Personal blog of Yzmir Ramirez

More money for the now very buzzy business of reshaping how people work: Worksome is announcing it recently closed a $13 million Series A funding round for its “freelance talent platform” — after racking up 10x growth in revenue since January 2020, just before the COVID-19 pandemic sparked a remote working boom.

The 2017 founded startup, which has a couple of ex-Googlers in its leadership team, has built a platform to connect freelancers looking for professional roles with employers needing tools to find and manage freelancer talent.

It says it’s seeing traction with large enterprise customers that have traditionally used Managed Service Providers (MSPs) to manage and pay external workforces — and views employment agency giants like Randstad, Adecco and Manpower as ripe targets for disruption.

“Most multinational enterprises manage flexible workers using legacy MSPs,” says CEO and co-founder Morten Petersen (one of the Xooglers). “These largely analogue businesses manage complex compliance and processes around hiring and managing freelance workforces with handheld processes and outdated technology that is not built for managing fluid workforces. Worksome tackles this industry head on with a better, faster and simpler solution to manage large freelancer and contractor workforces.”

Worksome focuses on helping medium/large companies — who are working with at least 20+ freelancers at a time — fill vacancies within teams rather than helping companies outsource projects, per Petersen, who suggests the latter is the focus for the majority of freelancer platforms.

“Worksome helps [companies] onboard people who will provide necessary skills and will be integral to longer-term business operations. It makes matches between companies and skilled freelancers, which the businesses go on to trust, form relationships with and come back to time and time again,” he goes on.

“When companies hire dozens or hundreds of freelancers at one time, processes can get very complicated,” he adds, arguing that on compliance and payments Worksome “takes on a much greater responsibility than other freelancing platforms to make big hires easier”.

The startup also says it’s concerned with looking out for (and looking after) its freelancer talent pool — saying it wants to create “a world of meaningful work” on its platform, and ensure freelancers are paid fairly and competitively. (And also that they are paid faster than they otherwise might be, given it takes care of their payroll so they don’t have to chase payments from employers.)

The business started life in Copenhagen — and its Series A has a distinctly Nordic flavor, with investment coming from the Danish business angel and investor on the local version of the Dragons’ Den TV program Løvens Hule; the former Minister for Higher Education and Science, Tommy Ahlers; and family home manufacturer Lind & Risør.

It had raised just under $6M prior to thus round, per Crunchbase, and also counts some (unnamed) Google executives among its earlier investors.

Freelancer platforms (and marketplaces) aren’t new, of course. There are also an increasing number of players in this space — buoyed by a new flush of VC dollars chasing the ‘future of work’, whatever hybrid home-office flexible shape that might take. So Worksome is by no means alone in offering tech tools to streamline the interface between freelancers and businesses.

A few others that spring to mind include Lystable (now Kalo), Malt, Fiverr — or, for techie job matching specifically, the likes of HackerRank — plus, on the blue collar work side, Jobandtalent. There’s also a growing number of startups focusing on helping freelancer teams specifically (e.g. Collective), so there’s a trend towards increasing specialism.

Worksome says it differentiates vs other players (legacy and startups) by combining services like tax compliance, background and ID checks and handling payroll and other admin with an AI powered platform that matches talent to projects.

Although it’s not the only startup offering to do the back-office admin/payroll piece, either, nor the only one using AI to match skilled professionals to projects. But it claims it’s going further than rival ‘freelancer-as-a-service’ platforms — saying it wants to “address the entire value chain” (aka: “everything from the hiring of freelance talent to onboarding and payment”).

Worksome has 550 active clients (i.e. employers in the market for freelancer talent) at this stage; and has accepted 30,000 freelancers into its marketplace so far.

Its current talent pool can take on work across 12 categories, and collectively offers more than 39,000 unique skills, per Petersen.

The biggest categories of freelancer talent on the platform are in Software and IT; Design and Creative Work; Finance and Management Consulting; plus “a long tail of niche skills” within engineering and pharmaceuticals.

While its largest customers are found in the creative industries, tech and IT, pharma and consumer goods. And its biggest markets are the U.K. and U.S.

