Onymos raises $12M to provide plug-in features for apps

Onymos, a “feature-as-a-service” platform for app development, today announced that it closed a $12 million Series A round led by Great Point Ventures with participation from Benhamou Global Ventures, Engineering Capital, and Industry Ventures. The funds will be put toward product development and ramping up Onymos’ go-to-market activities, according to CEO Shiva Nathan, as well as product and service development.

Nathan made the sales pitch for Onymos via email: “Software-as-a-service sprawl creates complexity and a lack of visibility for enterprises and their apps,” he told TechCrunch. “For an enterprise engineering team, the balance has moved from having to choose from just a few providers for the long term to being overwhelmed by the myriad of choices. Even after picking services, efvery app is at the mercy of trillion-dollar companies who might deprecate those services sooner or later, change how they’re implemented, or introduce updates that are incompatible with your current build environment. We want to shift that paradigm.”

To Nathan’s point, app development can be time-consuming and costly. According to a 2018 Stripe survey, the average developer spends more than 17 hours a week dealing with maintenance issues such as debugging and refactoring. In addition, they spend approximately four hours a week on ‘bad code,’ which equates to nearly $85 billion worldwide in opportunity cost lost annually, Stripe estimates.


Image Credits: Onymos

Onymos aims to help developers offload some of the work by providing off-the-shelf features that can be slotted into new and existing apps. The platform offers login, biometrics, chat, data storage, location services, and notification modules that include the necessary components including the user interface, underlying logic, and server-side functions needed to process data in the cloud (e.g., public clouds like Amazon Web Services, Microsoft Azure, and Google Cloud Platform).

The promise of a maintenance-free, fully secure app development future with Onymos is probably too good to be true — every platform, no matter how holistic, has bugs after all. But what services like Onymos can deliver is time to spend on other, more important product R&D tasks, at least in theory.

“Companies can make their software developers spend time debugging corner cases in Meta’s Graph API for Facebook Login or leverage their creativity and insight to build real value-add capabilities that help their employers win. We think the choice is clear,” Nathan said. “Onymos’ … platform abstracts away the complexity of app development by acting as an integration layer for software vendors and allowing customers to build on a single endpoint. When enterprises use Onymos features, they don’t have to worry about operating system and API updates, device compatibility, or maintenance.”

“Feature-as-a-service” is an old idea, dating back at least several years. There’s Localytics for social and Mapbox for location. Storyteller lets anyone add Instagram-style Stories to their own apps or website. Meanwhile, WorkOS provides developers enterprise capabilities like single sign-on and directory sync to apps.

Nathan sees expansion — particularly in the machine learning space — as Onymos’ path to standing out.

“We plan to expand our product portfolio into the machine learning space, introduce new end-to-end features, and support more development frameworks,” Nathan said. “What we’ve found working with customers in the healthcare space is that there’s so much need for optical character recognition and data extraction. These processes are still too time-consuming for enterprises, and there’s an opportunity to introduce new efficiencies.”

Onymos currently has 30 people on its payroll and expects to have between 40 and 60 by year’s end. To date, the company has raised $15 million.

Robust Intelligence raises $30M Series B to stress test AI models

Robust Intelligence, an AI startup that helps businesses stress test their AI models and prevent them from failing, today announced that it has raised a $30 million Series B funding round led by Tiger Global. Previous investor Sequoia, which led the company’s Series A round, as well as Harpoon Venture Capital and Engineering Capital also participated in this oversubscribed round.

The company was co-founded by Yaron Singer, a tenured professor of Computer Science and Applied Mathematics at Harvard University, and his former student Kojin Oshiba.

Robust Intelligence CEO Yaron Singer. Image Credits: Robust Intelligence

“AI has been this academic endeavor,” said Singer. “When I was doing grad school, it was an academic discipline — it was a vision. And then came the internet, data, Google and data processing — and then it realized its potential in the span of seven, eight years. Now we’re trying to be as rigorous as we are with software development, which humanity has been doing for 60 years, right? We’re trying to play catch-up with AI and it’s a whole different animal.”

