Mar
26
2020
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Kaizo raises $3M for its AI-based tools to improve customer service support teams

CRM has for years been primarily a story of software to manage customer contacts, data to help agents do their jobs, and tools to manage incoming requests and outreach strategies. Now to add to that we’re starting to see a new theme: apps to help agents track how they work and to work better.

Today comes the latest startup in that category, a Dutch company called Kaizo, which uses AI and gamification to provide feedback on agents’ work, tips on what to do differently, and tools to set and work to goals — all of which can be used remotely, in the cloud. Today, it is announcing $3 million in a seed round of funding co-led by Gradient — Google’s AI venture fund — and French VC Partech. 

And along with the seed round, Kaizo (which rebranded last week from its former name, Ticketless) is announcing that Christoph Auer-Welsbach, a former partner at IBM Ventures, is joining the company as a co-founder, alongside founder Dominik Blattner. 

Although this is just a seed round, it’s coming after a period of strong growth for the company. Kaizo has already 500 companies including Truecaller, SimpleSurance, Miro, CreditRepairCloud, Justpark, Festicket and Nmbrs are using its software, covering “thousands” of customer support agents, which use a mixture of free and paid tools that integrate with established CRM software from the likes of Salesforce, Zendesk and more.

Customer service, and the idea of gamifying it to motivate employees, might feel like the last thing on people’s minds at the moment, but it is actually timely and relevant to our current state in responding to and living with the coronavirus.

People are spending much more time at home, and are turning to the internet and remote services to get what they need, and in many cases are finding that their best-laid plans are now in freefall. Both of these are driving a lot of traffic to sites and primarily customer support centers, which are getting overwhelmed with people reaching out for help.

And that’s before you consider how customer support teams might be impacted by coronavirus and the many mandates we’ve had to stay away from work, and the stresses they may be under.

“In our current social climate, customer support is an integral part of a company’s stability and growth that has embraced remote work to meet the demands of a globalized customer-base,” said Dominik Blattner, founder of Kaizo, in a statement. “With the rise of support teams utilizing a digital workplace, providing standards to measure an agent’s performance has never been more important. KPIs provide these standards, quantifying the success, achievement and contribution of each team member.”

On a more general level, Kaizo is also changing the conversation around how to improve one’s productivity. There has been a larger push for “quantified self” platforms, which has very much played out both in workplaces and in our personal lives, but a lot of services to track performance have focused on both managers and employees leaning in with a lot of input. That means if they don’t set aside the time to do that, the platforms never quite work the way they should.

This is where the AI element of Kaizo plays a key role, by taking on the need to proactively report into a system.

“This is how we’re distinct,” Auer-Welsbach said in an interview. “Normally KPIs are top-down. They are about people setting goals and then reporting they’ve done something. This is a bottom-up approach. We’re not trying to change employees’ behaviour. We plug into whatever environment they are using, and then our tool monitors. The employee doesn’t have to report or measure anything. We track clicks on the CRM, ticketing, and more, and we analyse all that.” He notes that Kaizo is looking at up to 50 datapoints in its analysis.

“We’re excited about Kaizo’s novel approach to applying AI to existing ticket data from platforms like Zendesk and Salesforce to optimize the customer support workflow,” said Darian Shirazi, General Partner at Gradient Ventures, in a statement. “Using machine learning, Kaizo understands which behaviors in customer service tickets lead to better outcomes for customers and then guides agents to replicate that using ongoing game mechanics. Customer support and service platforms today are failing to leverage data in the right way to make the life of agents easier and more effective. The demand Kaizo has seen since they launched on the Zendesk Marketplace shows agents have been waiting for such a solution for some time.”

Kaizo is not the only startup to have identified the area of building new services to improve the performance of customer support teams. Assembled earlier this month also raised $3.1 million led by Stripe for what it describes as the “operating system” for customer support.

Jan
28
2020
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RealityEngines launches its autonomous AI service

RealityEngines.AI, an AI and machine learning startup founded by a number of former Google executives and engineers, is coming out of stealth today and announcing its first set of products.