“We are currently trailing at +20,000 yearly placements,” says Petersen, adding: “The average yearly spend per client is $300,000.”

Worksome says the Series A funding will go on stoking growth by investing in marketing. It also plans to spend on product dev and on building out its team globally (it also has offices in London and New York).

Over the past 12 months the startup doubled the size of its team to 50 — and wants to do so again within 12 months so it can ramp up its enterprise client base in the U.S., U.K. and euro-zone.

“Yes, there are a lot of freelancer platforms out there but a lot of these don’t appreciate that hiring is only the tip of the iceberg when it comes to reducing the friction in working with freelancers,” argues Petersen. “Of the time that goes into hiring, managing and paying freelancers, 75% is currently spent on admin such as timesheet approvals, invoicing and compliance checks, leaving only a tiny fraction of time to actually finding talent.”

Worksome woos employers with a “one-click-hire” offer — touting its ability to find and hire freelancers “within seconds”.

If hiring a stranger in seconds sounds ill-advised, Worksome greases this external employment transaction by taking care of vetting the freelancers itself (including carrying out background checks; and using proprietary technology to asses freelancers’ skills and suitability for its marketplace).

“We have a two-step vetting process to ensure that we only allow the best freelance talent onto the Worksome platform,” Petersen tells TechCrunch. “For step one, an inhouse-built robot assesses our freelancer applicants. It analyses their skillset, social media profiles, profile completeness and hourly or daily rate, as well as their CV and work history, to decide whether each person is a good fit for Worksome.

“For step two, our team of talent specialists manually review and decline or approve the freelancers that pass through step one with a score of 85% or more. We have just approved our 30,000th freelancer and will be able to both scale and improve our vetting procedure as we grow.”

A majority of freelancer applicants fail Worksome’s proprietary vetting processes. This is clear because it says it has received 80,000 applicants so far — but only approved 30,000.

That raises interesting questions about how it’s making decisions on who is (and isn’t) an ‘appropriate fit’ for its talent marketplace.

It says its candidate assessing “robot” looks at “whether freelancers can demonstrate the skillset, matching work history, industry experience and profile depth” deemed necessary to meet its quality criteria — giving the example that it would not accept a freelancer who says they can lead complex IT infrastructure projects if they do not have evidence of relevant work, education and skills.

On the AI freelancer-to-project matching side, Worksome says its technology aims to match freelancers “who have the highest likelihood of completing a job with high satisfaction, based on their work-history, and performance and skills used on previous jobs”.

“This creates a feedback loop that… ensure that both clients and freelancers are matched with great people and great work,” is its circular suggestion when we ask about this.

But it also emphasizes that its AI is not making hiring decisions on its own — and is only ever supporting humans in making a choice. (An interesting caveat since existing EU data protection rules, under Article 22 of the GDPR, provide for a right for individuals to object to automated decision making if significant decisions are being taken without meaningful human interaction.) 

Using automation technologies (like AI) to make assessments that determine whether a person gains access to employment opportunities or doesn’t can certainly risk scaled discrimination. So the devil really is in the detail of how these algorithmic assessments are done.

That’s why such uses of technology are set to face close regulatory scrutiny in the European Union — under incoming rules on ‘high risk’ users of artificial intelligence — including the use of AI to match candidates to jobs.

The EU’s current legislative proposals in this area specifically categorize “employment, workers management and access to self-employment” as a high risk use of AI, meaning applications like Worksome are likely to face some of the highest levels of regulatory supervision in the future.

Nonetheless, Worksome is bullish when we ask about the risks associated with using AI as an intermediary for employment opportunities.

“We utilise fairly advanced matching algorithms to very effectively shortlist candidates for a role based solely on objective criteria, rinsed from human bias,” claims Petersen. “Our algorithms don’t take into account gender, ethnicity, name of educational institutions or other aspects that are usually connected to human bias.”

