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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…
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.
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.
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Grasshopper’s Judith Erwin leaps into innovation banking
In the years following the financial crisis, de novo bank activity in the US slowed to a trickle. But as memories fade, the economy expands and the potential of tech-powered financial services marches forward, entrepreneurs have once again been asking the question, “Should I start a bank?”
And by bank, I’m not referring to a neobank, which sits on top of a bank, or a fintech startup that offers an interesting banking-like service of one kind or another. I mean a bank bank.
One of those entrepreneurs is Judith Erwin, a well-known business banking executive who was part of the founding team at Square 1 Bank, which was bought in 2015. Fast forward a few years and Erwin is back, this time as CEO of the cleverly named Grasshopper Bank in New York.
With over $130 million in capital raised from investors including Patriot Financial and T. Rowe Price Associates, Grasshopper has a notable amount of heft for a banking newbie. But as Erwin and her team seek to build share in the innovation banking market, she knows that she’ll need the capital as she navigates a hotly contested niche that has benefited from a robust start-up and venture capital environment.
Gregg Schoenberg: Good to see you, Judith. To jump right in, in my opinion, you were a key part of one of the most successful de novo banks in quite some time. You were responsible for VC relationships there, right?
…My background is one where people give me broken things, I fix them and give them back.
Judith Erwin: The VC relationships and the products and services managing the balance sheet around deposits. Those were my two primary roles, but my background is one where people give me broken things, I fix them and give them back.
Schoenberg: Square 1 was purchased for about 22 times earnings and 260% of tangible book, correct?
Erwin: Sounds accurate.
Schoenberg: Plus, the bank had a phenomenal earnings trajectory. Meanwhile, PacWest, which acquired you, was a “perfectly nice bank.” Would that be a fair characterization?
Erwin: Yes.
Schoenberg: Is part of the motivation to start Grasshopper to continue on a journey that maybe ended a little bit prematurely last time?
Erwin: That’s a great insight, and I did feel like we had sold too soon. It was a great deal for the investors — which included me — and so I understood it. But absolutely, a lot of what we’re working to do here are things I had hoped to do at Square 1.
Image via Getty Images / Classen Rafael / EyeEm
Schoenberg: You’re obviously aware of the 800-pound gorilla in the room in the form of Silicon Valley Bank . You’ve also got the megabanks that play in the segment, as well as Signature Bank, First Republic, Bridge Bank and others.
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Capital One CTO George Brady will join us at TC Sessions: Enterprise
When you think of old, giant mainframes that sit in the basement of a giant corporation, still doing the same work they did 30 years ago, chances are you’re thinking about a financial institution. It’s the financial enterprises, though, that are often leading the charge in bringing new technologies and software development practices to their employees and customers. That’s in part because they are in a period of disruption that forces them to become more nimble. Often, this means leaving behind legacy technology and embracing the cloud.
At TC Sessions: Enterprise, which is happening on September 5 in San Francisco, Capital One executive VP in charge of its technology operations, George Brady, will talk about the company’s journey from legacy hardware and software to embracing the cloud and open source, all while working in a highly regulated industry. Indeed, Capital One was among the first companies to embrace the Facebook-led Open Compute project and it’s a member of the Cloud Native Computing Foundation. It’s this transformation at Capital One that Brady is leading.
At our event, Brady will join a number of other distinguished panelists to specifically talk about his company’s journey to the cloud. There, Capital One is using serverless compute, for example, to power its Credit Offers API using AWS’s Lambda service, as well as a number of other cloud technologies.
Before joining Capital One as its CTO in 2014, Brady ran Fidelity Investment’s global enterprise infrastructure team from 2009 to 2014 and served as Goldman Sachs’ head of global business applications infrastructure before that.
Currently, he leads cloud application and platform productization for Capital One. Part of that portfolio is Critical Stack, a secure container orchestration platform for the enterprise. Capital One’s goal with this work is to help companies across industries become more compliant, secure and cost-effective operating in the public cloud.
Early-bird tickets are still on sale for $249; grab yours today before we sell out.
Student tickets are just $75 — grab them here.
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Careteam aims to unite patients and healthcare providers with a platform approach
How best to untangle the Gordian knot that is navigating your own healthcare? It’s a tricky question, and one that seems to have become only more complicated as technology improves, in many regards — systems don’t necessarily speak to one another, and it’s still hard for an ordinary patient without specialist knowledge to make sense of everything. Careteam is a Canadian startup hoping to address that, looking to replicate the kind of advances made possible by technology in industries like e-commerce and enterprise software.
