are also likely going to be the ones that will pose big challenges for it. The new ubiquity (and our present lives working at home) might make us more open to using video calling, but for better or worse, we’re all also now pretty used to what we already use. And for many companies, they’ve now paid up as premium users to one service or another, so they may be reluctant to try out new and less-tested platforms.

But as we’ve seen in tech so many times, sometimes it pays to be a late mover, and the early movers are not always the winners.

The first iteration of Headroom will include features that will automatically take transcripts of the whole conversation, with the ability to use the video replay to edit the transcript if something has gone awry; offer a summary of the key points that are made during the call; and identify gestures to help shift the conversation.

And Green tells me that they are already also working on features that will be added into future iterations. When the videoconference uses supplementary presentation materials, those can also be processed by the engine for highlights and transcription too.

And another feature will optimize the pixels that you see for much better video quality, which should come in especially handy when you or the person/people you are talking to are on poor connections.

“You can understand where and what the pixels are in a video conference and send the right ones,” he explained. “Most of what you see of me and my background is not changing, so those don’t need to be sent all the time.”

All of this taps into some of the more interesting aspects of sophisticated computer vision and natural language algorithms. Creating a summary, for example, relies on technology that is able to suss out not just what you are saying, but what are the most important parts of what you or someone else is saying.

And if you’ve ever been on a videocall and found it hard to make it clear you’ve wanted to say something, without straight-out interrupting the speaker, you’ll understand why gestures might be very useful.

But they can also come in handy if a speaker wants to know if he or she is losing the attention of the audience: The same tech that Headroom is using to detect gestures for people keen to speak up can also be used to detect when they are getting bored or annoyed and pass that information on to the person doing the talking.

“It’s about helping with EQ,” he said, with what I’m sure was a little bit of his tongue in his cheek, but then again we were on a Google Meet, and I may have misread that.

And that brings us to why Headroom is tapping into an interesting opportunity. At their best, when they work, tools like these not only supercharge videoconferences, but they have the potential to solve some of the problems you may have come up against in face-to-face meetings, too. Building software that actually might be better than the “real thing” is one way of making sure that it can have staying power beyond the demands of our current circumstances (which hopefully won’t be permanent circumstances).

Mar
26
2020
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Kaizo raises $3M for its AI-based tools to improve customer service support teams

CRM has for years been primarily a story of software to manage customer contacts, data to help agents do their jobs, and tools to manage incoming requests and outreach strategies. Now to add to that we’re starting to see a new theme: apps to help agents track how they work and to work better.

Today comes the latest startup in that category, a Dutch company called Kaizo, which uses AI and gamification to provide feedback on agents’ work, tips on what to do differently, and tools to set and work to goals — all of which can be used remotely, in the cloud. Today, it is announcing $3 million in a seed round of funding co-led by Gradient — Google’s AI venture fund — and French VC Partech. 

And along with the seed round, Kaizo (which rebranded last week from its former name, Ticketless) is announcing that Christoph Auer-Welsbach, a former partner at IBM Ventures, is joining the company as a co-founder, alongside founder Dominik Blattner. 

Although this is just a seed round, it’s coming after a period of strong growth for the company. Kaizo has already 500 companies including Truecaller, SimpleSurance, Miro, CreditRepairCloud, Justpark, Festicket and Nmbrs are using its software, covering “thousands” of customer support agents, which use a mixture of free and paid tools that integrate with established CRM software from the likes of Salesforce, Zendesk and more.

Customer service, and the idea of gamifying it to motivate employees, might feel like the last thing on people’s minds at the moment, but it is actually timely and relevant to our current state in responding to and living with the coronavirus.

People are spending much more time at home, and are turning to the internet and remote services to get what they need, and in many cases are finding that their best-laid plans are now in freefall. Both of these are driving a lot of traffic to sites and primarily customer support centers, which are getting overwhelmed with people reaching out for help.

And that’s before you consider how customer support teams might be impacted by coronavirus and the many mandates we’ve had to stay away from work, and the stresses they may be under.

