Ment.io wants to help your team make decisions

Getting even the most well-organized team to agree on anything can be hard. Tel Aviv’s Ment.io, formerly known as Epistema, wants to make this process easier by applying smart design and a dose of machine learning to streamline the decision-making process.

Like with so many Israeli startups, Ment.io’s co-founders Joab Rosenberg and Tzvika Katzenelson got their start in Israel’s intelligence service. Indeed, Rosenberg spent 25 years in the intelligence service, where his final role was that of the deputy head analyst. “Our story starts from there, because we had the responsibility of gathering the knowledge of a thousand analysts, surrounded by tens of thousands of collection unit soldiers,” Katzenelson, who is Ment.io’s CRO, told me. He noted that the army had turned decision making into a form of art. But when the founders started looking at the tech industry, they found a very different approach to decision making — and one that they thought needed to change.

If there’s one thing the software industry has, it’s data and analytics. These days, the obvious thing to do with all of that information is to build machine learning models, but Katzenelson (rightly) argues that these models are essentially black boxes. “Data does not speak for itself. Correlations that you may find in the data are certainly not causations,” he said. “Every time you send analysts into the data, they will come up with some patterns that may mislead you.”

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So Ment.io is trying to take a very different approach. It uses data and machine learning, but it starts with questions and people. The service actually measures the level of expertise and credibility every team member has around a given topic. “One of the crazy things we’re doing is that for every person, we’re creating their cognitive matrix. We’re able to tell you within the context of your organization how believable you are, how balanced you are, how clearly you are being perceived by your counterparts, because we are gathering all of your clarification requests and every time a person challenges you with something.”Ment1

At its core, Ment.io is basically an internal Q&A service. Anybody can pose questions and anybody can answer them with any data source or supporting argument they may have.

“We’re doing structuring,” Katzenelson explained. “And that’s basically our philosophy: knowledge is just arguments and counterarguments. And the more structure you can put in place, the more logic you can apply.”

In a sense, the company is doing this because natural language processing (NLP) technology isn’t yet able to understand the nuances of a discussion.Ment6If you’re anything like me, though, the last thing you want is to have to use yet another SaaS product at work. The Ment.io team is quite aware of that and has built a deep integration with Slack already and is about to launch support for Microsoft Teams in the next few days, which doesn’t come as a surprise, given that the team has participated in the Microsoft ScaleUp accelerator program.

The overall idea here, Katzenelson explained, is to provide a kind of intelligence layer on top of tools like Slack and Teams that can capture a lot of the institutional knowledge that is now often shared in relatively ephemeral chats.

Ment.io is the first Israeli company to raise funding from Peter Thiel’s late-stage fund, as well as from the Slack Fund, which surely creates some interesting friction, given the company’s involvement with both Slack and Microsoft, but Katzenelson argues that this is not actually a problem.

Microsoft is also a current Ment.io customer, together with the likes of Intel, Citibank and Fiverr.

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By Frederic Lardinois

Cybereason raises $200 million for its enterprise security platform

Cybereason, which uses machine learning to increase the number of endpoints a single analyst can manage across a network of distributed resources, has raised $200 million in new financing from SoftBank Group and its affiliates. 

It’s a sign of the belief that SoftBank has in the technology, since the Japanese investment firm is basically doubling down on commitments it made to the Boston-based company four years ago.

The company first came to our attention five years ago when it raised a $25 million financing from investors, including CRV, Spark Capital and Lockheed Martin.

Cybereason’s technology processes and analyzes data in real time across an organization’s daily operations and relationships. It looks for anomalies in behavior across nodes on networks and uses those anomalies to flag suspicious activity.

The company also provides reporting tools to inform customers of the root cause, the timeline, the person involved in the breach or breaches, which tools they use and what information was being disseminated within and outside of the organization.

For co-founder Lior Div, Cybereason’s work is the continuation of the six years of training and service he spent working with the Israeli army’s 8200 Unit, the military incubator for half of the security startups pitching their wares today. After his time in the military, Div worked for the Israeli government as a private contractor reverse-engineering hacking operations.

Over the last two years, Cybereason has expanded the scope of its service to a network that spans 6 million endpoints tracked by 500 employees, with offices in Boston, Tel Aviv, Tokyo and London.

“Cybereason’s big data analytics approach to mitigating cyber risk has fueled explosive expansion at the leading edge of the EDR domain, disrupting the EPP market. We are leading the wave, becoming the world’s most reliable and effective endpoint prevention and detection solution because of our technology, our people and our partners,” said Div, in a statement. “We help all security teams prevent more attacks, sooner, in ways that enable understanding and taking decisive action faster.”

The company said it will use the new funding to accelerate its sales and marketing efforts across all geographies and push further ahead with research and development to make more of its security operations autonomous.

“Today, there is a shortage of more than three million level 1-3 analysts,” said Yonatan Striem-Amit, chief technology officer and co-founder, Cybereason, in a statement. “The new autonomous SOC enables SOC teams of the future to harness technology where manual work is being relied on today and it will elevate  L1 analysts to spend time on higher value tasks and accelerate the advanced analysis L3 analysts do.”

Most recently the company was behind the discovery of Operation SoftCell, the largest nation-state cyber espionage attack on telecommunications companies. 

That attack, which was either conducted by Chinese-backed actors or made to look like it was conducted by Chinese-backed actors, according to Cybereason, targeted a select group of users in an effort to acquire cell phone records.

As we wrote at the time:

… hackers have systematically broken in to more than 10 cell networks around the world to date over the past seven years to obtain massive amounts of call records — including times and dates of calls, and their cell-based locations — on at least 20 individuals.

Researchers at Boston-based Cybereason, who discovered the operation and shared their findings with TechCrunch, said the hackers could track the physical location of any customer of the hacked telcos — including spies and politicians — using the call records.