When the company first announced its $5.25 million seed round last year, CEO Bindu Reddy wasn’t quite ready to disclose RealityEngines’ mission beyond saying that it planned to make machine learning easier for enterprises. With today’s launch, the team is putting this into practice by launching a set of tools that specifically tackle a number of standard enterprise use cases for ML, including user churn predictions, fraud detection, sales lead forecasting, security threat detection and cloud spend optimization. For use cases that don’t fit neatly into these buckets, the service also offers a more general predictive modeling service.

Before co-founding RealiyEngines, Reddy was the head of product for Google Apps and general manager for AI verticals at AWS. Her co-founders are Arvind Sundararajan (formerly at Google and Uber) and Siddartha Naidu (who founded BigQuery at Google). Investors in the company include Eric Schmidt, Ram Shriram, Khosla Ventures and Paul Buchheit.

As Reddy noted, the idea behind this first set of products from RealityEngines is to give businesses an easy entry into machine learning, even if they don’t have data scientists on staff.

Besides talent, another issue that businesses often face is that they don’t always have massive amounts of data to train their networks effectively. That has long been a roadblock for many companies that want to see what AI can do for them but that didn’t have the right resources to do so. RealityEngines overcomes this by creating realistic synthetic data that it can then use to augment a company’s existing data. In its tests, this creates models that are up to 15% more accurate than models that were trained without the synthetic data.

“The most prominent use of generative adversarial networks — GANS — has been to create deepfakes,” said Reddy. “Deepfakes have captured the public’s imagination by highlighting how easy it to spread misinformation with these doctored videos and images. However, GANS can also be applied to productive and good use. They can be used to create synthetic data sets which when then be combined with the original data, to produce robust AI models even when a business doesn’t have much training data.”

RealityEngines currently has about 20 employees, most of whom have a deep background in ML/AI, both as researchers and practitioners.

Aug
12
2019
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Lucidworks raises $100M to expand in AI-powered search-as-a-service for organizations

If the sheer amount of information that we can tap into using the internet has made the world our oyster, then the huge success of Google is a testament to how lucrative search can be in helping to light the way through that data maze.

Now, in a sign of the times, a startup called Lucidworks, which has built an AI-based engine to help individual organizations provide personalised search services for their own users, has raised $100 million in funding. Lucidworks believes its approach can produce better and more relevant results than other search services in the market, and it plans to use the funding for its next stage of growth to become, in the words of CEO Will Hayes, “the world’s next important platform.”

The funding is coming from PE firm Francisco Partners? and ?TPG Sixth Street Partners?. Existing investors in the company include Top Tier Capital Partners, Shasta Ventures, Granite Ventures and Allegis Cyber.

Lucidworks has raised around $200 million in funding to date, and while it is not disclosing the valuation, the company says it has been doubling revenues each year for the last three and counts companies like Reddit, Red Hat, REI and the U.S. Census among some 400 others of its customers using its flagship product, Fusion. PitchBook notes that its last round in 2018 was at a modest $135 million, and my guess is that is up by quite some way.

The idea of building a business on search, of course, is not at all new, and Lucidworks works is in a very crowded field. The likes of Amazon, Google and Microsoft have built entire empires on search — in Google’s and Microsoft’s case, by selling ads against those search results; in Amazon’s case, by generating sales of items in the search results — and they have subsequently productised that technology, selling it as a service to others.

Alongside that are companies that have been building search-as-a-service from the ground up — like Elastic, Sumo Logic and Splunk (whose founding team, coincidentally, went on to found Lucidworks…) — both for back-office processes as well as for services that are customer-facing.

In an interview, Hayes said that what sets Lucidworks apart is how it uses machine learning and other AI processes to personalise those results after “sorting through mountains of data,” to provide enterprise information to knowledge workers, shopping results on an e-commerce site to consumers, data to wealth managers or whatever it is that is being sought.

Take the case of a shopping experience, he said by way of explanation. “If I’m on REI to buy hiking shoes, I don’t just want to see the highest-rated hiking shoes, or the most expensive,” he said.

The idea is that Lucidworks builds algorithms that bring in other data sources — your past shopping patterns, your location, what kind of walking you might be doing, what other people like you have purchased — to produce a more focused list of products that you are more likely to buy.

“Amazon has no taste,” he concluded, a little playfully.

Today, around half of Lucidworks’ business comes from digital commerce and digital content — searches of the kind described above for products, or monitoring customer search queries sites like Red Hat or Reddit — and half comes from knowledge worker applications inside organizations.