“In our current social climate, customer support is an integral part of a company’s stability and growth that has embraced remote work to meet the demands of a globalized customer-base,” said Dominik Blattner, founder of Kaizo, in a statement. “With the rise of support teams utilizing a digital workplace, providing standards to measure an agent’s performance has never been more important. KPIs provide these standards, quantifying the success, achievement and contribution of each team member.”

On a more general level, Kaizo is also changing the conversation around how to improve one’s productivity. There has been a larger push for “quantified self” platforms, which has very much played out both in workplaces and in our personal lives, but a lot of services to track performance have focused on both managers and employees leaning in with a lot of input. That means if they don’t set aside the time to do that, the platforms never quite work the way they should.

This is where the AI element of Kaizo plays a key role, by taking on the need to proactively report into a system.

“This is how we’re distinct,” Auer-Welsbach said in an interview. “Normally KPIs are top-down. They are about people setting goals and then reporting they’ve done something. This is a bottom-up approach. We’re not trying to change employees’ behaviour. We plug into whatever environment they are using, and then our tool monitors. The employee doesn’t have to report or measure anything. We track clicks on the CRM, ticketing, and more, and we analyse all that.” He notes that Kaizo is looking at up to 50 datapoints in its analysis.

“We’re excited about Kaizo’s novel approach to applying AI to existing ticket data from platforms like Zendesk and Salesforce to optimize the customer support workflow,” said Darian Shirazi, General Partner at Gradient Ventures, in a statement. “Using machine learning, Kaizo understands which behaviors in customer service tickets lead to better outcomes for customers and then guides agents to replicate that using ongoing game mechanics. Customer support and service platforms today are failing to leverage data in the right way to make the life of agents easier and more effective. The demand Kaizo has seen since they launched on the Zendesk Marketplace shows agents have been waiting for such a solution for some time.”

Kaizo is not the only startup to have identified the area of building new services to improve the performance of customer support teams. Assembled earlier this month also raised $3.1 million led by Stripe for what it describes as the “operating system” for customer support.

May
14
2019
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Algorithmia raises $25M Series B for its AI automation platform

Algorithmia, a Seattle-based startup that offers a cloud-agnostic AI automation platform for enterprises, today announced a $25 million Series B funding round led by Norwest Partners. Madrona, Gradient Ventures, Work-Bench, Osage University Partners and Rakuten Ventures also participated in this round.

While the company started out five years ago as a marketplace for algorithms, it now mostly focuses on machine learning and helping enterprises take their models into production.

“It’s actually really hard to productionize machine learning models,” Algorithmia CEO Diego Oppenheimer told me. “It’s hard to help data scientists to not deal with data infrastructure but really being able to build out their machine learning and AI muscle.”

To help them, Algorithmia essentially built out a machine learning DevOps platform that allows data scientists to train their models on the platform and with the framework of their choice, bring it to Algorithmia — a platform that has already been blessed by their IT departments — and take it into production.

“Every Fortune 500 CIO has an AI initiative but they are bogged down by the difficulty of managing and deploying ML models,” said Rama Sekhar, a partner at Norwest Venture Partners, who has now joined the company’s board. “Algorithmia is the clear leader in building the tools to manage the complete machine learning life cycle and helping customers unlock value from their R&D investments.”

With the new funding, the company will double down on this focus by investing in product development to solve these issues, but also by building out its team, with a plan to double its headcount over the next year. A year from now, Oppenheimer told me, he hopes that Algorithmia will be a household name for data scientists and, maybe more importantly, their platform of choice for putting their models into production.

“How does Algorithmia succeed? Algorithmia succeeds when our customers are able to deploy AI and ML applications,” Oppenheimer said. “And although there is a ton of excitement around doing this, the fact is that it’s really difficult for companies to do so.”

The company previously raised a $10.5 million Series A round led by Google’s AI fund. It’s customers now include the United Nations, a number of U.S. intelligence agencies and Fortune 500 companies. In total, more than 90,000 engineers and data scientists are now on the platform.

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