Lior Div, Cybereason’s co-founder and chief executive, told TechCrunch it’s “massive-scale” espionage.

Call detail records — or CDRs — are the crown jewels of any intelligence agency’s collection efforts. These call records are highly detailed metadata logs generated by a phone provider to connect calls and messages from one person to another. Although they don’t include the recordings of calls or the contents of messages, they can offer detailed insight into a person’s life. The National Security Agency  has for years controversially collected the call records of Americans from cell providers like AT&T and Verizon (which owns TechCrunch), despite the questionable legality.

It’s not the first time that Cybereason has uncovered major security threats.

Back when it had just raised capital from CRV and Spark, Cybereason’s chief executive was touting its work with a defense contractor who’d been hacked. Again, the suspected culprit was the Chinese government.

As we reported, during one of the early product demos for a private defense contractor, Cybereason identified a full-blown attack by the Chinese — 10,000 thousand usernames and passwords were leaked, and the attackers had access to nearly half of the organization on a daily basis.

The security breach was too sensitive to be shared with the press, but Div says that the FBI was involved and that the company had no indication that they were being hacked until Cybereason detected it.


By Jonathan Shieber

72 hours left on early-bird pricing to TC Sessions: Enterprise 2019

Synchronize your Fitbits, people. You have 72 hours left to get your fiscal fitness on. Three days to save $100 on tickets to TC Sessions: Enterprise 2019 in San Francisco on September 5. Buy your early-bird ticket by August 9 at 11:59 p.m. (PT) and then go back to counting your steps.

We say with confidence that no tech category’s more competitive than enterprise software. The gigantic, $500 billion market generates a constant flow of multibillion-dollar acquisitions every year. And it takes a special kind of fierce early-stage enterprise startup to jump in, invent new services and shake up old-school incumbents.

More than 1,000 attendees will be in the house to explore this rich, complex topic, TechCrunch-style. Our editors will interview top titans in the enterprise world — like SAP CEO, Bill McDermott; Atlassian co-founder, Scott Farquhar; and Jocelyn Goldfein, managing director at Zetta Venture Partners. They’ll also tap rising founders of upstart startups.

The enterprise just can’t get enough of AI, but large companies face a huge challenge: packaging all that data in machine learning models — a necessary element for using AI to automate processes. That’s why we’re especially excited that Bindu Reddy, co-founder and CEO at RealityEngines, will join us onstage.

Her company aims to create research-driven cloud services to reduce some of the inherent complexity of working with AI tools. Reddy, along with investor Jocelyn Goldfein, a managing director at Zetta Venture Partners, and others will talk about the growing role of AI in the enterprise.

That’s just the tip of the Enterprise iceberg. More than 20 interviews, panel discussions, Q&As and breakout sessions will cover a wide range of technologies, including intelligent marketing automation, the cloud, Kubernetes and even quantum and blockchain. Peruse the agenda to see what else we have in store for you.

Early-bird pricing for TC Sessions: Enterprise 2019 ends in just 72 hours. Buy your ticket by August 9 at 11:59 p.m. (PT) and you’ll save $100. But wait, there’s more — for every ticket you buy, we’ll register you for a free Expo-only pass to TechCrunch Disrupt SF 2019. Now that’s fiscal fitness.

Is your company interested in sponsoring or exhibiting at TC Sessions: Enterprise? Contact our sponsorship sales team by filling out this form.


By Emma Comeau

Dasha AI is calling so you don’t have to

While you’d be hard pressed to find any startup not brimming with confidence over the disruptive idea they’re chasing, it’s not often you come across a young company as calmly convinced it’s engineering the future as Dasha AI.

The team is building a platform for designing human-like voice interactions to automate business processes. Put simply, it’s using AI to make machine voices a whole lot less robotic.

“What we definitely know is this will definitely happen,” says CEO and co-founder Vladislav Chernyshov. “Sooner or later the conversational AI/voice AI will replace people everywhere where the technology will allow. And it’s better for us to be the first mover than the last in this field.”

“In 2018 in the US alone there were 30 million people doing some kind of repetitive tasks over the phone. We can automate these jobs now or we are going to be able to automate it in two years,” he goes on. “If you multiple it with Europe and the massive call centers in India, Pakistan and the Philippines you will probably have something like close to 120M people worldwide… and they are all subject for disruption, potentially.”

The New York based startup has been operating in relative stealth up to now. But it’s breaking cover to talk to TechCrunch — announcing a $2M seed round, led by RTP Ventures and RTP Global: An early stage investor that’s backed the likes of Datadog and RingCentral. RTP’s venture arm, also based in NY, writes on its website that it prefers engineer-founded companies — that “solve big problems with technology”. “We like technology, not gimmicks,” the fund warns with added emphasis.

Dasha’s core tech right now includes what Chernyshov describes as “a human-level, voice-first conversation modelling engine”; a hybrid text-to-speech engine which he says enables it to model speech disfluencies (aka, the ums and ahs, pitch changes etc that characterize human chatter); plus “a fast and accurate” real-time voice activity detection algorithm which detects speech in under 100 milliseconds, meaning the AI can turn-take and handle interruptions in the conversation flow. The platform can also detect a caller’s gender — a feature that can be useful for healthcare use-cases, for example.

Another component Chernyshov flags is “an end-to-end pipeline for semi-supervised learning” — so it can retrain the models in real time “and fix mistakes as they go” — until Dasha hits the claimed “human-level” conversational capability for each business process niche. (To be clear, the AI cannot adapt its speech to an interlocutor in real-time — as human speakers naturally shift their accents closer to bridge any dialect gap — but Chernyshov suggests it’s on the roadmap.)