Careteam co-founder and CEO Dr. Alexandra Greenhill has experienced the frustration of being a tech-savvy person in a world of healthcare that can seem technologically inept — both as a practicing GP and as someone who depends on the healthcare system as a patient and a relative of patients with more sophisticated medical needs.
“I spent more than 15 years innovating within the healthcare system,” Greenhill told me in an interview. “I computerized hospitals, helped doctors adopt electronic medical records and other types of innovation practices. And then for the last eight years, I’ve been in tech, trying to figure out how to build the kind of technology we need in health, and especially digital health.”
All that experience led Greenhill to the realization that while there were many companies building specific solutions for real, but relatively narrow problems, that didn’t reflect how most people experienced care. Greenhill and her team of three other co-founders (Jeremy P. Smith, Robert I. Atwell and Kevin Lysyk) had all had unfortunate, but eye-opening experiences with family members in need of treatment for major diseases.
“You step in and you discover that cancer care, palliative care, post-surgical care — there’s so many things that would have gone wrong if we didn’t have the expertise ourselves,” Greenhill said. “But in the meantime, you end up being sort of pulled into multiple directions and saying ‘this makes no sense.’ You know, I can purchase stuff online in my private life; I can use all kinds of tools in the business world, and yet it’s back to paper and voice in health, which matters most.”
Careteam CEO and founder Dr. Alexandra Greenhill
What Careteam provides is collaboration for care — true collaboration, designed to span patients, their doctors and other healthcare pros, their families and anyone who matters to them in the course of pursuing their care. It provides the ability to communicate instantly, build care plans that integrate all aspects of their tailored health plans, receive custom-configurable notifications and measure progress toward specific goals set by patient and healthcare providers.
Part of the reason this process has become opaque or difficult is precisely due to innovation: Greenhill takes issue with the prevailing narrative that the healthcare industry is somehow allergic to innovation.
“There’s this sort of perception that healthcare doesn’t innovate, but it’s also almost insulting to the healthcare system, because we have innovated — we save people from cancer, where we couldn’t,” she noted. “We cure HIV, in some cases, and we prevent it from being transmitted to unborn babies of mothers with full-blown AIDS and things that in my working lifetime were impossibilities; it was science fiction to help someone with HIV. And, and we’ve managed to do all of that, and it’s a success story. We’ve created complexity, we’ve created people who live with 12 conditions for many, many years and take complicated drug regiments.”
In addition to advances in treatment, Greenhill notes that she and her team couldn’t have build Careteam five years ago, because cloud storage wasn’t secure and everything had to be done on a site-specific instance, and that would’ve been cost-prohibitive to build. In other words, technology has been applied to, and vastly improved, healthcare overall, regardless of the general perception of the industry as an innovation laggard.
That’s why Greenhill’s startup doesn’t shy away from complexity — they embrace it. Careteam is designed not to try to normalize and standardize the varied and highly specialized landscape of healthcare solutions and providers through anything like a one-size-fits-all API. Instead, the company’s tech development is cleverly designed to be flexible when it comes to integrations.
“We collectively spent $1.9 billion in Canada, to try and digitize the healthcare system, create standards and create some exchange between data,” Greenhill said. “The NHS tried the same, big U.S. hospital systems have created their own little sort of islands, including Kaiser and Mayo and others. And the conclusion of all of that is standardization in healthcare just doesn’t seem to catch on.”
Careteam’s approach has been instead to integrate specific clinics, and let practitioners and patients derive benefits and help spur the adoption of the platform to their companion organizations and clinics. It’s a sort of rhizomatic approach that starts with a node central to a patient’s care and spreads through the healthcare professionals and members of the patient’s support network that the product helps. And integration is made possible without technical demands on the part of partners thanks to the work of CTO Lysyk, according to Greenhill.
The Vancouver-based startup is working with the Centre for Aging + Brain Health in Toronto, Ontario in a validation program announced last year, and also raised an initial round of funding in January led by BCF Ventures with participation from Right Side Capital, Globalive Capital, Atrium Ventures, and angels Barney Pell and Ajay Agarwal .
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Couchbase’s mobile database gets built-in ML and enhanced synchronization features
Couchbase, the company behind the eponymous NoSQL database, announced a major update to its mobile database today that brings some machine learning smarts, as well as improved synchronization features and enhanced stats and logging support, to the software.