“AI has immense potential in solving major industry challenges such as recruitment bias, low worker mobility and low access to digital skills among small to medium sized businesses. We are firm believers that technology should be utilized to remove human bias’ from any hiring process,” he goes on, adding: “Our tech was built to this very purpose from the beginning, and the new proposed legislation has the potential to serve as a validator for the hard work we’ve put into this.

“The obvious potential downside would be if new legislation would limit innovation by making it harder for startups to experiment with new technologies. As always, legislation like this will impact the Davids more than the Goliaths, even though the intentions may have been the opposite.”

Zooming back out to consider the pandemic-fuelled remote working boom, Worksome confirms that most of the projects for which it supplied freelancers last year were conducted remotely.

“We are currently seeing a slow shift back towards a combination of remote and onsite work and expect this combination to stick amongst most of our clients,” Petersen goes on. “Whenever we are in uncertain economic times, we see a rise in the number of freelancers that companies are using. However, this trend is dwarfed by a much larger overall trend towards flexible work, which drives the real shift in the market. This shift has been accelerated by COVID-19 but has been underway for many years.

“While remote work has unlocked an enormous potential for accessing talent everywhere, 70% of the executives expect to use more temporary workers and contractors onsite than they did before COVID-19, according to a recent McKinsey study. This shows that businesses really value the flexibility in using an on-demand workforce of highly skilled specialists that can interact directly with their own teams.”

Asked whether it’s expecting growth in freelancing to sustain even after we (hopefully) move beyond the pandemic — including if there’s a return to physical offices — Petersen suggests the underlying trend is for businesses to need increased flexibility, regardless of the exact blend of full-time and freelancer staff. So platforms like Worksome are confidently poised to keep growing.

“When you ask business leaders, 90% believe that shifting their talent model to a blend of full-time and freelancers can give a future competitive advantage (Source: BCG),” he says. “We see two major trends driving this sentiment; access to talent, and building an agile and flexible organization. This has become all the more true during the pandemic — a high degree of flexibility is allowing organisations to better navigate both the initial phase of the pandemic as well the current pick up of business activity.

“With the amount of change that we’re currently seeing in the world, and with businesses are constantly re-inventing themselves, the access to highly skilled and flexible talent is absolutely essential — now, in the next 5 years, and beyond.”

AWS today closed out its first re:Invent keynote with a focus on edge computing. The company launched two smaller appliances for its Outpost service, which originally brought AWS as a managed service and appliance right into its customers’ existing data centers in the form of a large rack. Now, the company is launching these smaller versions so that its users can also deploy them in their stores or office locations. These appliances are fully managed by AWS and offer 64 cores of compute, 128GB of memory and 4TB of local NVMe storage.

In addition, the company expanded its set of Local Zones, which are basically small extensions of existing AWS regions that are more expensive to use but offer low-latency access in metro areas. This service launched in Los Angeles in 2019 and starting today, it’s also available in preview in Boston, Houston and Miami. Soon, it’ll expand to Atlanta, Chicago, Dallas, Denver, Kansas City, Las Vegas, Minneapolis, New York, Philadelphia, Phoenix, Portland and Seattle. Google, it’s worth noting, is doing something similar with its Mobile Edge Cloud.

The general idea here — and that’s not dissimilar from what Google, Microsoft and others are now doing — is to bring AWS to the edge and to do so in a variety of form factors.

As AWS CEO Andy Jassy rightly noted, AWS always believed that the vast majority of companies, “in the fullness of time” (Jassy’s favorite phrase from this keynote), would move to the cloud. Because of this, AWS focused on cloud services over hybrid capabilities early on. He argues that AWS watched others try and fail in building their hybrid offerings, in large parts because what customers really wanted was to use the same control plane on all edge nodes and in the cloud. None of the existing solutions from other vendors, Jassy argues, got any traction (though AWSs competitors would surely deny this) because of this.

The first result of that was VMware Cloud on AWS, which allowed customers to use the same VMware software and tools on AWS they were already familiar with. But at the end of the day, that was really about moving on-premises services to the cloud.

