Mar
02
2021
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Microsoft Azure expands its NoSQL portfolio with Managed Instances for Apache Cassandra

At its Ignite conference today, Microsoft announced the launch of Azure Managed Instance for Apache Cassandra, its latest NoSQL database offering and a competitor to Cassandra-centric companies like Datastax. Microsoft describes the new service as a ‘semi-managed offering that will help companies bring more of their Cassandra-based workloads into its cloud.

“Customers can easily take on-prem Cassandra workloads and add limitless cloud scale while maintaining full compatibility with the latest version of Apache Cassandra,” Microsoft explains in its press materials. “Their deployments gain improved performance and availability, while benefiting from Azure’s security and compliance capabilities.”

Like its counterpart, Azure SQL Manages Instance, the idea here is to give users access to a scalable, cloud-based database service. To use Cassandra in Azure before, businesses had to either move to Cosmos DB, its highly scalable database service which supports the Cassandra, MongoDB, SQL and Gremlin APIs, or manage their own fleet of virtual machines or on-premises infrastructure.

Cassandra was originally developed at Facebook and then open-sourced in 2008. A year later, it joined the Apache Foundation and today it’s used widely across the industry, with companies like Apple and Netflix betting on it for some of their core services, for example. AWS launched a managed Cassandra-compatible service at its re:Invent conference in 2019 (it’s called Amazon Keyspaces today), Microsoft launched the Cassandra API for Cosmos DB in September 2018. With today’s announcement, though, the company can now offer a full range of Cassandra-based servicer for enterprises that want to move these workloads to its cloud.


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Feb
17
2021
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Spectral raises $6.2M for its DevSecOps service

Tel Aviv-based Spectral is bringing its new DevSecOps code scanner out of stealth today and announcing a $6.2 million funding round. The startup’s programming language-agnostic service aims to automated code security development teams to help them detect potential security issues in their codebases and logs, for example. Those issues could be hardcoded API keys and other credentials, but also security misconfiguration and shadow IT assets.

The four-person founding team has a deep background in building AI, monitoring and security tools. CEO Dotan Nahum was a Chief Architect at Klarna and Conduit (now Como, though you may remember Conduit from its infamous toolbar that was later spun off), and the CTO at Como and HiredScore, for example. Other founders worked on building monitoring tools at Elastic and HP and on security at Akamai. As Nahum told me, the idea for Spectral came to him and co-founder and COO Idan Didi during their shared time at mobile application build Conduit/Como.

Image Credits: Spectral

“We basically stored certificates for every client that we had, so we could submit their apps to the various marketplaces,” Nahum told me of his experience at Counduit/Como. “That certificate really proves that you are who you are and it’s super sensitive. And at each point at these companies, I really didn’t have the right tools to actually make sure that we’re storing, handling, detecting [this information] and making sure that it doesn’t leak anywhere.”

Nahum decided to quit his current job and started to build a prototype to see if he could build a tool that could solve this problem (and his work on this prototype quickly discovered an issue at Slack). And as enterprises move from on-premises software to the cloud and to microservices and DevOps, the need for better DevSecOps tools is only increasing.

“The emphasis is to create a great developer experience,” Nahum noted. “Because that’s where we started from. We didn’t start as a top down cyber tool. We started as a modest DevOps friendly, developer-friendly tool.”

Image Credits: Spectral

One interesting aspect of Spectral’s approach, which uses a machine learning model to detect these breaches across programming languages, is that it also scans public-facing systems. On the backend, Spectral integrates with tools like Travis, Jenkins, CircleCI, Webpack, Gatsby and Netlify, but it can also monitor Slack, npm, maven and log providers — tools that most companies don’t really think about when they think about threat modeling.

“Our solution prevents security breaches on a daily basis,” said Spectral co-founder and COO Idan Didi. “The pain points we’re addressing resonate strongly across every company developing software, because as they evolve from own-code to glue-code to no-code approaches they allow their developers to gain more speed, but they also add on significant amounts of risk. Spectral lets developers be more productive while keeping the company secure.”

