Category: machine learning

Thomvest invests in Opaque Systems — from friendship to company-building

Thomvest invests in Opaque Systems — from friendship to company-building

Thomvest is thrilled to announce our Series A investment in Opaque Systems, the company behind open source MC2, a platform for collaborative analytics and artificial intelligence ( AI) at scale. Confidential computing is a market that is ready for takeoff — Gartner estimates that by 2025, 60% of large organizations will adopt privacy-enhancing computation (PEC) for processing data in untrusted environments and (multiparty) data analytics use cases. Opaque Systems was founded by an incredible team — Rishabh Poddar, Raluca Ada Popa and Ion Stoica — who have the right skills to solve this incredibly technical challenge.

Our journey with Rishabh, Opaque’s co-founder and CEO started many years ago during his PhD days at UC Berkeley. My interest in one of his published papers led to a memorable early-morning coffee meeting, when he was a member of Berkeley RISELab whose mission is to develop technologies that enable applications to interact intelligently and securely with their environment in real time. Rishabh’s excitement about the confidential computing space is infectious and we knew right away that we had to be a part of his journey whenever he decided to launch his dreamboat. We are thankful that time has arrived!

AI and machine learning (ML) techniques are increasingly being adopted by enterprises today to make business-critical decisions. For continued accuracy, machine learning needs a large amount of data which today is stuck in organizational silos. Sharing confidential data is a bottleneck for large enterprises in regulated industries who want to (Read more...)

Democratizing Machine Learning to Predict the Future with Richard Harris of Black Crow AI

This post is by MPD from @MPD - Medium

On today’s pod I chat with Richard Harris, the Founder & CEO of Black Crow Ai.

Black Crow is a no-code, real-time machine-learning based predictive software that helps companies understand likely customer behavior.

Richard’s a veteran entrepreneur and has been involved in tech since the 90s. He cut his teeth in the world of consulting, was involved with Travelocity during the dot com boom, then has continued to be a serial founder.

As you’ll hear him explain, the world is turning into a browser. Between mobile devices, computers, wearable tech, self driving cars — real time data will be streaming from every part of our lives. He’s using this insight to build a company to help startups and brands collect and understand their first party data so that they can increase revenue and margins.

In addition to discussing how Blackcrow operates and the machine learning industry, we also talk about some strategies for how founders can navigate down market cycles — like the one we’re entering now. He’s been through three of these cycles and has some very helpful wisdom. If you started your company after 2010, then I definitely recommend that you give this one a listen.

Listen via your preferred platform here.

Show Links:

Please note, Interplay is an investor in Black Crow.

Democratizing Machine Learning to Predict the Future with Richard Harris of Black Crow AI was originally published in @MPD on Medium, where people are continuing the conversation by highlighting and responding to this story.

Here’s where MLOps is accelerating enterprise AI adoption

A version of this blog appeared in TechCrunch: Here’s where MLOps is accelerating enterprise AI adoption

Why is MLOps important?

In early 2000s most business-critical software was hosted in privately-run data centers. Initially skeptical, enterprises moved their critical applications to the cloud. DevOps fueled this cloud adoption as it gave decision makers a sense of control over business-critical applications hosted outside their own data centers. Today, enterprises are in a similar phase of trial and acceptance of ML in their production environments and MLOps is acting as an accelerator. Similar to cloud-native startups, many startups today are ML-native and offer differentiated products to their customers. But a vast majority of large and mid-size enterprises are either in trial phase or just struggling to productionize functioning models. Here are some key challenges that MLOps can help with:

Cross-team ML collaboration is a must but tough to accomplish

A model may be as simple as a churn-prediction model, or as complex as the one determining Uber or Lyft pricing between San Jose and San Francisco. It is an incredibly complex task to create a model and enable teams to benefit from it. In addition to a large amount of labeled historic data to train the model, multiple teams need to coordinate to continuously monitor the models for performance degradation. There are three different core roles that are involved in ML modeling but each one has different motivations and incentives:

1. Data Engineers: these are trained engineers who excel at grabbing data from multiple (Read more...)

Business Canvas, a Korea-based document management SaaS company, closes $2.5M seed round

Business Canvas, the South Korean document management SaaS company behind Typed, announced today it has raised a $2.5 million seed round led by Mirae Asset Venture Investment, with participation from Kakao Ventures and Nextrans Inc.