With Outpost, AWS launched a fully managed edge solution that can run AWS infrastructure in its customers’ data centers. It’s been an interesting journey for AWS, but the fact that the company closed out its keynote with this focus on hybrid — no matter how it wants to define it — shows that it now understands that there is clearly a need for this kind of service. The AWS way is to extend AWS into the edge — and I think most of its competitors will agree with that. Microsoft tried this early on with Azure Stack and really didn’t get a lot of traction, as far as I’m aware, but it has since retooled its efforts around Azure Arc. Google, meanwhile, is betting big on Anthos.

Companies that have leveraged technology to make the procurement and delivery of food more accessible to more people have been seeing a big surge of business this year, as millions of consumers are encouraged (or outright mandated, due to COVID-19) to socially distance or want to avoid the crowds of physical shopping and eating excursions.

Today, one of the companies that is supplying produce and other items both to consumers and other services that are in turn selling food and groceries to them, is announcing a new round of funding as it gears up to take its next step, an IPO.

GrubMarket, which provides a B2C platform for consumers to order produce and other food and home items for delivery, and a B2B service where it supplies grocery stores, meal-kit companies and other food tech startups with products that they resell, is today announcing that it has raised $60 million in a Series D round of funding.

Sources close to the company confirmed to TechCrunch that GrubMarket — which is profitable, and originally hadn’t planned to raise more than $20 million — has now doubled its valuation compared to its last round — sources tell us it is now between $400 million and $500 million.

The funding is coming from funds and accounts managed by BlackRock, Reimagined Ventures, Trinity Capital Investment, Celtic House Venture Partners, Marubeni Ventures, Sixty Degree Capital and Mojo Partners, alongside previous investors GGV Capital, WI Harper Group, Digital Garage, CentreGold Capital, Scrum Ventures and other unnamed participants. Past investors also included Y Combinator, where GrubMarket was part of the Winter 2015 cohort. For some context, GrubMarket last raised money in April 2019 — $28 million at a $228 million valuation, a source says.

Mike Xu, the founder and CEO, said that the plan remains for the company to go public (he’s talked about it before), but given that it’s not having trouble raising from private markets and is currently growing at 100% over last year, and the IPO market is less certain at the moment, he declined to put an exact timeline on when this might actually happen, although he was clear that this is where his focus is in the near future.

“The only success criteria of my startup career is whether GrubMarket can eventually make $100 billion of annual sales,” he said to me over both email and in a phone conversation. “To achieve this goal, I am willing to stay heads-down and hardworking every day until it is done, and it does not matter whether it will take me 15 years or 50 years.”

I don’t doubt that he means it. I’ll note that we had this call in the middle of the night his time in California, even after I asked multiple times if there wasn’t a more reasonable hour in the daytime for him to talk. (He insisted that he got his best work done at 4:30 a.m., a result of how a lot of the grocery business works.) Xu on the one hand is very gentle with a calm demeanor, but don’t let his quiet manner fool you. He also is focused and relentless in his work ethic.

When people talk today about buying food, alongside traditional grocery stores and other physical food markets, they increasingly talk about grocery delivery companies, restaurant delivery platforms, meal kit services and more that make or provide food to people by way of apps. GrubMarket has built itself as a profitable but quiet giant that underpins the fuel that helps companies in all of these categories by becoming one of the critical companies building bridges between food producers and those that interact with customers.

Its opportunity comes in the form of disruption and a gap in the market. Food production is not unlike shipping and other older, non-tech industries, with a lot of transactions couched in legacy processes: GrubMarket has built software that connects the different segments of the food supply chain in a faster and more efficient way, and then provides the logistics to help it run.

To be sure, it’s an area that would have evolved regardless of the world health situation, but the rise and growth of the coronavirus has definitely “helped” GrubMarket not just by creating more demand for delivered food, but by providing a way for those in the food supply chain to interact with less contact and more tech-fueled efficiency.

Sales of WholesaleWare, as the platform is called, Xu said, have seen more than 800% growth over the last year, now managing “several hundreds of millions of dollars of food wholesale activities” annually.