The company was founded in mid-2020, but it already has about 15 employees and counts a number of large publicly-listed companies among its customers.

Feb
17
2021
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Sinch acquires Inteliquent for $1.14B to take on Twilio in the US

After raising $690 million from SoftBank in December to make acquisitions, the Sweden-based cloud communications company Sinch has followed through on its strategy in that department. Today the company announced that it is acquiring Inteliquent, an interconnection provider for voice communications in the U.S. currently owned by private equity firm GTCR, for $1.14 billion in cash.

And to finance the deal, Sinch said it has raised financing totaling SEK8.2 billion — $986 million — from Handelsbanken and Danske Bank, along with other facilities it had in place.

The deal will give Sinch — a competitor to Twilio with a range of messaging, calling and marketing (engagement) APIs for those building communications into their services in mobile apps and other services — a significant foothold in the U.S. market.

Inteliquent — a profitable company with 500 employees and revenues of $533 million, gross profit of $256 million and EBITDA of $135 million in 2020 — claims to be one of the biggest voice carriers in North America, serving both other service providers and enterprises. Its network connects to all the major telcos, covering 94% of the U.S. population, with more than 300 billion minutes of voice calls and 100 million phone numbers handled annually for customers.

Sinch is publicly traded in Sweden — where its market cap is currently at $13 billion (just over 108 billion Swedish krona) — and the acquisition begs the question of whether the company plans to establish more of a financial presence in the U.S., for example with a listing there. We have asked the company what its next steps might be and will update this post as and when we learn more.

“Becoming a leader in the U.S. voice market is key to establish Sinch as the leading global cloud communications platform,” said Oscar Werner, Sinch CEO, in a statement. “Inteliquent serves the largest and most demanding voice customers in America with superior quality backed by a fully-owned network across the entire U.S.. Our joint strengths in voice and messaging provide a unique position to grow our business and power a superior customer experience for our customers.”

Inteliquent provides two main areas of service, Communications-Platform-as-a-Service (CPaaS) for API-based services to provide voice calling and phone numbers; and more legacy Infrastructure-as-a-Service (IaaS) products for telcos such as off-net call termination (when a call is handed off from one carrier to another) and toll-free numbers. These each account for roughly half of the total business although — unsurprisingly — the CPaaS business is growing at twice the rate of IaaS.

Its business, like many others focusing on services for people who are relying more on communications services as they are seeing each other in person less — saw a surge of use this past year, it said. (Revenues adjusted without COVID lift, it noted, would have been $499 million, so still healthy.)

Sinch is focused on delivering unparalleled customer experiences at scale and with the investors we have today, we believe we have the financial muscle for both extensive product development and M&A that is needed to take advantage of a consolidating global market as we continue building the leading CPaaS company,” Werner told TechCrunch over email.

As for Sinch, since being founded by CLX in 2008 (its name was a rebrand after CLX acquired Sinch, which spun out from Rebtel in 2014) to take on the business of providing communications tools to developers, it has been on an acquisition roll to bulk up its geographical reach and the services that it provides to those customers.

Deals have included, most recently, buying ACL in India for $70 million and SAP’s digital interconnect business for $250 million. The deals — combined with Twilio’s own acquisitions of companies like SendGrid for $2 billion and last year’s Segment for $3.2 billon, speak both to the bigger trend of consolidation in the digital (API-based) communications space, as well as the huge value that is contained within it.

Inteliquent itself had been in private equity hands before this, controlled by GTCR based in Chicago, like Inteliquent itself. According to PitchBook, its most recent financing was a mezzanine loan from Oaktree Capital in 2018 for just under $19 million.

Interestingly, Inteliquent itself has been an investor in innovative communications startups, participating in a Series B for Zipwhip, a startup that is building better ways to integrate mobile messaging tools into landline services.