As Singer noted, given its statistical nature, AI can exhibit unexpected behavior. At its core, the mission of Robust Intelligence then is to eliminate these AI mistakes.

To do so, the company offers its users what it calls the Robust Intelligence Model Engine (RIME), with what is essentially an AI firewall at its core. This firewall wraps around a company’s AI models and protects it from making mistakes by constantly stress testing these models.

“If you have an AI model and you have data, with a click of a button you run stress testing. We automatically test data and your AI models, both before the model goes into production, as well as while it is in production,” said Singer. The idea here is to automatically find the failure modes of any given model, but also to catch issues like data drift and related issues.

Image Credits: Robust Intelligence

What’s interesting here is that the AI firewall itself is an AI model that predicts whether a data point will lead to a wrong prediction. “This is one of the hardest problems that we’re solving in AI and machine learning,” Singer explained.

“I was first exposed to Robust Intelligence’s capabilities in the company’s early development,” said Tiger Global partner, John Curtius. “After seeing the company and its product grow over the past year it became obvious that Robust Intelligence’s offerings are changing the face of AI reliability, and I knew Tiger Global could help provide key resources.”

The company plans to use the new funding to scale its sales operations, but the majority will go to product and engineering.

InsurGrid raises pre-seed financing to help modernize legacy insurance agents

Insurance agents spend hours handling paperwork and grabbing client information over the phone. A new seed-stage startup, InsurGrid, has developed a software solution to help ease the process, and make it easier for agents to serve existing clients — and secure new ones.

InsurGrid gives agents a personalized platform to collect information from clients, such as date of birth, driver’s license information and policy declaration. This platform helps agents avoid sitting on long calls or managing back-to-back emails, and instead gives them one spot to understand how all their different clients function. It is starting with property and casualty management.

The startup integrates with 85 insurance carriers, serving as the software layer instead of the provider. Using the InsurGrid platform, insurers can ask clients to upload information and within seconds be registered as a policyholder. This essentially turns into a living Rolodex that insurers can use to access information on the account, and offer quotes on a faster rate.

Image Credits: InsurGrid

There’s a monetary benefit in providing better service. Eden Insurance, a customer of InsurGrid, said that people who submit information through the platform converted at an 82% higher rate than those who don’t. Jeremy Eden, the agency owner of Eden Insurance, said they were able to show consumers that its plan was $300 cheaper than its existing rate.

At the heart of InsurGrid is a bet from the founding team that legacy insurance agents aren’t going anywhere. Co-founder/CEO Chase Beach pointed out that the majority of the $684 billion of annual property and casualty insurance premiums in the United States is distributed by approximately 800,000 agents working in 16,000 brokerages. So far, InsurGrid works with more than 150 of those agencies.

When asked if InsurGrid ever had plans to offer its own insurance, similar to insurtech giants Hippo, Lemonade and Root, Beach said that it is solely working on innovating around the sales process for now. He said that these big companies, which have either recently gone public or are planning to, still rely on agents to be successful.

“Instead of us replacing the insurance agent, what if we gave them that same level of technology of a Hippo or large carrier,” Beach said. “And provide them with the digital experiences so they can compete in 2021.”

As time goes on, he sees insurance agents taking the same role that financial advisors or real estate agents take: “very much involved in the process because they are that expert.”

Other startups that have popped up in this space include Gabi, Trellis and Canopy Connect. The differentiator, the team sees, is that Beach comes from a 144-year-old insurance legacy, giving him key insights on how to sell to agents in a successful and effective way. It is starting with sales, but expect InsurGrid to expand to other parts of the insurance process as well.

To help them compete with new and old startups, InsurGrid recently raised $1.3 million in pre-seed financing to help it fulfill its goal to be the “underdog for the underdogs,” Beach said. Investors include Engineering Capital, Hustle Fund, Vess Capital, Sahil Lavingia and Trevor Kienzle.

This VC just closed on $60 million to fund “technical risk,” saying other VCs rarely do the same

Ashmeet Sidana, a longtime VC who struck out on his own in 2015 to form Engineering Capital, just closed his third and newest fund with $60 million in capital commitments from a university endowment, a fund of funds, and three foundations.