Underpinning its tech is the sheer size of the operation: economies of scale in action. The company is active in the San Francisco Bay Area, Los Angeles, San Diego, Seattle, Texas, Michigan, Boston and New York (and many places in between) and says that it currently operates some 21 warehouses nationwide. Xu describes GrubMarket as a “major food provider” in the Bay Area and the rest of California, with (as one example) more than 5 million pounds of frozen meat in its east San Francisco Bay warehouse.

Its customers include more than 500 grocery stores, 8,000 restaurants and 2,000 corporate offices, with familiar names like Whole Foods, Kroger, Albertson, Safeway, Sprouts Farmers Market, Raley’s Market, 99 Ranch Market, Blue Apron, Hello Fresh, Fresh Direct, Imperfect Foods, Misfit Market, Sun Basket and GoodEggs all on the list, with GrubMarket supplying them items that they resell directly, or use in creating their own products (like meal kits).

While much of GrubMarket’s growth has been — like a lot of its produce — organic, its profitability has helped it also grow inorganically. It has made some 15 acquisitions in the last two years, including Boston Organics and EJ Food Distributor this year.

It’s not to say that GrubMarket has not had growing pains. The company, Xu said, was like many others in the food delivery business — “overwhelmed” at the start of the pandemic in March and April of this year. “We had to limit our daily delivery volume in some regions, and put new customers on waiting lists.” Even so, the B2C business grew between 300% and 500% depending on the market. Xu said things calmed down by May and even as some B2B customers never came back after cities were locked down, as a category, B2B has largely recovered, he said.

Interestingly, the startup itself has taken a very proactive approach in order to limit its own workers’ and customers’ exposure to COVID-19, doing as much testing as it could — tests have been, as we all know, in very short supply — as well as a lot of social distancing and cleaning operations.

“There have been no mandates about masks, but we supplied them extensively,” he said.

So far it seems to have worked. Xu said the company has only found “a couple of employees” that were positive this year. In one case in April, a case was found not through a test (which it didn’t have, this happened in Michigan) but through a routine check and finding an employee showing symptoms, and its response was swift: the facilities were locked down for two weeks and sanitized, despite this happening in one of the busiest months in the history of the company (and the food supply sector overall).

That’s notable leadership at a time when it feels like a lot of leaders have failed us, which only helps to bolster the company’s strong growth.

Frame AI, a New York City startup that uses artificial intelligence and machine learning to help companies understand their customers better across multiple channels, announced a $6.3 million Series A investment today.

G20 Ventures and Greycroft led the round together. Bill Wiberg, co-founder and partner at G20, will join Frame’s board under the terms of the deal. The total raised with an earlier seed round is over $10 million, according to the company.

“Frame is basically an early warning system and continuous monitoring tool for your customer voice,” Frame CEO and co-founder George Davis told TechCrunch . What that means, in practice, is the tool plugs into help desk software, call center tooling, CRM systems and anywhere else in a company that communicates with a customer.

“We then use natural language understanding to pull out emerging themes and basically aggregate them to account and segment levels so that customer experience leaders can prioritize taking actions to improve their relationships,” Davis explained.

He believes that customer experience leaders are being asked to do more and more in terms of talking to customers on ever more channels and digesting that into useful information for the rest of their company to be responsive to customer needs, and he says that there isn’t a lot of tooling to help with this particular part of the customer experience problem.

“We don’t think they have the right tools to do either the listening in the first place or the analysis. We’re trying to make it possible for them to hear their customers everywhere they’re already talking to them, and then act on that information,” he said.

He says they work alongside customer data platforms (CDPs) like Segment, Salesforce Customer 360 and Adobe Real-time CDP. “We can take the customer voice information from all of these unstructured sources, all these natural language sources and turn it into moments that can be contributed back to one of these structured data platforms.”

Davis certainly recognizes that his company is getting this money in the middle of a health and economic crisis, and he hopes that a tool like his that can help take the pulse of the customer across multiple channels can help companies succeed at a time when a data-driven approach to customer experience is more important than ever.

He says that by continuing to hire through this and building his company, he can contribute to restarting the economic engine, even if in some small way.