“For instance, we can start with 70% correct conversations and then gradually improve the model up to say 95% of correct conversations,” he says of the learning element, though he admits there are a lot of variables that can impact error rates — not least the call environment itself. Even cutting edge AI is going to struggle with a bad line.

The platform also has an open API so customers can plug the conversation AI into their existing systems — be it telephony, Salesforce software or a developer environment, such as Microsoft Visual Studio.

Currently they’re focused on English, though Chernyshov says the architecture is “basically language agnostic” — but does requires “a big amount of data”.

The next step will be to open up the dev platform to enterprise customers, beyond the initial 20 beta testers, which include companies in the banking, healthcare and insurance sectors — with a release slated for later this year or Q1 2020.

Test use-cases so far include banks using the conversation engine for brand loyalty management to run customer satisfaction surveys that can turnaround negative feedback by fast-tracking a response to a bad rating — by providing (human) customer support agents with an automated categorization of the complaint so they can follow up more quickly. “This usually leads to a wow effect,” says Chernyshov.

Ultimately, he believes there will be two or three major AI platforms globally providing businesses with an automated, customizable conversational layer — sweeping away the patchwork of chatbots currently filling in the gap. And of course Dasha intends their ‘Digital Assistant Super Human Alike’ to be one of those few.

“There is clearly no platform [yet],” he says. “Five years from now this will sound very weird that all companies now are trying to build something. Because in five years it will be obvious — why do you need all this stuff? Just take Dasha and build what you want.”

“This reminds me of the situation in the 1980s when it was obvious that the personal computers are here to stay because they give you an unfair competitive advantage,” he continues. “All large enterprise customers all over the world… were building their own operating systems, they were writing software from scratch, constantly reinventing the wheel just in order to be able to create this spreadsheet for their accountants.

“And then Microsoft with MS-DOS came in… and everything else is history.”

That’s not all they’re building, either. Dasha’s seed financing will be put towards launching a consumer-facing product atop its b2b platform to automate the screening of recorded message robocalls. So, basically, they’re building a robot assistant that can talk to — and put off — other machines on humans’ behalf.

Which does kind of suggest the AI-fuelled future will entail an awful lot of robots talking to each other… 🤖🤖🤖

Chernyshov says this b2c call screening app will most likely be free. But then if your core tech looks set to massively accelerate a non-human caller phenomenon that many consumers already see as a terrible plague on their time and mind then providing free relief — in the form of a counter AI — seems the very least you should do.

Not that Dasha can be accused of causing the robocaller plague, of course. Recorded messages hooked up to call systems have been spamming people with unsolicited calls for far longer than the startup has existed.

Dasha’s PR notes Americans were hit with 26.3BN robocalls in 2018 alone — up “a whopping” 46% on 2017.

Its conversation engine, meanwhile, has only made some 3M calls to date, clocking its first call with a human in January 2017. But the goal from here on in is to scale fast. “We plan to aggressively grow the company and the technology so we can continue to provide the best voice conversational AI to a market which we estimate to exceed $30BN worldwide,” runs a line from its PR.

After the developer platform launch, Chernyshov says the next step will be to open up access to business process owners by letting them automate existing call workflows without needing to be able to code (they’ll just need an analytic grasp of the process, he says).

Later — pegged for 2022 on the current roadmap — will be the launch of “the platform with zero learning curve”, as he puts it. “You will teach Dasha new models just like typing in a natural language and teaching it like you can teach any new team member on your team,” he explains. “Adding a new case will actually look like a word editor — when you’re just describing how you want this AI to work.”

His prediction is that a majority — circa 60% — of all major cases that business face — “like dispatching, like probably upsales, cross sales, some kind of support etc, all those cases” — will be able to be automated “just like typing in a natural language”.

So if Dasha’s AI-fuelled vision of voice-based business process automation come to fruition then humans getting orders of magnitude more calls from machines looks inevitable — as machine learning supercharges artificial speech by making it sound slicker, act smarter and seem, well, almost human.

But perhaps a savvier generation of voice AIs will also help manage the ‘robocaller’ plague by offering advanced call screening? And as non-human voice tech marches on from dumb recorded messages to chatbot-style AIs running on scripted rails to — as Dasha pitches it — fully responsive, emoting, even emotion-sensitive conversation engines that can slip right under the human radar maybe the robocaller problem will eat itself? I mean, if you didn’t even realize you were talking to a robot how are you going to get annoyed about it?

Dasha claims 96.3% of the people who talk to its AI “think it’s human”, though it’s not clear what sample size the claim is based on. (To my ear there are definite ‘tells’ in the current demos on its website. But in a cold-call scenario it’s not hard to imagine the AI passing, if someone’s not paying much attention.)

The alternative scenario, in a future infested with unsolicited machine calls, is that all smartphone OSes add kill switches, such as the one in iOS 13 — which lets people silence calls from unknown numbers.

And/or more humans simply never pick up phone calls unless they know who’s on the end of the line.

So it’s really doubly savvy of Dasha to create an AI capable of managing robot calls — meaning it’s building its own fallback — a piece of software willing to chat to its AI in future, even if actual humans refuse.

Dasha’s robocall screener app, which is slated for release in early 2020, will also be spammer-agnostic — in that it’ll be able to handle and divert human salespeople too, as well as robots. After all, a spammer is a spammer.

“Probably it is the time for somebody to step in and ‘don’t be evil’,” says Chernyshov, echoing Google’s old motto, albeit perhaps not entirely reassuringly given the phrase’s lapsed history — as we talk about the team’s approach to ecosystem development and how machine-to-machine chat might overtake human voice calls.

“At some point in the future we will be talking to various robots much more than we probably talk to each other — because you will have some kind of human-like robots at your house,” he predicts. “Your doctor, gardener, warehouse worker, they all will be robots at some point.”