“We’ve led the innovation and data management at the edge since the release of our mobile database five years ago,” Couchbase’s VP of Engineering Wayne Carter told me. “And we’re excited that others are doing that now. We feel that it’s very, very important for businesses to be able to utilize these emerging technologies that do sit on the edge to drive their businesses forward, and both making their employees more effective and their customer experience better.”
The latter part is what drove a lot of today’s updates, Carter noted. He also believes that the database is the right place to do some machine learning. So with this release, the company is adding predictive queries to its mobile database. This new API allows mobile apps to take pre-trained machine learning models and run predictive queries against the data that is stored locally. This would allow a retailer to create a tool that can use a phone’s camera to figure out what part a customer is looking for.
To support these predictive queries, Couchbase mobile is also getting support for predictive indexes. “Predictive indexes allow you to create an index on prediction, enabling correlation of real-time predictions with application data in milliseconds,” Carter said. In many ways, that’s also the unique value proposition for bringing machine learning into the database. “What you really need to do is you need to utilize the unique values of a database to be able to deliver the answer to those real-time questions within milliseconds,” explained Carter.
The other major new feature in this release is delta synchronization, which allows businesses to push far smaller updates to the databases on their employees’ mobile devices. That’s because they only have to receive the information that changed instead of a full updated database. Carter says this was a highly requested feature, but until now, the company always had to prioritize work on other components of Couchbase.
This is an especially useful feature for the company’s retail customers, a vertical where it has been quite successful. These users need to keep their catalogs up to data and quite a few of them supply their employees with mobile devices to help shoppers. Rumor has it that Apple, too, is a Couchbase user.
The update also includes a few new features that will be more of interest to operators, including advanced stats reporting and enhanced logging support.
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Fleksy’s AI keyboard is getting a store to put mini apps at chatters’ fingertips
Remember Fleksy? The customizable Android keyboard app has a new trick up its sleeve: It’s adding a store where users can find and add lightweight third party apps to enhance their typing experience.
Right now it’s launched a taster, preloading a selection of ‘mini apps’ into the keyboard — some from very familiar brand names, some a little less so — so users can start to see how it works.
The first in-keyboard apps are Yelp (local services search); Skyscanner (flight search); Giphy (animated Gif search); GifNote (music Gifs; launching for U.S. users only for rights reasons); Vlipsy (reaction video clips); and Emogi (stickers) — with “many more” branded apps slated as coming in the next few months.
They’re not saying exactly what other brands are coming but there are plenty of familiar logos to be spotted in their press materials — from Spotify to Uber to JustEat to Tripadvisor to PayPal and more…
The full keyboard store itself — which will let users find and add and/or delete apps — will be launching at the end of this month.
The latest version of the Fleksy app can be downloaded for free via the Play Store.
Mini apps made for messaging
The core idea for these mini apps (aka Fleksyapps) is to offer lightweight additions designed to serve the messaging use case.
Say, for example, you’re chatting about where to eat and a friend suggests sushi. The Yelp Fleksyapp might pop up a contextual suggestion for a nearby Japanese restaurant that can be shared directly into the conversation — thereby saving time by doing away with the need for someone to cut out of the chat, switch apps, find some relevant info and cut and paste it back into the chat.
Fleksyapps are intended to be helpful shortcuts that keep the conversation flowing. They also of course put brands back into the conversation.
“We couldn’t be more excited to bring the power of the world’s popular songs with GIFs, videos and photos to the new Fleksyapps platform,” says Gifnote co-founder, John vanSuchtelen, in a supporting statement.
Fleksy’s mini apps appear above the Qwerty keyboard — in much the same space as a next-word prediction. The user can scroll through the app stack (each a tiny branded circle until tapped on to expand) and choose one to interact with. It’s similar to the micro apps lodged in Apple’s iMessage but on Android where iMessage isn’t… The team also plans for Fleksy to support a much wider range of branded apps — hence the Fleksyapps store.
In-keyboard apps is not a new concept for the dev team behind Fleksy; an earlier keyboard app of theirs (called ThingThing) offered micro apps they built themselves as a tool to extend its utility.
But now they’re hoping to garner backing and buy in from third party brands excited about the exposure and reach they could gain by being where users spend the most device time: The keyboard.
