May
11
2021
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SightCall raises $42M for its AR-based visual assistance platform

Long before COVID-19 precipitated “digital transformation” across the world of work, customer services and support was built to run online and virtually. Yet it too is undergoing an evolution supercharged by technology.

Today, a startup called SightCall, which has built an augmented reality platform to help field service teams, the companies they work for, and their customers carry out technical and mechanical maintenance or repairs more effectively, is announcing $42 million in funding, money that it plans to use to invest in its tech stack with more artificial intelligence tools and expanding its client base.

The core of its service, explained CEO and co-founder Thomas Cottereau, is AR technology (which comes embedded in their apps or the service apps its customers use, with integrations into other standard software used in customer service environments including Microsoft, SAP, Salesforce and ServiceNow). The augmented reality experience overlays additional information, pointers and other tools over the video stream.

This is used by, say, field service engineers coordinating with central offices when servicing equipment; or by manufacturers to provide better assistance to customers in emergencies or situations where something is not working but might be repaired quicker by the customers themselves rather than engineers that have to be called out; or indeed by call centers, aided by AI, to diagnose whatever the problem might be. It’s a big leap ahead for scenarios that previously relied on work orders, hastily drawn diagrams, instruction manuals and voice-based descriptions to progress the work in question.

“We like to say that we break the barriers that exist between a field service organization and its customer,” Cottereau said.

The tech, meanwhile, is unique to SightCall, built over years and designed to be used by way of a basic smartphone, and over even a basic mobile network — essential in cases where reception is bad or the locations are remote. (More on how it works below.)

Originally founded in Paris, France before relocating to San Francisco, SightCall has already built up a sizable business across a pretty wide range of verticals, including insurance, telecoms, transportation, telehealth, manufacturing, utilities and life sciences/medical devices.

SightCall has some 200 big-name enterprise customers on its books, including the likes of Kraft-Heinz, Allianz, GE Healthcare and Lincoln Motor Company, providing services on a B2B basis as well as for teams that are out in the field working for consumer customers, too. After seeing 100% year-over-year growth in annual recurring revenue in 2019 and 2020, SightCall’s CEO says it’s looking like it will hit that rate this year as well, with a goal of $100 million in annual recurring revenue.

The funding is being led by InfraVia, a European private equity firm, with Bpifrance also participating. The valuation of this round is not being disclosed, but I should point out that an investor told me that PitchBook’s estimate of $122 million post-money is not accurate (we’re still digging on this and will update as and when we learn more).

For some further context on this investment, InfraVia invests in a number of industrial businesses, alongside investments in tech companies building services related to them such as recent investments in Jobandtalent, so this is in part a strategic investment. SightCall has raised $67 million to date.

There has been an interesting wave of startups emerging in recent years building out the tech stack used by people working in the front lines and in the field, a shift after years of knowledge workers getting most of the attention from startups building a new generation of apps.

Workiz and Jobber are building platforms for small business tradespeople to book jobs and manage them once they’re on the books; BigChange helps manage bigger fleets; and Hover has built a platform for builders to be able to assess and estimate costs for work by using AI to analyze images captured by their or their would-be customers’ smartphone cameras.

And there is Streem, which I discovered is a close enough competitor to SightCall that they’ve acquired AdWords ads based on SightCall searches in Google. Just ahead of the COVID-19 pandemic breaking wide open, General Catalyst-backed Streem was acquired by Frontdoor to help with the latter’s efforts to build out its home services business, another sign of how all of this is leaping ahead.

What’s interesting in part about SightCall and sets it apart is its technology. Co-founded in 2007 by Cottereau and Antoine Vervoort (currently SVP of product and engineering), the two are long-time telecoms industry vets who had both worked on the technical side of building next-generation networks.

SightCall started life as a company called Weemo that built video chat services that could run on WebRTC-based frameworks, which emerged at a time when we were seeing a wider effort to bring more rich media services into mobile web and SMS apps. For consumers and to a large extent businesses, mobile phone apps that work “over the top” (distributed not by your mobile network carrier but the companies that run your phone’s operating system, and thus partly controlled by them) really took the lead and continue to dominate the market for messaging and innovations in messaging.

After a time, Weemo pivoted and renamed itself as SightCall, focusing on packaging the tech that it built into whichever app (native or mobile web) where one of its enterprise customers wanted the tech to live.

The key to how it works comes by way of how SightCall was built, Cottereau explained. The company has spent 10 years building and optimizing a network across data centers close to where its customers are, which interconnects with Tier 1 telecoms carriers and has a lot of latency in the system to ensure uptime. “We work with companies where this connectivity is mission critical,” he said. “The video solution has to work.”