The logic at work here is that if resistance to an AI-powered Cambrian Explosion of machine speech is futile, it’s better to be at the cutting edge, building the most human-like robots — and making the robots at least sound like they care.

Dasha’s conversational quirks certainly can’t be called a gimmick. Even if the team’s close attention to mimicking the vocal flourishes of human speech — the disfluencies, the ums and ahs, the pitch and tonal changes for emphasis and emotion — might seem so at first airing.

In one of the demos on its website you can hear a clip of a very chipper-sounding male voice, who identifies himself as “John from Acme Dental”, taking an appointment call from a female (human), and smoothly dealing with multiple interruptions and time/date changes as she changes her mind. Before, finally, dealing with a flat cancelation.

A human receptionist might well have got mad that the caller essentially just wasted their time. Not John, though. Oh no. He ends the call as cheerily as he began, signing off with an emphatic: “Thank you! And have a really nice day. Bye!”

If the ultimate goal is Turing Test levels of realism in artificial speech — i.e. a conversation engine so human-like it can pass as human to a human ear — you do have to be able to reproduce, with precision timing, the verbal baggage that’s wrapped around everything humans say to each other.

This tonal layer does essential emotional labor in the business of communication, shading and highlighting words in a way that can adapt or even entirely transform their meaning. It’s an integral part of how we communicate. And thus a common stumbling block for robots.

So if the mission is to power a revolution in artificial speech that humans won’t hate and reject then engineering full spectrum nuance is just as important a piece of work as having an amazing speech recognition engine. A chatbot that can’t do all that is really the gimmick.

Chernyshov claims Dasha’s conversation engine is “at least several times better and more complex than [Google] Dialogflow, [Amazon] Lex, [Microsoft] Luis or [IBM] Watson”, dropping a laundry list of rival speech engines into the conversation.

He argues none are on a par with what Dasha is being designed to do.

The difference is the “voice-first modelling engine”. “All those [rival engines] were built from scratch with a focus on chatbots — on text,” he says, couching modelling voice conversation “on a human level” as much more complex than the more limited chatbot-approach — and hence what makes Dasha special and superior.

“Imagination is the limit. What we are trying to build is an ultimate voice conversation AI platform so you can model any kind of voice interaction between two or more human beings.”

Google did demo its own stuttering voice AI — Duplex — last year, when it also took flak for a public demo in which it appeared not to have told restaurant staff up front they were going to be talking to a robot.

Chernyshov isn’t worried about Duplex, though, saying it’s a product, not a platform.

“Google recently tried to headhunt one of our developers,” he adds, pausing for effect. “But they failed.”

He says Dasha’s engineering staff make up more than half (28) its total headcount (48), and include two doctorates of science; three PhDs; five PhD students; and ten masters of science in computer science.

It has an R&D office in Russian which Chernyshov says helps makes the funding go further.

“More than 16 people, including myself, are ACM ICPC finalists or semi finalists,” he adds — likening the competition to “an Olympic game but for programmers”. A recent hire — chief research scientist, Dr Alexander Dyakonov — is both a doctor of science professor and former Kaggle No.1 GrandMaster in machine learning. So with in-house AI talent like that you can see why Google, uh, came calling…

Dasha

 

But why not have Dasha ID itself as a robot by default? On that Chernyshov says the platform is flexible — which means disclosure can be added. But in markets where it isn’t a legal requirement the door is being left open for ‘John’ to slip cheerily by. Bladerunner here we come.

The team’s driving conviction is that emphasis on modelling human-like speech will, down the line, allow their AI to deliver universally fluid and natural machine-human speech interactions which in turn open up all sorts of expansive and powerful possibilities for embeddable next-gen voice interfaces. Ones that are much more interesting than the current crop of gadget talkies.

This is where you could raid sci-fi/pop culture for inspiration. Such as Kitt, the dryly witty talking car from the 1980s TV series Knight Rider. Or, to throw in a British TV reference, Holly the self-depreciating yet sardonic human-faced computer in Red Dwarf. (Or indeed Kryten the guilt-ridden android butler.) Chernyshov’s suggestion is to imagine Dasha embedded in a Boston Dynamics robot. But surely no one wants to hear those crawling nightmares scream…

Dasha’s five-year+ roadmap includes the eyebrow-raising ambition to evolve the technology to achieve “a general conversational AI”. “This is a science fiction at this point. It’s a general conversational AI, and only at this point you will be able to pass the whole Turing Test,” he says of that aim.

“Because we have a human level speech recognition, we have human level speech synthesis, we have generative non-rule based behavior, and this is all the parts of this general conversational AI. And I think that we can we can — and scientific society — we can achieve this together in like 2024 or something like that.

“Then the next step, in 2025, this is like autonomous AI — embeddable in any device or a robot. And hopefully by 2025 these devices will be available on the market.”

Of course the team is still dreaming distance away from that AI wonderland/dystopia (depending on your perspective) — even if it’s date-stamped on the roadmap.

But if a conversational engine ends up in command of the full range of human speech — quirks, quibbles and all — then designing a voice AI may come to be thought of as akin to designing a TV character or cartoon personality. So very far from what we currently associate with the word ‘robotic’. (And wouldn’t it be funny if the term ‘robotic’ came to mean ‘hyper entertaining’ or even ‘especially empathetic’ thanks to advances in AI.)

Let’s not get carried away though.

In the meanwhile, there are ‘uncanny valley’ pitfalls of speech disconnect to navigate if the tone being (artificially) struck hits a false note. (And, on that front, if you didn’t know ‘John from Acme Dental’ was a robot you’d be forgiven for misreading his chipper sign off to a total time waster as pure sarcasm. But an AI can’t appreciate irony. Not yet anyway.)