“Think of it a bit like the iMessage equivalent but on Android across any app. Or the WeChat mini program but inside the keyboard, available everywhere — not only in one app,” CEO Olivier Plante tells TechCrunch. “That’s a problem of messaging apps these days. All of them are verticals but the keyboard is horizontal. So that’s the benefit for those brands. And the user will have the ability to move them around, add some, to remove some, to explore, to discover.”
“The brands that want to join our platform they have the option of being preloaded by default. The analogy is that by default on the home screen of a phone you are by default in our keyboard. And moving forward you’ll be able to have a membership — you’re becoming a ‘brand member’ of the Fleksyapps platform, and you can have your brand inside the keyboard,” he adds.
The first clutch of Fleksyapps were developed jointly, with the team working with the brands in question. But Plante says they’re planning to launch a tool in future so brands will be able to put together their own apps — in as little as just a few hours.
“We’re opening this array of functionalities and there’s a lot of verticals possible,” he continues. “In the future months we will embed new capabilities for the platform — new type of apps. You can think about professional apps, or cloud apps. Accessing your files from different types of clouds. You have the weather vertical. You have ecommerce vertical. You have so many verticals.
“What you have on the app store today will be reflected into the Fleksyappstore. But really with the focus of messaging and being useful in messaging. So it’s not the full app that we want to bring in — it’s really the core functionality of this app.”
The Yelp Fleksyapp, for example, only includes the ability to see nearby places and search for and share places. So it’s intentionally stripped down. “The core benefit for the brand is it gives them the ability to extend their reach,” says Plante. “We don’t want to compete with the app, per se, we just want to bring these types of app providers inside the messenger on Android across any app.”
On the user side, the main advantage he touts is “it’s really, really fast — fleshing that out to: “It’s very lightweight, it’s very, very fast and we want to become the fastest access to content across any app.”
Users of Fleksyapps don’t need to have the full app installed because the keyboard plugs directly into the API of each branded service. So they get core functionality in bite-sized form without a requirement to download the full app. (Of course they can if they wish.)
So Plante also notes the approach has benefits vis-a-vis data consumption — which could be an advantage in emerging markets where smartphone users’ choices may be hard-ruled by the costs of data and/or connectivity limits.
“For those types of users it gives them an ability to access content but in a very light way — where the app itself, loading the app, loading all the content inside the app can be megabits. In Fleksy you’re talking about kilobits,” he says.
Privacy-sensitive next app suggestions
While baking a bunch of third party apps into a keyboard might sound like a privacy nightmare, the dev team behind Fleksy have been careful to make sure users remain in control.
To wit: Also on board is an AI keyboard assistant (called Fleksynext) — aka “a neural deep learning engine” — which Plante says can detect the context, intention and sentiment of conversations in order to offer “very useful” app suggestions as the chat flows.
The idea is the AI supports the substance of the chat by offering useful functionality from whatever pick and mix of apps are available. Plante refers to these AI-powered ‘next app’ suggestions as “pops”.
And — crucially, from a privacy point of view — the Fleksynext suggestion engine operates locally, on device.
That means no conversation data is sent out of the keyboard. Indeed, Plante says nothing the user types in the keyboard itself is shared with brands (including suggestions that pop up but get ignored). So there’s no risk — as with some other keyboard apps — of users being continually strip-mined for personal data to profile them as they type.
That said, if the user chooses to interact with a Fleksyapp (or its suggestive pop) they are then interacting with a third party’s API. So the usual tracking caveats apply.
“We interact with the web so there’s tracking everywhere,” admits Plante. “But, per se, there’s not specific sensitive data that is shared suddenly with someone. It is not related with the service itself — with the Fleksy app.”
The key point is that the keyboard user gets to choose which apps they want to use and which they don’t. So they can choose which third parties they want to share their plans and intentions with and which they don’t.
“We’re not interesting in making this an advertising platform where the advertiser decides everything,” emphasizes Plante. “We want this to be really close to the user. So the user decides. My intentions. My sentiment. What I type decides. And that is really our goal. The user is able to power it. He can tap on the suggestion or ignore it. And then if he taps on it it’s a very good quality conversion because the user really wants to access restaurants nearby or explore flights for escaping his daily routine… or transfer money. That could be another use-case for instance.”
They won’t be selling brands a guaranteed number of conversions, either.
That’s clearly very important because — to win over users — Fleksynext suggestions will need to feel telepathically useful, rather than irritating, misfired nag. Though the risk of that seems low given how Fleksy users can customize the keyboard apps to only see stuff that’s useful to them.