“We’re excited about the tremendous opportunities this combination unlocks, expanding the services we can provide to our customers. Combining our leading voice offering with Sinch’s global messaging capabilities truly positions us for leadership in the rapidly developing market for cloud communications“, comments Ed O’Hara, Inteliquent CEO, in a statement.

Feb
04
2021
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Google Cloud launches Apigee X, the next generation of its API management platform

Google today announced the launch of Apigee X, the next major release of the Apgiee API management platform it acquired back in 2016.

“If you look at what’s happening — especially after the pandemic started in March last year — the volume of digital activities has gone up in every kind of industry, all kinds of use cases are coming up. And one of the things we see is the need for a really high-performance, reliable, global digital transformation platform,” Amit Zavery, Google Cloud’s head of platform, told me.

He noted that the number of API calls has gone up 47 percent from last year and that the platform now handles about 2.2 trillion API calls per year.

At the core of the updates are deeper integrations with Google Cloud’s AI, security and networking tools. In practice, this means Apigee users can now deploy their APIs across 24 Google Cloud regions, for example, and use Google’s caching services in more than 100 edge locations.

Image Credits: Google

In addition, Apigee X now integrates with Google’s Cloud Armor firewall and its Cloud Identity Access Management platform. This also means that Apigee users won’t have to use third-party tools for their firewall and identity management needs.

“We do a lot of AI/ML-based anomaly detection and operations management,” Zavery explained. “We can predict any kind of malicious intent or any other things which might happen to those API calls or your traffic by embedding a lot of those insights into our API platform. I think [that] is a big improvement, as well as new features, especially in operations management, security management, vulnerability management and making those a core capability so that as a business, you don’t have to worry about all these things. It comes with the core capabilities and that is really where the front doors of digital front-ends can shine and customers can focus on that.”

The platform now also makes better use of Google’s AI capabilities to help users identify anomalies or predict traffic for peak seasons. The idea here is to help customers automate a lot of the standards automation tasks and, of course, improve security at the same time.

As Zavery stressed, API management is now about more than just managing traffic between applications. But more than just helping customers manage their digital transformation projects, the Apigee team is now thinking about what it calls ‘digital excellence.’ “That’s how we’re thinking of the journey for customers moving from not just ‘hey, I can have a front end,’ but what about all the excellent things you want to do and how we can do that,” Zavery said.

“During these uncertain times, organizations worldwide are doubling-down on their API strategies to operate anywhere, automate processes, and deliver new digital experiences quickly and securely,” said James Fairweather, Chief Innovation Officer at Pitney Bowes. “By powering APIs with new capabilities like reCAPTCHA Enterprise, Cloud Armor (WAF), and Cloud CDN, Apigee X makes it easy for enterprises like us to scale digital initiatives, and deliver innovative experiences to our customers, employees and partners.”

Nov
12
2020
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Mirantis brings extensions to its Lens Kubernetes IDE, launches a new Kubernetes distro

Earlier this year, Mirantis, the company that now owns Docker’s enterprise business, acquired Lens, a desktop application that provides developers with something akin to an IDE for managing their Kubernetes clusters. At the time, Mirantis CEO Adrian Ionel told me that the company wants to offer enterprises the tools to quickly build modern applications. Today, it’s taking another step in that direction with the launch of an extensions API for Lens that will take the tool far beyond its original capabilities.

In addition to this update to Lens, Mirantis also today announced a new open-source project: k0s. The company describes it as “a modern, 100% upstream vanilla Kubernetes distro that is designed and packaged without compromise.”

It’s a single optimized binary without any OS dependencies (besides the kernel). Based on upstream Kubernetes, k0s supports Intel and Arm architectures and can run on any Linux host or Windows Server 2019 worker nodes. Given these requirements, the team argues that k0s should work for virtually any use case, ranging from local development clusters to private data centers, telco clusters and hybrid cloud solutions.