The plan will be to continue that proportion, while also adding other kinds of features — more natural language processing and more semantic search features — to expand the kinds of queries that can be made, and also cues that Fusion can use to produce results.

Interestingly, Hayes said that while it’s come up a number of times, Lucidworks doesn’t see itself ever going head-to-head with a company like Google or Amazon in providing a first-party search platform of its own. Indeed, that may be an area that has, for the time being at least, already been played out. Or it may be that we have turned to a time when walled gardens — or at least more targeted and curated experiences — are coming into their own.

“We still see a lot of runway in this market,” said Jonathan Murphy of Francisco Partners. “We were very attracted to the idea of next-generation search, on one hand serving internet users facing the pain of the broader internet, and on the other enterprises as an enterprise software product.” 

Lucidworks, it seems, has also entertained acquisition approaches, although Hayes declined to get specific about that. The longer-term goal, he said, “is to build something special that will stay here for a long time. The likelihood of needing that to be a public company is very high, but we will do what we think is best for the company and investors in the long run. But our focus and intention is to continue growing.”

Jun
12
2019
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RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises

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

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

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

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

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

Mar
13
2019
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Automation Hero picks up $14.5 million led by Atomico

Automation Hero, formerly SalesHero, has secured $14.5 million in new funding led by Atomico, with participation by Baidu Ventures and Cherry Ventures. As part of the deal, Atomico principal Ben Blume will join the company’s board of directors.

The automation startup launched in 2017 as SalesHero, giving sales orgs a simple way to automate back-office processes like filing an expense report or updating the CRM. It does this through an AI assistant called Robin — “Batman and Robin, it worked with the superhero theme, and it’s gender neutral,” co-founder and CEO Stefan Groschupf explained — that can be configured to go through the regular workflow and take care of repetitive tasks.

“We brought computers into the workplace because we believed they could make us more productive,” said Groschupf. “But in many companies, people spend a lot of time entering data and doing painful manual processes to make these machines happy.”

The idea was to give salespeople more time to actually do their job, which is selling to clients. If all the administrative and repetitive “paperwork” is done by a computer, human employees can become more productive and efficient at skilled tasks.

By weaving together click robots, Automation Hero users can build out their own workflows through a no-code interface, tying together a wide variety of both structured and unstructured data sources. Those workflows are then presented in the inbox each morning by Robin, the AI assistant, and are executed as soon as the user gives the go-ahead.

After launch, the team realized that other types of organizations, beyond sales departments, were building out automations. Insurance firms, in particular, were using the software to automate some of the repetitive tasks involved with filing and assessing claims.

This led to today’s rebrand to Automation Hero.

Groschupf said that by automating the process of filling out a single closing form, it saved one insurance firm’s 430 sales reps 18.46 years per year.

Automation Hero has now raised a total of $19 million.

“We’re really excited with Atomico to bring on a great VC and good people,” said Groschupf. “I’ve raised capital before and I’ve worked with some of the more questionable VCs, as it turns out. We’re super-excited we’ve found an investor that really bakes important things, like a diversity policy and a family leave policy, right into the company’s investment agreement.”

Though he didn’t confirm, it’s likely that Groschupf is referring to KPCB, which has run into its fair share of controversy over the past few years and was an investor in Groschupf’s previous startup, Datameer.

Feb
05
2019
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Databricks raises $250M at a $2.75B valuation for its analytics platform

Databricks, the company founded by the original team behind the Apache Spark big data analytics engine, today announced that it has raised a $250 million Series E round led by Andreessen Horowitz. Coatue Management, Green Bay Ventures, Microsoft and NEA, also participated in this round, which brings the company’s total funding to $498.5 million. Microsoft’s involvement here is probably a bit of a surprise, but it’s worth noting that it also worked with Databricks on the launch of Azure Databricks as a first-party service on the platform, something that’s still a rarity in the Azure cloud.

As Databricks also today announced, its annual recurring revenue now exceeds $100 million. The company didn’t share whether it’s cash flow-positive at this point, but Databricks CEO and co-founder Ali Ghodsi shared that the company’s valuation is now $2.75 billion.

Current customers, which the company says number around 2,000, include the likes of Nielsen, Hotels.com, Overstock, Bechtel, Shell and HP.