“In a sense we’re starting reshape a bit how advertising is seen by putting the user in the center,” suggests Plante. “And giving them a useful means of accessing content. This is the original vision and we’ve been very loyal to that — and we think it can reshape the landscape.”
“When you look into five years from now, the smartphone we have will be really, really powerful — so why process things in the cloud? When you can process things on the phone. That’s what we are betting on: Processing everything on the phone,” he adds.
When the full store launches users will be able to add and delete (any) apps — included preloads. So they will be in the driving seat. (We asked Plante to a confirm the user will be able to delete all apps, including any pre-loadeds and he said yes. So if you take him at his word Fleksy will not be cutting any deals with OEMs or carriers to indelibly preload certain Fleksyapps. Or, to put it another way, crapware baked into the keyboard is most definitely not plan.)
Depending on what other Fleksyapps launch in future a Fleksy keyboard user could choose to add, for example, a search service like DuckDuckGo or France’s Qwant to power a pro-privacy alternative to using Google search in the keyboard. Or they could choose Google.
Again the point is the choice is theirs.
Scaling a keyboard into a platform
The idea of keyboard-as-platform offers at least the possibility of reintroducing the choice and variety of smartphone app stores back before the cynical tricks of attention-harvesting tech giants used their network effects and platform power to throttle the app economy.
The Android keyboard space was also a fertile experiment ground in years past. But it’s now dominated by Google’s Gboard and Microsoft-acquired Swiftkey. Which makes Fleksy the plucky upstart gunning to scale an independent alternative that’s not owned by big tech and is open to any third party that wants to join its mini apps party.
“It will be Bing search for Swiftkey, it will be Google search for Gboard, it will be Google Music, it will be YouTube. But on our side we can have YouTube, we can also have… other services that exist for video. The same way with pictures and the same way for file-sharing and drive. So you have Google Drive but you have Dropbox, you have OneDrive, there’s a lot of services in the cloud. And we want to be the platform that has them all, basically,” says Plante.
The original founding team of the Fleksy keyboard was acqui-hired by Pinterest back in 2016, leaving the keyboard app itself to languish with minimal updates. Then two years ago Barcelona-based keyboard app maker, ThingThing, stepped in to take over development.
Plante confirms it’s since fully acquired the Fleksy keyboard technology itself — providing a solid foundation for the keyboard-as-platform business it’s now hoping to scale with the launch of Fleksyapps.
Talking of scale, he tells us the startup is in the process of raising a multi-million Series A — aiming to close this summer. (ThingThing last took in $800,000 via equity crowdfunding last fall.)
The team’s investor pitch is the keyboard offers perhaps the only viable conduit left on mobile to reset the playing field for brands by offering a route to cut through tech giant walled gardens and get where users are spending most of their time and attention: i.e. typing and sharing stuff with their friends in private one-to-one and group chats.
That means the keyboard-as-platform has the potential to get brands of all stripes back in front of users — by embedding innovative, entertaining and helpful bite-sized utility where it can prove its worth and amass social currency on the dominant messaging platforms people use.
The next step for the rebooted Fleksy team is of course building scale by acquiring users for a keyboard which, as of half a year ago, only had around 1M active users from pure downloads.
Its strategy on this front is to target Android device makers to preload Fleksy as the default keyboard.
ThingThing’s business model is a revenue share on any suggestions the keyboard converts, which it argues represent valuable leads for brands — given the level of contextual intention. It is also intending to charge brands that want to be preloaded on the Fleksy keyboard by default.
Again, though, a revenue share model requires substantial scale to work. Not least because brands will need to see evidence of scale to buy into the Fleksyapps’ vision.
Plante isn’t disclosing active users of the Fleksy keyboard right now. But says he’s confident they’re on track to hit 30M-35M active users this year — on account of around ten deals he says are in the pipeline with device makers to preload Fleksy’s keyboard. (Palm was an early example, as we reported last year.)
The carrot for OEMs to join the Fleksyapps party is they’re cutting them in on the revenue share from user interactions with branded keyboard apps — playing to device makers’ needs to find ways to boost famously tight hardware margins.
“The fact that the keyboard can monetize and provide value to the phone brands — this is really massive for them,” argues Plante. “The phone brands can expect revenue flowing in their bank account because we give the brands distribution and the handset manufacturer will make money and we will make money.”