Nor can robots appreciate the difference between ethical and unethical verbal communication they’re being instructed to carry out. Sales calls can easily cross the line into spam. And what about even more dystopic uses for a conversation engine that’s so slick it can convince the vast majority of people it’s human — like fraud, identity theft, even election interference… the potential misuses could be terrible and scale endlessly.

Although if you straight out ask Dasha whether it’s a robot Chernyshov says it has been programmed to confess to being artificial. So it won’t tell you a barefaced lie.

Dasha

How will the team prevent problematic uses of such a powerful technology?

“We have an ethics framework and when we will be releasing the platform we will implement a real-time monitoring system that will monitor potential abuse or scams, and also it will ensure people are not being called too often,” he says. “This is very important. That we understand that this kind of technology can be potentially probably dangerous.”

“At the first stage we are not going to release it to all the public. We are going to release it in a closed alpha or beta. And we will be curating the companies that are going in to explore all the possible problems and prevent them from being massive problems,” he adds. “Our machine learning team are developing those algorithms for detecting abuse, spam and other use cases that we would like to prevent.”

There’s also the issue of verbal ‘deepfakes’ to consider. Especially as Chernyshov suggests the platform will, in time, support cloning a voiceprint for use in the conversation — opening the door to making fake calls in someone else’s voice. Which sounds like a dream come true for scammers of all stripes. Or a way to really supercharge your top performing salesperson.

Safe to say, the counter technologies — and thoughtful regulation — are going to be very important.

There’s little doubt that AI will be regulated. In Europe policymakers have tasked themselves with coming up with a framework for ethical AI. And in the coming years policymakers in many countries will be trying to figure out how to put guardrails on a technology class that, in the consumer sphere, has already demonstrated its wrecking-ball potential — with the automated acceleration of spam, misinformation and political disinformation on social media platforms.

“We have to understand that at some point this kind of technologies will be definitely regulated by the state all over the world. And we as a platform we must comply with all of these requirements,” agrees Chernyshov, suggesting machine learning will also be able to identify whether a speaker is human or not — and that an official caller status could be baked into a telephony protocol so people aren’t left in the dark on the ‘bot or not’ question. 

“It should be human-friendly. Don’t be evil, right?”

Asked whether he considers what will happen to the people working in call centers whose jobs will be disrupted by AI, Chernyshov is quick with the stock answer — that new technologies create jobs too, saying that’s been true right throughout human history. Though he concedes there may be a lag — while the old world catches up to the new.

Time and tide wait for no human, even when the change sounds increasingly like we do.


By Natasha Lomas

Buy a demo table at TC Sessions: Enterprise 2019

Early-stage enterprise startup founders listen up. That sound you hear is opportunity knocking. Answer the call, open the door and join us for TC Sessions: Enterprise on September 5 in San Francisco. Our day-long conference not only explores the promises and challenges of this $500 billion market, it also provides an opportunity for unparalleled exposure.

How’s that? Buy a Startup Demo Package and showcase your genius to more than 1,000 of the most influential enterprise founders, investors, movers and shakers. This event features the enterprise software world’s heaviest hitters. People like SAP CEO Bill McDermott; Aaron Levie, Box co-founder, chairman and CEO; and George Brady, executive VP in charge of technology operations at Capital One.

Demo tables are reserved for startups with less than $3 million, cost $2,000 and include four tickets to the event. We have a limited number of demo tables available, so don’t wait to introduce your startup to this very targeted audience.

The entire day is a full-on deep dive into the big challenges, hot topics and potential promise facing enterprise companies today. Forget the hype. TechCrunch editors will interview founders and leaders — established and emerging — on topics ranging from intelligent marketing automation and the cloud to machine learning and AI. You’ll hear from VCs about where they’re directing their enterprise investments.

Speaking of investors and hot topics, Jocelyn Goldfein, a managing director at Zetta Venture Partners, will join TechCrunch editors and other panelists for a discussion about the growing role of AI in enterprise software.

Check out our growing (and amazing, if we do say so ourselves) roster of speakers.

Our early-bird pricing is still in play, which means tickets cost $249 and students pay only $75. Plus, for every TC Sessions: Enterprise ticket you buy, we’ll register you for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.

TC Sessions: Enterprise takes place September 5 at San Francisco’s Yerba Buena Center for the Arts. Buy a Startup Demo Package, open the door to opportunity and place your early-stage enterprise startup directly in the path of influential enterprise software founders, investors and technologists.

Looking for sponsorship opportunities? Contact our TechCrunch team to learn about the benefits associated with sponsoring TC Sessions: Enterprise 2019.


By Emma Comeau

Google updates its speech tech for contact centers

Last July, Google announced its Contact Center AI product for helping businesses get more value out of their contact centers. Contact Center AI uses a mix of Google’s machine learning-powered tools to help build virtual agents and help human agents as they do their job. Today, the company is launching several updates to this product that will, among other things, bring improved speech recognition features to the product.

As Google notes, its automated speech recognition service gets to very high accuracy rates, even on the kind of noisy phone lines that many customers use to complain about their latest unplanned online purchase. To improve these numbers, Google is now launching a feature called ‘Auto Speech Adaptation in Dialogflow,” (with Dialogflow being Google tool for building conversational experiences). With this, the speech recognition tools are able to take the context of the conversation into account and hence improve their accuracy by about 40 percent, according to Google.

Speech Recognition Accuracy

In addition, Google is also launching a new model phone model for understanding short utterances, which is now about 15 percent more accurate for U.S. English, as well as a number of other updates that improve transcription accuracy, make the training process easier and allow for endless audio streaming to the Cloud Speech-to-Text API, which previously had a 5-minute limit.