“What Ali and the Databricks team have built is truly phenomenal,” Green Bay Ventures co-founder Anthony Schiller told me. “Their success is a testament to product innovation at the highest level. Databricks is without question best-in-class and their impact on the industry proves it. We were thrilled to participate in this round.”

While Databricks is obviously known for its contributions to Apache Spark, the company itself monetizes that work by offering its Unified Analytics platform on top of it. This platform allows enterprises to build their data pipelines across data storage systems and prepare data sets for data scientists and engineers. To do this, Databricks offers shared notebooks and tools for building, managing and monitoring data pipelines, and then uses that data to build machine learning models, for example. Indeed, training and deploying these models is one of the company’s focus areas these days, which makes sense, given that this is one of the main use cases for big data, after all.

On top of that, Databricks also offers a fully managed service for hosting all of these tools.

“Databricks is the clear winner in the big data platform race,” said Ben Horowitz, co-founder and general partner at Andreessen Horowitz, in today’s announcement. “In addition, they have created a new category atop their world-beating Apache Spark platform called Unified Analytics that is growing even faster. As a result, we are thrilled to invest in this round.”

Ghodsi told me that Horowitz was also instrumental in getting the company to re-focus on growth. The company was already growing fast, of course, but Horowitz asked him why Databricks wasn’t growing faster. Unsurprisingly, given that it’s an enterprise company, that means aggressively hiring a larger sales force — and that’s costly. Hence the company’s need to raise at this point.

As Ghodsi told me, one of the areas the company wants to focus on is the Asia Pacific region, where overall cloud usage is growing fast. The other area the company is focusing on is support for more verticals like mass media and entertainment, federal agencies and fintech firms, which also comes with its own cost, given that the experts there don’t come cheap.

Ghodsi likes to call this “boring AI,” since it’s not as exciting as self-driving cars. In his view, though, the enterprise companies that don’t start using machine learning now will inevitably be left behind in the long run. “If you don’t get there, there’ll be no place for you in the next 20 years,” he said.

Engineering, of course, will also get a chunk of this new funding, with an emphasis on relatively new products like MLFlow and Delta, two tools Databricks recently developed and that make it easier to manage the life cycle of machine learning models and build the necessary data pipelines to feed them.

Jan
17
2019
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Former Facebook engineer picks up $15M for AI platform Spell

In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former director of Engineering for Facebook AI Research had every intention to keep working on AI, but quickly realized a huge issue.

Unless you’re under the umbrella of one of these big tech companies like Facebook, it can be very difficult and incredibly expensive to get your hands on the hardware necessary to run machine learning experiments.

So he built Spell, which today received $15 million in Series A funding led by Eclipse Ventures and Two Sigma Ventures.

Spell is a collaborative platform that lets anyone run machine learning experiments. The company connects clients with the best, newest hardware hosted by Google, AWS and Microsoft Azure and gives them the software interface they need to run, collaborate and build with AI.

“We spent decades getting to a laptop powerful enough to develop a mobile app or a website, but we’re struggling with things we develop in AI that we haven’t struggled with since the 70s,” said Piantino. “Before PCs existed, the computers filled the whole room at a university or NASA and people used terminals to log into a single main frame. It’s why Unix was invented, and that’s kind of what AI needs right now.”

In a meeting with Piantino this week, TechCrunch got a peek at the product. First, Piantino pulled out his MacBook and opened up Terminal. He began to run his own code against MNIST, which is a database of handwritten digits commonly used to train image detection algorithms.

He started the program and then moved over to the Spell platform. While the original program was just getting started, Spell’s cloud computing platform had completed the test in less than a minute.

The advantage here is obvious. Engineers who want to work on AI, either on their own or for a company, have a huge task in front of them. They essentially have to build their own computer, complete with the high-powered GPUs necessary to run their tests.

With Spell, the newest GPUs from Nvidia and Google are virtually available for anyone to run their tests.

Individual users can get on for free, specify the type of GPU they need to compute their experiment and simply let it run. Corporate users, on the other hand, are able to view the runs taking place on Spell and compare experiments, allowing users to collaborate on their projects from within the platform.

Enterprise clients can set up their own cluster, and keep all of their programs private on the Spell platform, rather than running tests on the public cluster.