“We wanted to create a modern, robust and versatile base layer for various use cases where Kubernetes is in play. Something that leverages vanilla upstream Kubernetes and is versatile enough to cover use cases ranging from typical cloud based deployments to various edge/IoT type of cases,” said Jussi Nummelin, senior principal engineer at Mirantis and founder of k0s. “Leveraging our previous experiences, we really did not want to start maintaining the setup and packaging for various OS distros. Hence the packaging model of a single binary to allow us to focus more on the core problem rather than different flavors of packaging such as debs, rpms and what-nots.”

Mirantis, of course, has a bit of experience in the distro game. In its earliest iteration, back in 2013, the company offered one of the first major OpenStack distributions, after all.

Image Credits: Mirantis

As for Lens, the new API, which will go live next week to coincide with KubeCon, will enable developers to extend the service with support for other Kubernetes-integrated components and services.

“Extensions API will unlock collaboration with technology vendors and transform Lens into a fully featured cloud native development IDE that we can extend and enhance without limits,” said Miska Kaipiainen, the co-founder of the Lens open-source project and senior director of engineering at Mirantis. “If you are a vendor, Lens will provide the best channel to reach tens of thousands of active Kubernetes developers and gain distribution to your technology in a way that did not exist before. At the same time, the users of Lens enjoy quality features, technologies and integrations easier than ever.”

The company has already lined up a number of popular CNCF projects and vendors in the cloud-native ecosystem to build integrations. These include Kubernetes security vendors Aqua and Carbonetes, API gateway maker Ambassador Labs and AIOps company Carbon Relay. Venafi, nCipher, Tigera, Kong and StackRox are also currently working on their extensions.

“Introducing an extensions API to Lens is a game-changer for Kubernetes operators and developers, because it will foster an ecosystem of cloud-native tools that can be used in context with the full power of Kubernetes controls, at the user’s fingertips,” said Viswajith Venugopal, StackRox software engineer and developer of KubeLinter. “We look forward to integrating KubeLinter with Lens for a more seamless user experience.”

Jun
22
2020
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Hasura launches managed cloud service for its open-source GraphQL API platform

Hasura is an open-source engine that can connect to PostgreSQL databases and microservices across hybrid- and multi-cloud environments and then automatically build a GraphQL API backend for them, making it easier for developers to then build their own data-driven applications on top of this unified API . For a while now, the San Francisco-based startup has offered a paid version (Hasura Pro) with enterprise-ready reliability and security tools, in addition to its free open-source version. Today, the company launched Hasura Cloud, which takes the existing Pro version, adds a number of cloud-specific features like dynamic caching, auto-scaling and consumption-based pricing, and brings those together in a fully managed service.

Image Credits: Hasura

At its core, Hasura’s service promises businesses the ability to bring together data from their various siloed databases and allow their developers to extract value from them through its GraphQL APIs. While GraphQL is still relatively new, the Facebook-incubated technology has quickly become extremely popular among many development teams.

Before founding the company and launching it in 2018, Hasura CEO and co-founder Tanmai Gopal worked for a consulting firm — and like with so many founders, that’s where he got the inspiration for the service.

“One of the key things that we noticed was that in the entire landscape, computing is becoming better, there are better frameworks, it is easier to deploy code, databases are becoming better and they kind of work everywhere,” he said. “But this kind of piece in the middle that is still a bottleneck and that there isn’t really a good solution for is this data access piece.” Almost by default, most companies host data in various SaaS services and databases — and now they were trying to figure out how to develop apps based on this for both internal and external consumers, noted Gopal. “This data distribution problem was this bottleneck where everybody would just spend massive amounts of time and money. And we invented a way of kind of automating that,” he explained.

The choice of GraphQL was also pretty straightforward, especially because GraphQL services are an easy way for developers to consume data (even though, as Gopal noted, it’s not always fun to build the GraphQL service itself). One thing that’s unusual and worth noting about the core Hasura engine itself is that it is written in Haskell, which is a rather unusual choice.

Image Credits: Hasura

The team tells me that Hasura is now nearing 50 million downloads for its free version and the company is seeing large and small users from across various industries relying on its products, which is probably no surprise, given that the company is trying to solve a pretty universal problem around data access and consumption.