It’s a smart approach, and one that’s essentially only possible because Google’s own Gboard keyboard doesn’t come preloaded on the majority of Android devices. (Exceptions include its own Pixel brand devices.) So — unusually for a core phone app on Android — there’s a bit of an open door where the keyboard sits, instead of the usual preloaded Google wares. And that’s an opportunity.
Markets wise, ThingThing is targeting OEMs in all global regions with its Fleksy pitch — barring China (which Plante readily admits it too complex for a small startup to sensibly try jumping at).
Apps vs tech giants
In its stamping ground of Europe there are warm regulatory winds blowing too: An European Commission antitrust intervention last year saw Google hit with a $5BN fine over anti-competitive practices attached to its Android platform — forcing the company to change local licensing terms.
That antirust decision means mobile makers finally have the chance to unbundle Google apps from devices they sell in the region.
Which translates into growing opportunities for OEMs to rethink their Android strategies. Even as Google remains under pressure not to get in the way by force feeding any more of its wares.
Really, a key component of this shift is that device makers are being told to think, to look around and see what else is out there. For the first time there looks to be a viable chance to profit off of Android without having to preload everything Google wants.
“For us it’s a super good sign,” says Plante of the Commission decision. “Every monopolistic situation is a problem. And the market needs to be fragmented. Because if not we’re just going to lose innovation. And right now Europe — and I see good progress for the US as well — are trying to dismantle the imposed power of those big guys. For the simple evolution of human being and technology and the future of us.”
“I think good things can happen,” he adds. “We’re in talks with handset manufacturers who are coming into Europe and they want to be the most respectful of the market. And with us they have this reassurance that you have a good partner that ensures there’s a revenue stream, there’s a business model behind it, there’s really a strong use-case for users.
“We can finally be where we always wanted to be: A choice, an alternative. But having Google imposing its way since start — and making sure that all the direct competition of Google is just a side, I think governments have now seen the problem. And we’re a winner of course because we’re a keyboard.”
But what about iOS? Plante says the team has plans to bring what they’re building with Fleksy to Apple’s mobile platform too, in time. But for now they’re fully focusing efforts on Android — to push for scale and execute on their vision of staking their claim to be the independent keyboard platform.
Apple has supported third party keyboards on iOS for years. Unfortunately, though, the experience isn’t great — with a flaky toggle to switch away from the default Apple keyboard, combined with heavy system warnings about the risks of using third party keyboards.
Meanwhile the default iOS keyboard ‘just works’ — and users have loads of extra features baked by default into Apple’s native messaging app, iMessage.
Clearly alternative keyboards have found it all but impossible to build any kind of scale in that iOS pincer.
“iOS is coming later because we need to focus on these distribution deals and we need to focus on the brands coming into the platform. And that’s why iOS right now we’re really focusing for later. What we can say is it will come later,” says Plante, adding: “Apple limits a lot keyboards. You can see it with other keyboard companies. It’s the same. The update cycle for iOS keyboard is really, really, really slow.”
Plus, of course, Fleksy being preloaded as a default keyboard on — the team hopes — millions of Android devices is a much more scalable proposition vs just being another downloadable app languishing invisibly on the side lines of another tech giant’s platform.
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Pixeom raises $15M for its software-defined edge computing platform
Pixeom, a startup that offers a software-defined edge computing platform to enterprises, today announced that it has raised a $15 million funding round from Intel Capital, National Grid Partners and previous investor Samsung Catalyst Fund. The company plans to use the new funding to expand its go-to-market capacity and invest in product development.
If the Pixeom name sounds familiar, that may be because you remember it as a Raspberry Pi-based personal cloud platform. Indeed, that’s the service the company first launched back in 2014. It quickly pivoted to an enterprise model, though. As Pixeom CEO Sam Nagar told me, that pivot came about after a conversation the company had with Samsung about adopting its product for that company’s needs. In addition, it was also hard to find venture funding. The original Pixeom device allowed users to set up their own personal cloud storage and other applications at home. While there is surely a market for these devices, especially among privacy-conscious tech enthusiasts, it’s not massive, especially as users became more comfortable with storing their data in the cloud. “One of the major drivers [for the pivot] was that it was actually very difficult to get VC funding in an industry where the market trends were all skewing towards the cloud,” Nagar told me.