Sidana — who previously spent nearly nine years with Foundation Capital and received one of his first limited partner agreements  afterward from Foundation’s legendary founder, Kathryn Gould — says the fund came together despite the pandemic without too much pain.

That’s thanks in part to Sidana’s track record, including the sale of the cloud monitoring startup SignalFx to Splunk for $1 billion after it raised $179 million from VCs, and the sale of the cloud application monitoring startup Netsil by Nutanix for up for $74 million in stock after it raised just $5.7 million. (Engineering Capital was the first investor in both.)

Sidana’s day-to-day work in Palo Alto, Calif. –which centers on working with teams “that you can feed with two pizzas,” yet whose narrow technical insights can have broad applicability — was also an apparent draw. To learn more, we talked earlier today with Sidana, a self-described engineering nerd who studied computer science at Stanford about what “technical insights” have caught his attention most recently.

TC: You talk about pursuing founders with technical insights. Is that not true of most venture capitalists?

AS: No. Silicon Valley is a tech investing ecosystem, but most of its participants aren’t solving hard technical problems. They have market insights or consumer insights. It’s the difference between Google and Facebook. Google figured out how to index better, how to better prioritize a sorting problem. Facebook was started with the consumer insight that people want to be connected with each other. I focus on companies based on technical insights. Most VCs don’t.

TC: What are you looking for exactly?

AS: A team that’s using software or tech to solve a known problem that exists but for which there does not exist a solution. Many such problems exist. For example, we now the future will be multi cloud. Amazon has succeeded wildly with AWS. Microsoft is doing well with its cloud business. Google is catching up to them. Then you have the seven dwarves, including Digital Ocean. It’s a difficult way for enterprises to engage with infrastructure. Another technical problem is rooted in all of us wanting to give our infrastructure over to the cloud but not our data. How do we solve this? Some are solving it legally, some with publicity. But really, it’s a technical problem.

TC: What’s a recent bet you’ve made that has solved a technical problem?

AS: I’m the first investor in Baffle, which is a really interesting company that enables the user of a traditional relational database to see the data but not an administrator. [Editor’s note: the company says it enables the field level protection of data without requiring any application code changes.] Or Robust Intelligence is an even newer investment that’s solving the problem of data contamination in artificial intelligence.

TC: How so?

AS: When you run models and do machine learning, you do cybersecurity and protect them, but what about the data that the AI is working on? They have a killer demo that shows that when you deposit a check with your iPhone, your bank is of course using AI to recognize check and ensure the right amount goes into the proper account. [But a nefarious actor could] procure a small number of pixels that are invisible to the human eye in the photo of check and change the numbers and the routing number. What Robust does is protecting [both the bank and its customers] from that kind of data contamination.

TC: I know you tend to invest very early — often writing the first check. Are you hovering around Stanford all day? How do you find these nascent teams?

AS: I have good relationships with many schools, including [the University of] Michigan, Stanford, I’m involved with the University of Toronto’s Creative Destruction Lab; I keep active relationships with [schools in India]… I spend a lot of time with engineers in academia or industry.

TC: What size checks are you writing to get them started, and how much of their companies do you expect in return?

AS: Most people think investing in technical insights is expensive, but it can be very capital efficient if you are working with software. I’m also looking at companies where you can get to revenue with $1 million and $3 million and funding. That typically takes a small team of five to eight people who you can feed with two people.  Linux was ultimately written by one person. VMWare was started by a technical insight addressed by two people. Google had its earlier stuff working with just Larry and Sergey.

As for ownership, my job is to buy low and sell high. I’m as greedy as the next VC and would love to have as much ownership as I can, but there is no formula.

TC: What’s a mistake you tend to see with new teams?

AS: Gluttony. Most think they have to go after a big market and solve a big problem, but the magic of doing a startup is to focus on an incredibly narrow problem that has broad application. As Steve Jobs used to say it is difficult to throw away features, not to add them.