Spell also offers enterprise customers a “spell hyper” command that offers built-in support for hyperparameter optimization. Folks can track their models and results and deploy them to Kubernetes/Kubeflow in a single click.

But perhaps most importantly, Spell allows an organization to instantly transform their model into an API that can be used more broadly throughout the organization, or used directly within an app or website.

The implications here are huge. Small companies and startups looking to get into AI now have a much lower barrier to entry, whereas large traditional companies can build out their own proprietary machine learning algorithms for use within the organization without an outrageous upfront investment.

Individual users can get on the platform for free, whereas enterprise clients can get started for $99/month per host you use over the course of a month. Piantino explains that Spell charges based on concurrent usage, so if the customer has 10 concurrent things running, the company considers that the “size” of the Spell cluster and charges based on that.

Piantino sees Spell’s model as the key to defensibility. Whereas many cloud platforms try to lock customers in to their entire suite of products, Spell works with any language framework and lets users plug and play on the platforms of their choice by simply commodifying the hardware. In fact, Spell doesn’t even share with clients which cloud cluster (Microsoft Azure, Google or AWS) they’re on.

So, on the one hand the speed of the tests themselves goes up based on access to new hardware, but, because Spell is an agnostic platform, there is also a huge advantage in how quickly one can get set up and start working.

The company plans to use the funding to further grow the team and the product, and Piantino says he has his eye out for top-tier engineering talent, as well as a designer.

Dec
13
2018
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Chorus.ai rings up $33M for its platform that analyses sales calls to close more deals

Chorus.ai, a service that listens to sales calls in real time, and then transcribes and analyses them to give helpful tips to the salesperson, has raised $33 million to double down on the current demand for more AI-based tools in the enterprise.

The Series B is being led by Georgian Partners, with participation also from Redpoint Ventures and Emergence Capital, previous investors that backed Israeli-founded, SF-based Chorus.ai in its $16 million Series A two years ago.

In the gap between then and now, the startup has seen strong growth, listening in to some 5 million calls, and performing hundreds of thousands of hours of transcriptions for around 200 customers, including Adobe, Zoom, and Outreach (among others that it will not name).

Micha Breakstone, the co-founder (who has a pretty long history in conversational AI, heading up R&D at Ginger Software and then Intel after it acquired the startup; and before that building the tech that eventually became Summly and got acquired by Yahoo, among other roles), says that while the platform gives information and updates to salespeople in real time, much of the focus today is on providing information to users post-conversation, based on both audio and video calls.

One of its big areas is “smart themes” — patterns and rules Chorus has learned through all those calls. For example, it has identified what kind of language the most successful sales people are using and in turn prompts those who are less successful to use it more. Two general tips Breakstone told me about: using more collaborative terms like we and us; and giving more backstory to clients, although there will be more specific themes and approaches based on Chorus’s specific customers and products.

“I’d say we are super attuned to our customers and what they need and want,” Breakstone said. Which makes sense given the whole premise of Chorus.

It also creates smart “playlists” for managers who will almost certainly never have the time to review hundreds of hours of calls but might want to hear instructive highlights or ‘red alert’ moments where a more senior person might need to step in to save or close a deal.

There are currently what seems like dozens of startups and larger businesses that are currently tackling the opportunity to provide “conversational intelligence” to sales teams, using advances in natural language processing, voice recognition, machine learning and big data to help turn every sales person into a Jerry Maguire (yes, I know he’s an agent, but still, he needs to close deals, and he’s a salesman). They include TalkIQ (which has now been acquired by Dialpad), People.AI, Gong, Voicera, VoiceOps, and I’m pulling from a long list.

“We were among the very first to start this, no one knew what conversational intelligence was before us,” Breakstone says. He describes most of what was out in the market at the time as “Nineties technology” and adds that “our tech is superior because we built it in the correct way from the ground up, with nothing sent to a third party.”

He says that this is one reason why the company has negative churn — it essentially wins customers and hasn’t lost any. And having the tech all in-house not only means the platform is smarter and more accurate, but that helps with compliance around regulations like GDPR, which also has been a boost to its business. It’s also scored well on metrics around reps hitting targets better with its tools (the company claims its products lead to 50 percent greater quota attainment and ‘ramp time’ up by 30 percent for new sales people who use it).