The seed round will be used for accelerating product development and the global launch of an open beta for its AI-powered document management platform. The company opened an office in Santa Clara, California this year to spur its global expansion.

The problem that Business Canvas has identified and is building solutions to target is the challenge faced by people who are tasked with ingesting information and producing writing or decisions based on that: lawyers, entrepreneurs, researchers, students and communications workers like journalists among them. People are bombarded with information these days, thanks to technology. That might be good in some cases, but in the world of work, and specifically written work, there is such a thing as too much information, which can take a lot of time to process, and thus eat into the time we need to produce work based on that information.

Business Canvas, founded in 2020 by CEO Woojin Kim, Brian Shin, Seungmin Lee, Dongjoon Shin and Clint Yoo, is hoping to solve the challenge that every knowledge worker and writer faces: spending more time on research and file organization than the actual content output they need to create.

“In fact, people commit over 30% of their working hours trying to search for that file we once saved in (Read more...)

Fertility tracking app Flo closes $50M Series B

Flo, a fertility focused period-tracking app, has closed a $50 million Series B funding round which it says values the company at $800M — underlining how much value investors are now attaching to women’s health tech.

The 2016-founded startup raised a $1M seed in its first year and has gone on to raise a total of $65M. The latest B funding round is co-led by VNV Global and Target Global.

Flo’s user-base has grown to around 200M globally — a proportion of whom pay it a subscription to access exclusive content, in addition to the core period tracking features.

The app uses machine learning to offer users “curated” cycle tracking and predictions, personalized health insights, and real-time health alerts — based on tracked symptoms, with data fed in by its sizeable user base. So while it started out as a period tracker Flo now touts its app as a proactive, preventative healthcare tool for women —  connecting them to “science-backed content, expert-led courses and accurate cycle predictions”.

But that’s also a measure of increased competition for women-centric utilities like period tracking — with Apple, for example, (finally) adding cycle tracking in the Health app that’s native to iOS back in 2019. So femtech startups like Flo have to do a lot more than provide basic utility to win women these days.

Flo appears to be getting something right with its current content and marketing mix: Over the past 12 months it says its active subscriber base has increased 4x (Read more...)

Mobius Labs nabs $6M to help more sectors tap into computer vision

Berlin-based Mobius Labs has closed a €5.2 million (~$6.1M) funding round off the back of increased demand for its computer vision training platform. The Series A investment is led by Ventech VC, along with Atlantic Labs, APEX Ventures, Space Capital, Lunar Ventures plus some additional angel investors.

The startup offers an SDK that lets the user create custom computer vision models fed with a little of their own training data — as an alternative to off-the-shelf tools which may not have the required specificity for a particular use-case.

It also flags a ‘no code’ focus, saying its tech has been designed with a non-technical user in mind.

As it’s an SDK, Mobius Labs’ platform can also be deployed on premise and/or on device — rather than the customer needing to connect to a cloud service to tap into the AI tool’s utility.

“Our custom training user interface is very simple to work with, and requires no prior technical knowledge on any level,” claims Appu Shaji, CEO and chief scientist. 

“Over the years, a trend we have observed is that often the people who get the maximum value from AI are non technical personas like a content manager in a press and creative agency, or an application manager in the space sector. Our no-code AI allows anyone to build their own applications, thus enabling these users to get close to their vision without having to wait for AI experts or developer teams to help them.”

Mobius Labs — which was (Read more...)

Insurify, a ‘virtual insurance agent,’ raises $100M Series B

How many of us have not switched insurance carriers because we don’t want to deal with the hassle of comparison shopping?

A lot, I’d bet.

Today, Insurify, a startup that wants to help make it easier for people to get better rates on home, auto and life insurance, announced that it has closed $100 million in an “oversubscribed” Series B funding round led by Motive Partners.

Existing backers Viola FinTech, MassMutual Ventures, Nationwide, Hearst Ventures and Moneta VC also put money in the round, as well as new investors Viola Growth and Fort Ross Ventures. With the new financing, Cambridge, Massachusetts-based Insurify has now raised a total of $128 million since its 2013 inception. The company declined to disclose the valuation at which the money was raised.