If you want to, you can also now natively download MP3s of the audio (and then burn them to CDs, I guess).

dialogflow virtual agent.max 1100x1100


By Frederic Lardinois

Investor Jocelyn Goldfein to join us on AI panel at TechCrunch Sessions: Enterprise

Artificial intelligence is quickly becoming a foundational technology for enterprise software development and startups have begun addressing a variety of issues around using AI to make software and processes much more efficient.

To that end, we are delighted to announce that Jocelyn Goldfein, a Managing Director at Zetta Venture Partners will be joining on us a panel to discuss AI in the enterprise. It will take place at the TechCrunch Sessions: Enterprise show on September 5 at the Yerba Buena Center in San Francisco.

It’s not just startups that are involved in AI in the enterprise. Some of the biggest names in enterprise software including Salesforce Einstein, Adobe Sensei and IBM Watson have been addressing the need for AI to help solve the enterprise data glut.

Computers can process large amounts of information much more quickly than humans, and as enterprise companies generate increasing amounts of data, they need help understanding it all as the volume of information exceeds human capacity to sort through it.

Goldfein brings a deep engineering background to her investment work. She served as a VP of engineering at VMware and as an engineering director at Facebook, where she led the project that adopted machine learning for the News Feed ranker, launched major updates in photos and search, and helped spearhead Facebook’s pivot to mobile. Goldfein drove significant reforms in Facebook hiring practices and is a prominent evangelist for women in computer science. As an investor, she primarily is focused on startups using AI to take more efficient approaches to infrastructure, security, supply chains and worker productivity.

At TC Sessions: Enterprise, she’ll be joining Bindu Reddy from Reality Engines along with other panelists to discuss the growing role of AI in enterprise software with TechCrunch editors. You’ll learn why AI startups are attracting investor attention and how AI in general could fundamentally transform enterprise software.

Prior to joining Zetta, Goldfein had stints at Facebook and VMware, as well as startups Datify, MessageOne and Trilogy/pcOrder.

Early Bird tickets to see Joyce at TC Sessions: Enterprise are on sale for just $249 when you book here; but hurry, prices go up by $100 soon! Students, grab your discounted tickets for just $75 here.


By Ron Miller

Andrew Ng to talk about how AI will transform business at TC Sessions: Enterprise

When it comes to applying AI to the world around us, Andrew Ng has few if any peers. We are delighted to announce that the renowned founder, investor, AI expert and Stanford professor will join us on stage at the TechCrunch Sessions: Enterprise show on Sept. 5 at the Yerba Buena Center in San Francisco. 

AI promises to transform the $500 billion enterprise world like nothing since the cloud and SaaS.  Hundreds of startups are already seizing the AI moment in areas like recruiting, marketing and communications, and customer experience. The oceans of data required to power AI are becoming dramatically more valuable, which in turn is fueling the rise of new data platforms, another big topic of the show

Last year, Ng  launched the $175 million AI Fund, backed by big names like Sequoia, NEA, Greylock, and Softbank. The fund’s goal is to develop new AI businesses in a studio model and spin them out when they are ready for prime time. The first of that fund’s cohort is Landing AI, which also launched last year and aims to “empower companies to jumpstart AI and realize practical value.” It’s a wave businesses will want to catch if Ng is anywhere near right in his conviction that AI will generate $13 trillion in GDP growth globally in the next 20 years. You heard that right. 

At TC Sessions: Enterprise, TechCrunch’s editors will ask Ng to detail how he believes AI will unfold in the enterprise world and bring big productivity gains to business. 

As the former Chief Scientist at Baidu and the founding lead of Google Brain, Ng led the AI transformation of two of the world’s leading technology companies. Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of deeplearning.ai, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department and holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

Early Bird tickets to see Andrew at TC Sessions: Enterprise are on sale for just $249 when you book here, but hurry prices go up by $100 soon! Students, grab your discounted tickets for just $75 here.



By Frederic Lardinois

The startups creating the future of RegTech and financial services

Technology has been used to manage regulatory risk since the advent of the ledger book (or the Bloomberg terminal, depending on your reference point). However, the cost-consciousness internalized by banks during the 2008 financial crisis combined with more robust methods of analyzing large datasets has spurred innovation and increased efficiency by automating tasks that previously required manual reviews and other labor-intensive efforts.

So even if RegTech wasn’t born during the financial crisis, it was probably old enough to drive a car by 2008. The intervening 11 years have seen RegTech’s scope and influence grow.

RegTech startups targeting financial services, or FinServ for short, require very different growth strategies — even compared to other enterprise software companies. From a practical perspective, everything from the security requirements influencing software architecture and development to the sales process are substantially different for FinServ RegTechs.

The most successful RegTechs are those that draw on expertise from security-minded engineers, FinServ-savvy sales staff as well as legal and compliance professionals from the industry. FinServ RegTechs have emerged in a number of areas due to the increasing directives emanating from financial regulators.

This new crop of startups performs sophisticated background checks and transaction monitoring for anti-money laundering purposes pursuant to the Bank Secrecy Act, the Office of Foreign Asset Control (OFAC) and FINRA rules; tracks supervision requirements and retention for electronic communications under FINRA, SEC, and CFTC regulations; as well as monitors information security and privacy laws from the EU, SEC, and several US state regulators such as the New York Department of Financial Services (“NYDFS”).

In this article, we’ll examine RegTech startups in these three fields to determine how solutions have been structured to meet regulatory demand as well as some of the operational and regulatory challenges they face.

Know Your Customer and Anti-Money Laundering


By Danny Crichton

Demo your startup at TC Sessions: Enterprise 2019

Every year hundreds of startups launch with dreams of becoming the next enterprise software unicorn. And it’s no wonder, given the $500 billion market and the rate at which the enterprise giants snap up emerging players. If you’re the founder of an early-stage enterprise startup, join us for TC Sessions: Enterprise in San Francisco on September 5 at the Yerba Buena Center for the Arts.