At the time of its launch, Pixeom also based its technology on OpenStack, the massive open-source project that helps enterprises manage their own data centers, which isn’t exactly known as a service that can easily be run on a single machine, let alone a low-powered one. Today, Pixeom uses containers to ship and manage its software on the edge.
What sets Pixeom apart from other edge computing platforms is that it can run on commodity hardware. There’s no need to buy a specific hardware configuration to run the software, unlike Microsoft’s Azure Stack or similar services. That makes it significantly more affordable to get started and allows potential customers to reuse some of their existing hardware investments.
Pixeom brands this capability as “software-defined edge computing” and there is clearly a market for this kind of service. While the company hasn’t made a lot of waves in the press, more than a dozen Fortune 500 companies now use its services. With that, the company now has revenues in the double-digit millions and its software manages more than a million devices worldwide.
As is so often the case in the enterprise software world, these clients don’t want to be named, but Nagar tells me they include one of the world’s largest fast food chains, for example, which uses the Pixeom platform in its stores.
On the software side, Pixeom is relatively cloud agnostic. One nifty feature of the platform is that it is API-compatible with Google Cloud Platform, AWS and Azure and offers an extensive subset of those platforms’ core storage and compute services, including a set of machine learning tools. Pixeom’s implementation may be different, but for an app, the edge endpoint on a Pixeom machine reacts the same way as its equivalent endpoint on AWS, for example.
Until now, Pixeom mostly financed its expansion — and the salary of its more than 90 employees — from its revenue. It only took a small funding round when it first launched the original device (together with a Kickstarter campaign). Technically, this new funding round is part of this, so depending on how you want to look at this, we’re either talking about a very large seed round or a Series A round.
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Vizion.ai launches its managed Elasticsearch service
Setting up Elasticsearch, the open-source system that many companies large and small use to power their distributed search and analytics engines, isn’t the hardest thing. What is very hard, though, is to provision the right amount of resources to run the service, especially when your users’ demand comes in spikes, without overpaying for unused capacity. Vizion.ai’s new Elasticsearch Service does away with all of this by essentially offering Elasticsearch as a service and only charging its customers for the infrastructure they use.
Vizion.ai’s service automatically scales up and down as needed. It’s a managed service and delivered as a SaaS platform that can support deployments on both private and public clouds, with full API compatibility with the standard Elastic stack that typically includes tools like Kibana for visualizing data, Beats for sending data to the service and Logstash for transforming the incoming data and setting up data pipelines. Users can easily create several stacks for testing and development, too, for example.
Vizion.ai GM and VP Geoff Tudor
“When you go into the AWS Elasticsearch service, you’re going to be looking at dozens or hundreds of permutations for trying to build your own cluster,” Vision.ai’s VP and GM Geoff Tudor told me. “Which instance size? How many instances? Do I want geographical redundancy? What’s my networking? What’s my security? And if you choose wrong, then that’s going to impact the overall performance. […] We do balancing dynamically behind that infrastructure layer.” To do this, the service looks at the utilization patterns of a given user and then allocates resources to optimize for the specific use case.
What VVizion.ai hasdone here is take some of the work from its parent company Panzura, a multi-cloud storage service for enterprises that has plenty of patents around data caching, and applied it to this new Elasticsearch service.
There are obviously other companies that offer commercial Elasticsearch platforms already. Tudor acknowledges this, but argues that his company’s platform is different. With other products, he argues, you have to decide on the size of your block storage for your metadata upfront, for example, and you typically want SSDs for better performance, which can quickly get expensive. Thanks to Panzura’s IP, Vizion.ai is able to bring down the cost by caching recent data on SSDs and keeping the rest in cheaper object storage pools.
He also noted that the company is positioning the overall Vizion.ai service, with the Elasticsearch service as one of the earliest components, as a platform for running AI and ML workloads. Support for TensorFlow, PredictionIO (which plays nicely with Elasticsearch) and other tools is also in the works. “We want to make this an easy serverless ML/AI consumption in a multi-cloud fashion, where not only can you leverage the compute, but you can also have your storage of record at a very cost-effective price point.”
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Kong raises $43M Series C for its API platform
Kong, the open core API management and life cycle management company previously known as Mashape, today announced that it has raised a $43 million Series C round led by Index Ventures. Previous investors Andreessen Horowitz and Charles River Ventures (CRV), as well as new investors GGV Capital and World Innovation Lab, also participated. With this round, Kong has now raised a total of $71 million.