“It’s a bleak time, but I have a lot of confidence in New York and in the country, in the customer experience community and in the world’s ability to bounce back strong from this. I think it’s actually created a lot of solidarity that we’re all going to find a lot of new opportunities, and we’re going to just keep building Frame as fast as we can.”

Microsoft today announced the launch of Azure Edge Zones, which will allow Azure users to bring their applications to the company’s edge locations. The focus here is on enabling real-time low-latency 5G applications. The company is also launching a version of Edge Zones with carriers (starting with AT&T) in preview, which connects these zones directly to 5G networks in the carrier’s data center. And to round it all out, Azure is also getting Private Edge Zones for those who are deploying private 5G/LTE networks in combination with Azure Stack Edge.

In addition to partnering with carriers like AT&T, as well as Rogers, SK Telecom, Telstra and Vodafone, Microsoft is also launching new standalone Azure Edge Zones in more than 10 cities over the next year, starting with LA, Miami and New York later this summer.

“For the last few decades, carriers and operators have pioneered how we connect with each other, laying the foundation for telephony and cellular,” the company notes in today’s announcement. “With cloud and 5G, there are new possibilities by combining cloud services, like compute and AI with high bandwidth and ultra-low latency. Microsoft is partnering with them bring 5G to life in immersive applications built by organization and developers.”

This may all sound a bit familiar, and that’s because only a few weeks ago, Google launched Anthos for Telecom and its Global Mobile Edge Cloud, which at first glance offers a similar promise of bringing applications close to that cloud’s edge locations for 5G and telco usage. Microsoft argues that its offering is more comprehensive in terms of its partner ecosystem and geographic availability. But it’s clear that 5G is a trend all of the large cloud providers are trying to tap into. Microsoft’s own acquisition of 5G cloud specialist Affirmed Networks is yet another example of how it is looking to position itself in this market.

As far as the details of the various Edge Zone versions go, the focus of Edge Zones is mostly on IoT and AI workloads, while Microsoft notes that Edge Zones with Carriers is more about low-latency online gaming, remote meetings and events, as well as smart infrastructure. Private Edge Zones, which combine private carrier networks with Azure Stack Edge, is something only a small number of large enterprise companies would likely to look into, given the cost and complexity of rolling out a system like this.

If you’ve ever entered a company’s office as a visitor or contractor, you probably know the routine: check in with a receptionist, figure out who invited you, print out a badge and get on your merry way. Brussels, Belgium and New York-based Proxyclick aims to streamline this process, while also helping businesses keep their people and assets secure. As the company announced today, it has raised a $15 million Series B round led by Five Elms Capital, together with previous investor Join Capital.

In total, Proxyclick says its systems have now been used to register more than 30 million visitors in 7,000 locations around the world. In the U.K. alone, more than 1,000 locations use the company’s tools. Current customers include L’Oréal, Vodafone, Revolut, PepsiCo and Airbnb, as well as a number of other Fortune 500 firms.

Gregory Blondeau, founder and CEO of Proxyclick, stresses that the company believes that paper logbooks, which are still in use in many companies, are simply not an acceptable solution anymore, not in the least because that record is often permanent and visible to other visitors.

Proxyclick’s founding team.

“We all agree it is not acceptable to have those paper logbooks at the entrance where everyone can see previous visitors,” he said. “It is also not normal for companies to store visitors’ digital data indefinitely. We already propose automatic data deletion in order to respect visitor privacy. In a few weeks, we’ll enable companies to delete sensitive data such as visitor photos sooner than other data. Security should not be an excuse to exploit or hold visitor data longer than required.”

What also makes Proxyclick stand out from similar solutions is that it integrates with a lot of existing systems for access control (including C-Cure and Lenel systems). With that, users can ensure that a visitor only has access to specific parts of a building, too.

In addition, though, it also supports existing meeting rooms, calendaring and parking systems, and integrates with Wi-Fi credentialing tools so your visitors don’t have to keep asking for the password to get online.