Even better, grab the opportunity by the horns and buy a Startup Demo Package. There is limited space available. This is your chance to plant your company in front of some of the most influential enterprise movers and shakers — we’re talking more than 1,000 attendees. Demo tables are reserved for startups with less than $3 million in funding and are available for $2,000, which includes four tickets to the event.

This day-long intensive event features speakers, panel discussions, demos, workshops and world-class networking. Get ready for a head-on, hype-free exploration of the considerable challenges enterprise companies face — regardless of their size.

TechCrunch editors will interview founders and leaders from both established and up-and-coming companies on topics ranging from intelligent marketing automation and the cloud to machine learning and AI. And they’ll question enterprise-focused VCs about where they’re directing their early, middle and late-stage investments.

The full roster of speakers is still to be announced, but here’s a quick hit of who you can expect at TC Sessions: Enterprise.

You’ll hear from Scott Farquhar, co-founder and co-CEO of Atlassian, a company that’s changed the way developers work. Want to hear more about enterprise and the cloud? Snowflake’s co-founder and president of product, Benoit Dageville, will be on hand to talk about the company’s mission to bring the enterprise database to the cloud.

Have someone you want to hear from our stage? Submit your speaker suggestion here.

Pro Tip: For each TC Sessions: Enterprise ticket you buy, we’ll register you for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.

TC Sessions: Enterprise takes place September 5 at San Francisco’s Yerba Buena Center for the Arts. Don’t miss this opportunity to showcase your early-stage enterprise startup in front of leading enterprise software founders, investors and technologists. Buy your Startup Demo Package today.

Looking for sponsorship opportunities? Contact our TechCrunch team to learn about the benefits associated with sponsoring TC Sessions: Enterprise 2019.


By Emma Comeau

Software development analytics platform Sourced launches an enterprise edition

Sourced, or source{d}, as the company styles its name, provides developers and IT departments with deeper analytics into their software development lifecycle. It analyzes codebases, offers data about which APIs are being used and provides general information about developer productivity and other metrics. Today, Sourced is officially launching its Enterprise Edition, which gives IT departments and executives a number of advanced tools for managing their software portfolios and the processes they use to create them.

“Sourced enables large engineering organizations to better monitor, measure and manage their IT initiatives by providing a platform that empowers IT leaders with actionable data,” said the company’s CEO Eiso Kant. “The release of Sourced Enterprise is a major milestone towards proper engineering observability of the entire software development life cycle in enterprises.”

Engineering Effectiveness Efficiency

Since it’s one of the hallmarks of every good enterprise tools, it’s no surprise that Sourced Enterprise also offers features like role-based access control and other security features, as well as dedicated support and SLAs. IT departments can also run the service on-premise, or use it as a SaaS product.

The company also tells me that the enterprise version can handle larger codebases so that even complex queries over a large dataset only takes a few seconds (or minutes if it’s a really large codebase). To create these complex queries, the enterprise edition includes a number of add-ons to allow users to create these advanced queries. “These are available upon request and tailored to help enterprises overcome specific challenges that often rely on machine learning capabilities, such as identity matching or code duplication analysis,” the company says.

Cloud Migration

The service integrates with most commonly used project management and business intelligence tools, but it also ships with Apache Superset, an open-source business intelligence application that offers built-in data visualization capabilities.

These visualization capabilities are also now part of the Sourced Community Edition, which is now available in private beta.

“Sourced Enterprise gave us valuable insights into the Cloud Foundry codebase evolution, development patterns, trends, and dependencies, all presented in easy-to-digest dashboards,” said Chip Childers, the CTO of the open-source Cloud Foundry Foundation, which tested the Enterprise Edition ahead of its launch. “If you really want to understand what’s going on in your codebase and engineering department, Sourced is the way to go.”

To date, the company has raised $10 million from Frst VC, Heartcore Capital, Xavier Niel and others.

Talent Assessment Managment


By Frederic Lardinois

Video platform Kaltura adds advanced analytics

You may not be familiar with Kaltura‘s name, but chances are you’ve used the company’s video platform at some point or another, given that it offers a variety of video services for enterprises, educational institutions and video on demand platforms, including HBO,  Phillips, SAP, Stanford and others. Today, the company announced the launch of an advanced analytics platform for its enterprise and educational users.

This new platform, dubbed Kaltura Analytics for Admins, will provide its users with features like user-level reports. This may sound like a minor feature, since you probably don’t care about the exact details of a given user’s interactions with your video, but it will allow businesses to link this kind of behavior to other metrics. With this, you could measure the ROI of a given video by linking video watch time and sales, for example. This kind of granularity wasn’t possible with the company’s existing analytics systems. Companies and schools using the product will also get access to time period comparisons to help admins identify trends, deeper technology and geolocation reports, as well as real-time analytics for live events.

eCDN QoS dashboard

“Video is a unique data type in that it has deep engagement indicators for measurement, both around video creation – what types of content are being created by whom, as well as around video consumption and engagement with content – what languages were selected for subtitles, what hot-spots were clicked upon in video,” said Michal Tsur, President & General Manager of Enterprise and Learning at Kaltura. “Analytics is a very strategic area for our customers. Both for tech companies who are building on our VPaaS, as well as for large organizations and universities that use our video products for learning, communication, collaboration, knowledge management, marketing and sales.”

Tsur also tells me that the company is looking at how to best use machine learning to give its customers even deeper insights into how people watch videos — and potentially even offer predictive analytics in the long run.


By Frederic Lardinois

Snowflake co-founder and president of product Benoit Dageville is coming to TC Sessions: Enterprise

When it comes to a cloud success story, Snowflake checks all the boxes. It’s a SaaS product going after industry giants. It has raised bushels of cash and grown extremely rapidly — and the story is continuing to develop for the cloud data lake company.