Over the last few quarters, the team worked on launching its cloud service. “We’ve been thinking of the cloud in a very different way,” Gopal said. “It’s not your usual, take the open-source solution and host it, like a MongoDB Atlas or Confluent. What we’ve done is we’ve said, we’re going to re-engineer the open-source solution to be entirely multi-tenant and be completely pay-per pricing.”

Given this philosophy, it’s no surprise that Hasura’s pricing is purely based on how much data a user moves through the service. “It’s much closer to our value proposition,” Hasura co-founder and COO Rajoshi Ghosh said. “The value proposition is about data access. The big part of it is the fact that you’re getting this data from your databases. But the very interesting part is that this data can actually come from anywhere. This data could be in your third-party services, part of your data could be living in Stripe and it could be living in Salesforce, and it could be living in other services. […] We’re the data access infrastructure in that sense. And this pricing also — from a mental model perspective — makes it much clearer that that’s the value that we’re adding.”

Now, there are obviously plenty of other data-centric API services on the market, but Gopal argues that Hasura has an advantage because of its advanced caching for dynamic data, for example.

Apr
28
2020
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Checkly raises $2.25M seed round for its monitoring and testing platform

Checkly, a Berlin-based startup that is developing a monitoring and testing platform for DevOps teams, today announced that it has raised a $2.25 million seed round led by Accel. A number of angel investors, including Instana CEO Mirko Novakovic, Zeit CEO Guillermo Rauch and former Twilio CTO Ott Kaukver, also participated in this round.

The company’s SaaS platform allows developers to monitor their API endpoints and web apps — and it obviously alerts you when something goes awry. The transaction monitoring tool makes it easy to regularly test interactions with front-end websites without having to actually write any code. The test software is based on Google’s open-source Puppeteer framework and to build its commercial platform, Checkly also developed Puppeteer Recorder for creating these end-to-end testing scripts in a low-code tool that developers access through a Chrome extension.

The team believes that it’s the combination of end-to-end testing and active monitoring, as well as its focus on modern DevOps teams, that makes Checkly stand out in what is already a pretty crowded market for monitoring tools.

“As a customer in the monitoring market, I thought it had long been stuck in the 90s and I needed a tool that could support teams in JavaScript and work for all the different roles within a DevOps team. I set out to build it, quickly realizing that testing was equally important to address,” said Tim Nolet, who founded the company in 2018. “At Checkly, we’ve created a market-defining tool that our customers have been demanding, and we’ve already seen strong traction through word of mouth. We’re delighted to partner with Accel on building out our vision to become the active reliability platform for DevOps teams.”

Nolet’s co-founders are Hannes Lenke, who founded TestObject (which was later acquired by Sauce Labs), and Timo Euteneuer, who was previously Director Sales EMEA at Sauce Labs.

Tthe company says that it currently has about 125 paying customers who run about 1 million checks per day on its platform. Pricing for its services starts at $7 per month for individual developers, with plans for small teams starting at $29 per month.

Apr
22
2020
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AWS launches Amazon AppFlow, its new SaaS integration service

AWS today launched Amazon AppFlow, a new integration service that makes it easier for developers to transfer data between AWS and SaaS applications like Google Analytics, Marketo, Salesforce, ServiceNow, Slack, Snowflake and Zendesk. Like similar services, including Microsoft Azure’s Power Automate, for example, developers can trigger these flows based on specific events, at pre-set times or on-demand.

Unlike some of its competitors, though, AWS is positioning this service more as a data transfer service than a way to automate workflows and while the data flow can be bi-directional, AWS’s announcement focuses mostly on moving data from SaaS applications to other AWS services for further analysis. For this, AppFlow also includes a number of tools for transforming the data as it moves through the service.

“Developers spend huge amounts of time writing custom integrations so they can pass data between SaaS applications and AWS services so that it can be analysed; these can be expensive and can often take months to complete,” said AWS principal advocate Martin Beeby in today’s announcement. “If data requirements change, then costly and complicated modifications have to be made to the integrations. Companies that don’t have the luxury of engineering resources might find themselves manually importing and exporting data from applications, which is time-consuming, risks data leakage, and has the potential to introduce human error.”