Like similar systems, Proxyclick provides businesses with a tablet-based sign-in service that also allows them to get consent and NDA signatures right during the sign-in process. If necessary, the system also can compare the photos it takes to print out badges with those on a government-issued ID to ensure your visitors are who they say they are.

Blondeau noted that the whole industry is changing, too. “Visitor management is becoming mainstream, it is transitioning from a local, office-related subject handled by facility managers to a global, security and privacy-driven priority handled by chief information security officers. Scope, decision drivers and key people involved are not the same as in the early days,” he said.

It’s no surprise then that the company plans to use the new funding to accelerate its roadmap. Specifically, it’s looking to integrate its solution with more third-party systems with a focus on physical security features and facial recognition, as well as additional new enterprise features.

Cisco today announced that it has acquired Exablaze, an Australia-based company that designs and builds advanced networking gear based on field programmable gate arrays (FPGAs). The company focuses on solutions for businesses that need ultra-low latency networking, with a special emphasis on high-frequency trading. Cisco plans to integrate Exablaze’s technology into its own product portfolio.

“By adding Exablaze’s segment leading ultra-low latency devices and FPGA-based applications to our portfolio, financial and HFT customers will be better positioned to achieve their business objectives and deliver on their customer value proposition,” writes Cisco’s head of corporate development Rob Salvagno.

Founded in 2013, Exablaze has offices in Sydney, New York, London and Shanghai. While financial trading is an obvious application for its solutions, the company also notes that it has users in the big data analytics, high-performance computing and telecom space.

Cisco plans to add Exablaze to its Nexus portfolio of data center switches. The company also argues that in addition to integrating Exablaze’s current portfolio, the two companies will work on next-generation switches, with an emphasis on creating opportunities for expanding its solutions into AI and ML segments.

“The acquisition will bring together Cisco’s global reach, extensive sales and support teams, and broad technology and manufacturing base, with Exablaze’s cutting-edge low-latency networking, layer 1 switching, timing and time synchronization technologies, and low-latency FPGA expertise,” explains Exablaze co-founder and chairman Greg Robinson.

Cisco, which has always been quite acquisitive, has now made six acquisitions this year. Most of these were software companies, but with Acacia Communications, it also recently announced its intention to acquire another fabless semiconductor company that builds optical interconnects.

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…

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.

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.

Slack wants to be the new operating system for teams, something it has made clear on more than one occasion, including in its recent S-1 filing. To accomplish that goal, it put together an in-house $80 million venture fund in 2015 to invest in third-party developers building on top of its platform.

Weeks ahead of its direct listing on The New York Stock Exchange, it continues to put that money to work.

Troops is the latest to land additional capital from the enterprise giant. The New York-based startup helps sales teams communicate with a customer relationship management tool plugged directly into Slack. In short, it automates routine sales management activities and creates visibility into important deals through integrations with employee emails and Salesforce.

Troops founder and chief executive officer Dan Reich, who previously co-founded TULA Skincare, told TechCrunch he opted to build a Slackbot rather than create an independent platform because Slack is a rocket ship and he wanted a seat on board: “When you think about where Slack will go in the future, it’s obvious to us that companies all over the world will be using it,” he said.

Troops has raised $12 million in Series B funding in a round led by Aspect Ventures, with participation from the Slack Fund, First Round Capital, Felicis Ventures, Susa Ventures, Chicago Ventures, Hone Capital, InVision founder Clark Valberg and others. The round brings Troops’ total raised to $22 million.

Launched in 2015 by New York tech veterans Reich, Scott Britton and Greg Ratner, the trio weren’t initially sure of Slack’s growth trajectory. It wasn’t until Slack confirmed its intent to support the developer ecosystem with a suite of developer tools and a fund that the team focused its efforts on building a Slackbot.

“People sometimes thought of us, at least in the early days, as a little bit crazy,” Reich said. “But now Slack is the fastest-growing SaaS company ever.”

“We think the biggest opportunity in the [enterprise SaaS] category is going to be tools oriented around the customer-facing employee (CRM), and that’s where we are innovating,” he added.