In September, Snowflake’s co-founder and president of product Benoit Dageville will join us at our inaugural TechCrunch Sessions: Enterprise event on September 5 in San Francisco.

Dageville founded the company in 2012 with Marcin Zukowski and Thierry Cruanes with a mission to bring the database, a market that had been dominated for decades by Oracle, to the cloud. Later, the company began focusing on data lakes or data warehouses, massive collections of data, which had been previously stored on premises. The idea of moving these elements to the cloud was a pretty radical notion in 2012.

It began by supporting its products on AWS, and more recently expanded to include support for Microsoft Azure and Google Cloud.

The company started raising money shortly after its founding, modestly at first, then much, much faster in huge chunks. Investors included a Silicon Valley who’s who such as Sutter Hill, Redpoint, Altimeter, Iconiq Capital and Sequoia Capital .

Snowflake fund raising by round. Chart: Crunchbase

Snowflake fund raising by round. Chart: Crunchbase

The most recent rounds came last year, starting with a massive $263 million investment in January. The company went back for more in October with an even larger $450 million round.

It brought on industry veteran Bob Muglia in 2014 to lead it through its initial growth spurt. Muglia left the company earlier this year and was replaced by former ServiceNow chairman and CEO Frank Slootman.

TC Sessions: Enterprise (September 5 at San Francisco’s Yerba Buena Center) will take on the big challenges and promise facing enterprise companies today. TechCrunch’s editors will bring to the stage founders and leaders from established and emerging companies to address rising questions, like the promised revolution from machine learning and AI, intelligent marketing automation and the inevitability of the cloud, as well as the outer reaches of technology, like quantum computing and blockchain.

Tickets are now available for purchase on our website at the early-bird rate of $395.

Student tickets are just $245 – grab them here.

We have a limited number of Startup Demo Packages available for $2,000, which includes four tickets to attend the event.

For each ticket purchased for TC Sessions: Enterprise, you will also be registered for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.


By Ron Miller

Get your early-bird tickets to TC Sessions: Enterprise 2019

In a world where the enterprise market hovers around $500 billion in annual sales, is it any wonder that hundreds of enterprise startups launch into that fiercely competitive arena every year? It’s a thrilling, roller-coaster ride that’s seen it all: serious success, wild wealth and rapid failure.

That’s why we’re excited to host our inaugural TC Sessions Enterprise 2019 event on September 5 at the Yerba Buena Center for the Arts in San Francisco. Like TechCrunch’s other TC Sessions, this day-long intensive goes deep on one specific topic. Early-bird tickets are on sale now for $395 — and we have special pricing for MBA students and groups, too. Buy your tickets now and save.

Bonus ROI: For every ticket you buy to TC Sessions: Enterprise, we’ll register you for a free Expo Only pass to TechCrunch Disrupt SF on October 2-4. Sweet!

Expect a full day of programming featuring the people making it happen in enterprise today. We’re talking founders and leaders from established and emerging companies, plus proven enterprise-focused VCs. Discussions led by TechCrunch’s editors, including Connie Loizos, Frederic Lardinois and Ron Miller, will explore machine learning and AI, intelligent marketing automation and the inevitability of the cloud. We’ll even touch on topics like quantum computing and blockchain.

Tired of the hype and curious about what it really takes to build a successful enterprise company? We’ve got you. You’ll hear from proven serial entrepreneurs who’ve been there, done that and what they might like to build next.

We’re building the agenda of speakers, panelists and demos, and we have a limited number of speaking opportunities available. If you have someone in mind, submit your recommendation here.

This event is perfect for enterprise-minded founders, investors, MBA students, engineers, CTOs and CIOs. If you need four or more tickets, take advantage of our group rate and save 15% over the early-bird price when you buy in bulk. Are you an MBA student? Save your dough — buy a student ticket for $245.

TC Sessions: Enterprise 2019 takes place September 5 in San Francisco. Join us for actionable insights and world-class networking. Buy your early-bird tickets today.

Is your company interested in sponsoring or exhibiting at TC Sessions: Enterprise 2019? Contact our sponsorship sales team by filling out this form.


By Emma Comeau

RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises

RealityEngines.AI, a research startup that wants to help enterprises make better use of AI, even when they only have incomplete data, today announced that it has raised a $5.25 million seed funding round. The round was led by former Google CEO and Chairman Eric Schmidt and Google founding board member Ram Shriram. Khosla Ventures, Paul Buchheit, Deepchand Nishar, Elad Gil, Keval Desai, Don Burnette and others also participated in this round.

The fact that the service was able to raise from this rather prominent group of investors clearly shows that its overall thesis resonates. The company, which doesn’t have a product yet, tells me that it specifically wants to help enterprises make better use of the smaller and noisier datasets they have and provide them with state-of-the-art machine learning and AI systems that they can quickly take into production. It also aims to provide its customers with systems that can explain their predictions and are free of various forms of bias, something that’s hard to do when the system is essentially a black box.

As RealityEngines CEO Bindu Reddy, who was previously the head of products for Google Apps, told me the company plans to use the funding to build out its research and development team. The company, after all, is tackling some of the most fundamental and hardest problems in machine learning right now — and that costs money. Some, like working with smaller datasets, already have some available solutions like generative adversarial networks that can augment existing datasets and that RealityEngines expects to innovate on.

Reddy is also betting on reinforcement learning as one of the core machine learning techniques for the platform.

Once it has its product in place, the plan is to make it available as a pay-as-you-go managed service that will make machine learning more accessible to large enterprise, but also to small and medium businesses, which also increasingly need access to these tools to remain competitive.


By Frederic Lardinois