Every flow (which AWS defines as a call to a source application to transfer data to a destination) costs $0.001 per run, though, in typical AWS fashion, there’s also cost associated with data processing (starting at 0.02 per GB).

“Our customers tell us that they love having the ability to store, process, and analyze their data in AWS. They also use a variety of third-party SaaS applications, and they tell us that it can be difficult to manage the flow of data between AWS and these applications,” said Kurt Kufeld, Vice President, AWS. “Amazon AppFlow provides an intuitive and easy way for customers to combine data from AWS and SaaS applications without moving it across the public Internet. With Amazon AppFlow, our customers bring together and manage petabytes, even exabytes, of data spread across all of their applications – all without having to develop custom connectors or manage underlying API and network connectivity.”

At this point, the number of supported services remains comparatively low, with only 14 possible sources and four destinations (Amazon Redshift and S3, as well as Salesforce and Snowflake). Sometimes, depending on the source you select, the only possible destination is Amazon’s S3 storage service.

Over time, the number of integrations will surely increase, but for now, it feels like there’s still quite a bit more work to do for the AppFlow team to expand the list of supported services.

AWS has long left this market to competitors, even though it has tools like AWS Step Functions for building serverless workflows across AWS services and EventBridge for connections applications. Interestingly, EventBridge currently supports a far wider range of third-party sources, but as the name implies, its focus is more on triggering events in AWS than moving data between applications.

Oct
02
2019
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Kong acquires Insomnia, launches Kong Studio for API development

API and microservices platform Kong today announced that it has acquired Insomnia, a popular open-source tool for debugging APIs. The company, which also recently announced that it had raised a $43 million Series C round, has already put this acquisition to work by using it to build Kong Studio, a tool for designing, building and maintaining APIs for both REST and GraphQL endpoints.

As Kong CEO and co-founder Augusto Marietti told me, the company wants to expand its platform to cover the full service life cycle. So far, it has mostly focused on the runtime, but now it wants to enable developers to also design and test their services. “We looked at the space and Insomnia is the number one open source API testing platform,” he told me. “And we thought that by having Insomnia in our portfolio, we will get the pre-production part of things and on top of that, we’ll be able to build Kong Studio, which is kind of the other side of Insomnia that allows you to design APIs.”

For Oct. 2 Kong News Kong Service Control Platform

Insomnia launched in 2015, as a side project of its sole developer, Greg Schier. Schier quit his job in 2016 to focus on Insomnia full-time and then open-sourced it in 2017. Today, the project has 100 contributors and the tool is used by “hundreds of thousands of developers,” according to Schier.

Marietti says both the open-source project and the paid Insomnia Plus service will continue to operate as before.

In addition to Kong Studio and the Insomnia acquisition, the company also today launched the latest version of its Enterprise service, the aptly named Kong Enterprise 2020. New features here include support for REST, Kafka Streams and GraphQL. Kong also launched Kong Gateway 2.0 with additional GraphQL support and the ability to write plugins in Go.

Aug
01
2019
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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 U.S. 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 120 million 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 $2 million 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 less than 100 milliseconds, meaning the AI can turn-take and handle interruptions in the conversation flow. The platform also can 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 toward 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-fueled 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 26.3 billion robocalls in 2018 alone — up “a whopping” 46% on 2017.

Its conversation engine, meanwhile, has only made some 3 million 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 $30 billion worldwide,” runs a line from its PR.

After the developer platform launch, Chernyshov says the next step will be to open 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-fueled vision of voice-based business process automation comes 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 on what sample size the claim is based. (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 the 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 cancellation.

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 modeling engine.” “All those [rival engines] were built from scratch with a focus on chatbots — on text,” he says, couching modeling 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 10 masters of science in computer science.

It has an R&D office in Russia, 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 modeling 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 meantime, 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.

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