Since we last covered Insurify, the startup has seen some impressive growth. For example, it has seen its new and recurring revenue increase by “6x” since it closed its Series A funding in the 2019 fourth quarter. Over the last three years, Insurify has achieved a CAGR (compound annual growth rate) of 151%, according to co-founder and CEO Snejina Zacharia. It has also seen consistent “2.5x” year-over-year revenue growth, she said.

Insurify has built a machine learning-based virtual insurance agent that integrates with more than 100 carriers to digitize — and personalize — the insurance shopping experience. There are others in the insurtech space, but none that we (Read more...)

Sanas aims to convert one accent to another in real time for smoother customer service calls

In the customer service industry, your accent dictates many aspects of your job. It shouldn’t be the case that there’s a “better” or “worse” accent, but in today’s global economy (though who knows about tomorrow’s) it’s valuable to sound American or British. While many undergo accent neutralization training, Sanas is a startup with another approach (and a $5.5M seed round): using speech recognition and synthesis to change the speaker’s accent in near real time.

The company has trained a machine learning algorithm to quickly and locally (that is, without using the cloud) recognize a person’s speech on one end and, on the other, output the same words with an accent chosen from a list or automatically detected from the other person’s speech.

Screenshot of the Sanas desktop application.

Image Credits:

It slots right into the OS’s sound stack so it works out of the box with pretty much any audio or video calling tool. Right now the company is operating a pilot program with thousands of people in locations from the USA and UK to the Philippines, India, Latin America, and others. Accents supported will include American, Spanish, British, Indian, Filipino and Australian by the end of the year.

To tell the truth, the idea of Sanas kind of bothered me at first. It felt like a concession to bigoted people who consider their accent superior and think others below them. Tech will fix it… by accommodating the bigots. Great!

But while I still have a little bit of that feeling, I can see there’s (Read more...)

Taktile makes it easier to leverage machine learning in the financial industry

Meet Taktile, a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. But Taktile wants to differentiate itself from competitors by making it way easier to get started and switch to AI-powered models.

A few years ago, when you could read ‘machine learning’ and ‘artificial intelligence’ in every single pitch deck, some startups chose to focus on the financial industry in particular. It makes sense as banks and insurance companies gather a ton of data and know a lot of information about their customers. They could use that data to train new models and roll out machine learning applications.

New fintech companies put together their own in-house data science team and started working on machine learning for their own products. Companies like Younited Credit and October use predictive risk tools to make better lending decisions. They have developed their own models and they can see that their models work well when they run them on past data.

But what about legacy players in the financial industry? A few startups have worked on products that can be integrated in existing banking infrastructure. You can use artificial intelligence to identify fraudulent transactions, predict creditworthiness, detect fraud in insurance claims, etc.

Some of them have been thriving, such as Shift Technology with a focus on insurance in particular. But a lot of startups build proof-of-concepts and stop there. There’s no meaningful, long-term business (Read more...) closes huge $52.5M Series B after posting 4x ARR growth in the last year

Covering public companies can be a bit of a drag. They grow some modest amount each year, and their constituent analysts pester them with questions about gross margin expansion and sales rep efficiency. It can be a little dull. Then there are startups, which grow much more quickly — and are more fun to talk about.

That’s the case with The company announced an impressive set of metrics this morning, including that from July 2020 to July 2021, it grew its annual recurring revenue (ARR) 4x. Shelf also disclosed that it secured a $52.5 million Series B led by Tiger Global and Insight Partners.

That’s quick growth for a post-Series A startup. Crunchbase reckons that the company raised $8.2 million before its Series B, while PitchBook pegs the number at $6.5 million. Regardless, the company was efficiently expanding from a limited capital base before its latest fundraising event.

What does the company’s software do? Shelf plugs into a company’s information systems, learns from the data, and then helps employees respond to queries without forcing them to execute searches or otherwise hunt for information.

The company is starting with customer service as its target vertical. According to Shelf CEO Sedarius Perrotta, Shelf can absorb information from, say, Salesforce, SharePoint, legacy knowledge management platforms, and Zendesk. Then, after training models and staff, the company’s software can begin to provide support staff with answers to customer questions as they talk to customers in real time.

The company’s tech can also (Read more...)