The company’s CEO and co-founder Augusto Marietti tells me the company plans to use the funds to build out its service control platform. He likened this service to the “nervous system for an organization’s software architecture.”
Right now, Kong is just offering the first pieces of this, though. One area the company plans to especially focus on is security, in addition to its existing management tools, where Kong plans to add more machine learning capabilities over time, too. “It’s obviously a 10-year journey, but those two things — immunity with security and machine learning with [Kong] Brain — are really a 10-year journey of building an intelligent platform that can manage all the traffic in and out of an organization,” he said.
In addition, the company also plans to invest heavily in its expansion in both Europe and the Asia Pacific market. This also explains the addition of World Innovation Lab as an investor. The firm, after all, focuses heavily on connecting companies in the U.S. with partners in Asia — and especially Japan. As Marietti told me, the company is seeing a lot of demand in Japan and China right now, so it makes sense to capitalize on this, especially as the Chinese market is about to become more easily accessible for foreign companies.
Kong notes that it doubled its headcount in 2018 and now has more than 100 enterprise customers, including Yahoo! Japan, Ferrari, SoulCycle and WeWork.
It’s worth noting that while this is officially a Series C investment, Marietti is thinking of it more like a Series B round, given that the company went through a major pivot when it moved from being Mashape to its focus on Kong, which was already its most popular open-source tool.
“Modern software is now built in the cloud, with applications consuming other applications, service to service,” said Martin Casado, general partner at Andreessen Horowitz . “We’re at the tipping point of enterprise adoption of microservices architectures, and companies are turning to new open-source-based developer tools and platforms to fuel their next wave of innovation. Kong is uniquely suited to help enterprises as they make this shift by supporting an organization’s entire service architecture, from centralized or decentralized, monolith or microservices.”
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Vonage brings number programmability to its business service
Chances are you still mostly think of Vonage as a consumer VOIP player, but in recent years, the company also launched its Vonage Business Cloud (VBC) platform and acquired Nexmo, an API-based communications service that competes directly with many of Twilio’s core services. Today, Vonage is bringing its VBC service and Nexmo a bit closer with the launch of number programmability for its business customers.
What this means is that enterprises can now take any VBC number and extend it with the help of Nexmo’s APIs. To enable this, all they have to do is toggle a switch in their management console and then they’ll be able to programmatically route calls, create custom communications apps and workflows, and integrate third-party systems to build chatbots and other tools.
“About four years ago we made a pretty strong pivot to going from residential — a lot of people know Vonage as a residential player — to the business side,” Vonage senior VP of product management Jay Patel told me. “And through a series of acquisitions [including Nexmo], we’ve kind of built what we think is a very unique offering.” In many ways, those different platforms were always separated from each other, though. With all of the pieces in place now, however, the team started thinking about how it could use the Nexmo APIs to allow its customers in the unified communications and contact center space to more easily customize these services for them.
About a year ago, the team started working on this new functionality that brings the programmability of Nexmo to VBC. “We realized it doesn’t make sense for us to create our own new sets of APIs on our unified communications and contact center space,” said Patel. “Why don’t we use the APIs that Nexmo has already built?”
As Patel also stressed, the phone number is still very much linked to a business or individual employee — and they don’t want to change that just for the sake of having a programmable service. By turning on programmability for these existing numbers, though, and leveraging the existing Nexmo developer ecosystem and the building blocks those users have already created, the company believes that it’s able to offer a differentiated service that allows users to stay on its platform instead of having to forward a call to a third-party service like Twilio, for example, to enable similar capabilities.
In terms of those capabilities, users can pretty much do anything they want with these calls — and that’s important because every company has different processes and requirements. Maybe that’s logging info into multiple CRM systems in parallel or taking a clip of a call and pushing it into a different system for training purposes. Or you could have the system check your calendar when there are incoming calls and then, if it turns out you are in a meeting, offer the caller a callback whenever your calendar says you’re available again. All of that should only take a few lines of code or, if you want to avoid most of the coding, a few clicks in the company’s GUI for building these flows.
Vonage believes that these new capabilities will attract quite a few new customers. “It’s our value-add when we’re selling to new customers,” he said. “They’re looking for this kind of capability or are running into brick walls. We see a lot of companies that have an idea but they don’t know how to do it. They’re not engineers or they don’t have a big staff of developers, but because of the way we’ve implemented this, it brings the barrier of entry to create these solutions much lower than if you had a legacy system on-prem where you had to be a C++ developer to build an app.
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