Troops’ tools are helpful for any customer-facing team, Reich explains. Envoy, WeWork, HubSpot and a few hundred others are monthly paying subscribers of the tool, using it to interact with their CRM in a messaging interface and to receive notifications when a deal has closed. Troops integrates with Salesforce, so employees can use it to search records, schedule automatic reports and celebrate company wins.

Slack, in partnership with a number of venture capital funds, including Accel, Kleiner Perkins and Index, has also deployed capital to a number of other startups, like Lattice, Drafted and Loom.

With Slack’s direct listing afoot, the Troops team is counting on the imminent and long-term growth of the company’s platform.

“We think it’s still early days,” Reich said. “In the future, we see every company using something like Troops to manage their day-to-day.”

The heated debate around Amazon’s recently announced Long Island City “HQ2” is showing no signs of cooling down.

On Monday morning, the Retail, Wholesale and Department Store Union (RWDSU) hosted a briefing in which labor officials, economic development analysts, Amazon employees and elected New York State and City representatives further underlined concerns around the HQ2 process, the awarded incentives, and the potential impacts Amazon’s presence would have on city workers and residents.

While many of the arguments posed at the Summit weren’t necessarily new, the wide variety of stakeholders that showed up to express concern looked to contextualize the far-reaching risks associated with the deal.

The day began with representatives from New York union groups recounting Amazon’s shaky history with employee working conditions and questioning how the city’s working standards will be impacted if the 50,000 promised jobs do actually show up.

Two current employees working in an existing Amazon New York City warehouse in Staten Island provided poignant examples of improper factory conditions and promised employee benefits that never came to fruition. According to the workers, Amazon has yet to follow through on shuttle services and ride-sharing services that were promised to ease worker commutes, forcing the workers to resort to overcrowded and unreliable public transportation. One of the workers detailed that with his now four-hour commute to get to and from work, coupled with his meaningfully long shifts, he’s been unable to see his daughter for weeks.

Various economic development groups and elected officials including, New York City Comptroller Scott Stringer, City Council Speaker Corey Johnson, City Council Member Jimmy Van Bramer, and New York State Senator Mike Gianaris supported the labor arguments with spirited teardowns of the economic terms of the deal.

Like many critics of the HQ2 process, the speakers’ expressed their beliefs that Amazon knew where it wanted to bring its second quarters throughout the entirety of its auction process, given the talent pool and resources in the chosen locations, and that the entire undertaking was meant to squeeze out the best economic terms possible. And according to City Council Speaker Johnson, New York City “got played”.

Comptroller Stringer argued that Amazon is taking advantage of New York’s Relocation and Employment Assistance Program (REAP) and Industrial and Commercial Abatement Program (ICAP), which Stringer described as outdated and in need of reform, to receive the majority of the $2 billion-plus in promised economic incentives that made it the fourth largest corporate incentive deal in US history.

The speakers continued to argue that the unprecedented level of incentives will be nearly impossible to recoup and that New York will also face economic damages from lower sales tax revenue as improved Amazon service in the city cannibalizes local brick & mortar retail.

Fears over how Amazon’s presence will impact the future of New York were given more credibility with the presence of Seattle City Council members Lisa Herbold & Teresa Mosqueda, who had flown to New York from Seattle to discuss lessons learned from having Amazon’s Headquarters in the city and to warn the city about the negative externalities that have come with it.

Herbold and Mosqueda focused less on an outright rejection of the deal but instead emphasized that New York was in a position to negotiate for better terms focused on equality and corporate social responsibility, which could help the city avoid the socioeconomic turnover that has plagued Seattle and could create a new standard for public-private partnerships.

While the New York City Council noted it was looking into legal avenues, the opposition seemed to have limited leverage to push back or meaningfully negotiate the deal. According to state officials, the most clear path to fight the deal would be through votes by the state legislature and through the state Public Authorities Control Board who has to unanimously approve the subsidy package.

With the significant turnout seen at Monday’s summit, which included several high-ranking state and city officials, it seems clear that we’re still in the early innings of what’s likely to be a long battle ahead to close the HQ2 deal.

Amazon did not return requests for immediate comment.