Chorus.ai has helped us become a smarter sales organization as we’ve scaled. We have visibility into our sales conversations and what is working across all of our offices”, said Greg Holmes, Head of Sales for Zoom Video Communications, in a statement. “We’ve seen a drastic reduction in new hire ramp times and higher sales productivity with even more reps hitting quota. Chorus.ai is a game changer.”

Chorus has raised $55 million to date and Breakstone said he would not disclose its valuation — despite my best attempts to use some of those sales tips to winkle the information out of him. But I understand it to be “significantly higher” than in its last round, and definitely in the hundreds of millions.

As a point of reference, after its Series A two years ago, it was only valued at around $33 million post-money according to PitchBook.

“Maintaining high-quality sales conversations as you scale a sales organization is hard for many companies, but key to delivering predictable revenue growth. Chorus.ai’s Conversation Intelligence platform solves that challenge with a market-leading solution that is easy-to-use and delivers best-in-class results.” said Simon Chong, Managing Partner at Georgian Partners, in a statement. (Chong is joining the board with this round.) “Chorus.ai works with some of the best sales teams in the world and they love the product. We are very excited to partner with Chorus.ai on their next phase of growth as they help world class sales teams reach higher quota attainment and efficiency.”

Dec
11
2018
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TechSee nabs $16M for its customer support solution built on computer vision and AR

Chatbots and other AI-based tools have firmly found footing in the world of customer service, used either to augment or completely replace the role of a human responding to questions and complaints, or (sometimes, annoyingly, at the same time as the previous two functions) sell more products to users.

Today, an Israeli startup called TechSee is announcing $16 million in funding to help build out its own twist on that innovation: an AI-based video service, which uses computer vision, augmented reality and a customer’s own smartphone camera to provide tech support to customers, either alongside assistance from live agents, or as part of a standalone customer service “bot.”

Led by Scale Venture Partners — the storied investor that has been behind some of the bigger enterprise plays of the last several years (including Box, Chef, Cloudhealth, DataStax, Demandbase, DocuSign, ExactTarget, HubSpot, JFrog and fellow Israeli AI assistance startup WalkMe), the Series B also includes participation from Planven Investments, OurCrowd, Comdata Group and Salesforce Ventures. (Salesforce was actually announced as a backer in October.)

The funding will be used both to expand the company’s current business as well as move into new product areas like sales.

Eitan Cohen, the CEO and co-founder, said that the company today provides tools to some 15,000 customer service agents and counts companies like Samsung and Vodafone among its customers across verticals like financial services, tech, telecoms and insurance.

The potential opportunity is big: Cohen estimates there are about 2 million customer service agents in the U.S., and about 14 million globally.

TechSee is not disclosing its valuation. It has raised around $23 million to date.

While TechSee provides support for software and apps, its sweet spot up to now has been providing video-based assistance to customers calling with questions about the long tail of hardware out in the world, used for example in a broadband home Wi-Fi service.

In fact, Cohen said he came up with the idea for the service when his parents phoned him up to help them get their cable service back up, and he found himself challenged to do it without being able to see the set-top box to talk them through what to do.

So he thought about all the how-to videos that are on platforms like YouTube and decided there was an opportunity to harness that in a more organised way for the companies providing an increasing array of kit that may never get the vlogger treatment.

“We are trying to bring that YouTube experience for all hardware,” he said in an interview.

The thinking is that this will become a bigger opportunity over time as more services get digitised, the cost of components continues to come down and everything becomes “hardware.”

“Tech may become more of a commodity, but customer service does not,” he added. “Solutions like ours allow companies to provide low-cost technology without having to hire more people to solve issues [that might arise with it.]”

The product today is sold along two main trajectories: assisting customer reps; and providing unmanned video assistance to replace some of the easier and more common questions that get asked.

In cases where live video support is provided, the customer opts in for the service, similar to how she or he might for a support service that “takes over” the device in question to diagnose and try to fix an issue. Here, the camera for the service becomes a customer’s own phone.

Over time, that live assistance is used in two ways that are directly linked to TechSee’s artificial intelligence play. First, it helps to build up TechSee’s larger back catalogue of videos, where all identifying characteristics are removed with the focus solely on the device or problem in question. Second, the experience in the video is also used to build TechSee’s algorithms for future interactions. Cohen said there are now “millions” of media files — images and videos — in the company’s catalogue.

The effectiveness of its system so far has been pretty impressive. TechSee’s customers — the companies running the customer support — say they have on average seen a 40 percent increase in customer satisfaction (NPS scores), a 17 percent decrease in technician dispatches and between 20 and 30 percent increase in first-call resolutions, depending on the industry.

TechSee is not the only company that has built a video-based customer engagement platform: others include Stryng, CallVU and Vee24. And you could imagine companies like Amazon — which is already dabbling in providing advice to customers based on what its Echo Look can see — might be interested in providing such services to users across the millions of products that it sells, as well as provide that as a service to third parties.

According to Cohen, what TechSee has going for it compared to those startups, and also the potential entry of companies like Microsoft or Amazon into the mix, is a head start on raw data and a vision of how it will be used by the startup’s AI to build the business.

“We believe that anyone who wants to build this would have a challenge making it from scratch,” he said. “This is where we have strong content, millions of images, down to specific model numbers, where we can provide assistance and instructions on the spot.”

Salesforce’s interest in the company, he said, is a natural progression of where that data and customer relationship can take a business beyond responsive support into areas like quick warranty verification (for all those times people have neglected to do a product registration), snapping fender benders for insurance claims and of course upselling to other products and services.

“Salesforce sees the synergies between the sales cloud and the service cloud,” Cohen said.

“TechSee recognized the great potential for combining computer vision AI with augmented reality in customer engagement,” said Andy Vitus, partner at Scale Venture Partners, who joins the board with this round. “Electronic devices become more complex with every generation, making their adoption a perennial challenge. TechSee is solving a massive problem for brands with a technology solution that simplifies the customer experience via visual and interactive guidance.”

Nov
14
2018
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‘Software robot’ startup UiPath expands Series C to $265M at a $3B valuation

UiPath, a startup that works in the growing area of RPA, or robotic process automation — where AI-based software is used to help businesses run repetitive or mundane back-office tasks, to free up humans to tackle more sophisticated work — has raised money for the third time this year. The company is today announcing that it has closed out its Series C at $265 million — $40 million higher than the amount it said it was aiming for two months ago.

UiPath is now disclosing new investors in the round — namely, IVP, Madrona Venture Group and Meritech Capital — plus secondary sales for employees to give them liquidity, which made up the difference. The company has confirmed to me that the transactions were done at the same valuation as the rest of the Series C, at $3 billion. The Series C is still led by CapitalG and Sequoia Capital as before.

For some context, earlier this year, the company also raised a Series B of $153 million at a $1.1 billion valuation.

UiPath’s strong valuation hike and the rapid pace of its funding come at a time when both the company and its rivals are all growing quickly, as enterprises rush to capitalise on the rise of artificial intelligence in the workplace. In the case of RPA, the promise is that it will help bring down the cost of doing business and improve organizations’ efficiency. UiPath’s mantra is to provide “one robot for every person,” essentially doubling a company’s workforce without the need to hire more people.

UiPath says that its current annual run rate is now $150 million, up from a $100 million ARR figure it put out just two months ago, with customers now numbering at 2,100 and including the US Army, Defense Logistics Agency, GSA, IRS, NASA, Navy, and the Department of Veterans Affairs. One source at the company tells me that it’s getting approached “almost daily” for more funding at the moment.

At the same time, the competitive landscape is most definitely heating up. We’ve heard that Automation Anywhere, which also just raised money — $250 million — earlier this year, may also be looking to raise more (we’re looking into it). And just earlier this week, we reported that another RPA player, Kofax, acquired a division of Nuance for $400 million to ramp up its image processing business.

“I am honored to have IVP, Madrona Venture Group and Meritech Capital as new investors in UiPath. Their leadership and guidance will no doubt help us continue to define and lead the Automation First era for customers everywhere. UiPath has had many funding options and I believe we have selected the investors that align best with our culture and beliefs. I am humbled as the syndicate of unquestionably top-tier venture capital firms who believe in UiPath and support our future,” said UiPath CEO and co- founder Daniel Dines said in a statement. “Additionally, it is a core UiPath principle to share the success of the company in a meaningful way with our hard-working and long-time employees and we were excited to be able to extend the opportunity, at their personal choice, to realize partial liquidity in this round.”

Updated with clarification about the employee liquidity sales and new investor names.

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