As he describes it, the hybrid system SightCall has built incorporates its own IP that works both with telecoms hardware and software, resulting in a video service that provides 10 different ways for streaming video and a system that automatically chooses the best in a particular environment, based on where you are, so that even if mobile data or broadband reception don’t work, video streaming will. “Telecoms and software are still very separate worlds,” Cottereau said. “They still don’t speak the same language, and so that is part of our secret sauce, a global roaming mechanism.”

The tech that the startup has built to date not only has given it a firm grounding against others who might be looking to build in this space, but has led to strong traction with customers. The next steps will be to continue building out that technology to tap deeper into the automation that is being adopted across the industries that already use SightCall’s technology.

“SightCall pioneered the market for AR-powered visual assistance, and they’re in the best position to drive the digital transformation of remote service,” said Alban Wyniecki, partner at InfraVia Capital Partners, in a statement. “As a global leader, they can now expand their capabilities, making their interactions more intelligent and also bringing more automation to help humans work at their best.”

“SightCall’s $42M Series B marks the largest funding round yet in this sector, and SightCall emerges as the undisputed leader in capital, R&D resources and partnerships with leading technology companies enabling its solutions to be embedded into complex enterprise IT,” added Antoine Izsak of Bpifrance. “Businesses are looking for solutions like SightCall to enable customer-centricity at a greater scale while augmenting technicians with knowledge and expertise that unlocks efficiencies and drives continuous performance and profit.”

Cottereau said that the company has had a number of acquisition offers over the years — not a surprise when you consider the foundational technology it has built for how to architect video networks across different carriers and data centers that work even in the most unreliable of network environments.

“We want to stay independent, though,” he said. “I see a huge market here, and I want us to continue the story and lead it. Plus, I can see a way where we can stay independent and continue to work with everyone.”

May
05
2021
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Shift Technology raises $220M at a $1B+ valuation to fight insurance fraud with AI

While incumbent insurance providers continue to get disrupted by startups like Lemonade, Alan, Clearcover, Pie and many others applying tech to rethink how to build a business around helping people and companies mitigate against risks with some financial security, one issue that has not disappeared is fraud. Today, a startup out of France is announcing some funding for AI technology that it has built for all insurance providers, old and new, to help them detect and prevent it.

Shift Technology, which provides a set of AI-based SaaS tools to insurance companies to scan and automatically flag fraud scenarios across a range of use cases — they include claims fraud, claims automation, underwriting, subrogation detection and financial crime detection — has raised $220 million, money that it will be using both to expand in the property and casualty insurance market, the area where it is already strong, as well as to expand into health, and to double down on growing its business in the U.S. It also provides fraud detection for the travel insurance sector.

This Series D is being led by Advent International, via Advent Tech, with participation from Avenir and others. Accel, Bessemer Venture Partners, General Catalyst and Iris Capital — who were all part of Shift’s Series C led by Bessemer in 2019 — also participated. With this round, Paris-and-Boston-based Shift Technology has now raised some $320 million and has confirmed that it is now valued at over $1 billion.

The company currently has around 100 customers across 25 different countries — with the list including Generali France and Mitsui Sumitomo, to give you an idea of where it’s pitching its business — and says that it has already analyzed nearly two billion claims, data that’s feeding its machine learning algorithms to improve how they work.

The challenge (or I suppose, opportunity) that Shift is tackling, however, is much bigger. The Coalition Against Insurance Fraud, a nonprofit in the U.S., estimates that at least $80 billion of fraudulent claims are made annually in the U.S. alone, but the figure is likely significantly higher. One problem has, ironically, been the move to more virtualized processes, which open the door to malicious actors exploiting loopholes in claims filing and fudging information. Another is the fact that insurance has grown as a market, but so too has the amount of people who are in financial straights, leading to more desperate and illegal acts to gain an edge.

Shift is also not alone in tackling this issue: the market for insurance fraud detection technology globally was estimated to be worth $2.5 billion in 2019 and projected to be worth as much as $8 billion by 2024.

In addition to others in claims management tech such as Brightcore and Guidewire, many of the wave of insurtech startups are building in their own in-house AI-based fraud protection, and it’s very likely that we’ll see a rise of other fraud protection services, built out of adjacent areas like fintech to guard against financial crime, making their way to insurance. As many a fintech entrepreneur has said to me in the past, the mechanics of how the two verticals work and the compliance issues both face are very closely aligned.

“The entire Shift team has worked tirelessly to build this company and provide insurers with the technology solutions they need to empower employees to best be there for their policyholders. We are thrilled to partner with Advent International, given their considerable sector expertise and global reach and are taking another giant step forward with this latest investment,” stated Jeremy Jawish, CEO and co-founder, Shift Technology, in a statement. “We have only just scratched the surface of what is possible when AI-based decision automation and optimization is applied to the critical processes that drive the insurance policy lifecycle.”

For its backers, one key point with Shift is that it’s helping older providers bring on more tools and services that can help them improve their margins as well as better compete against the technology built by newer players.

“Since its founding in 2014, Shift has made a name for itself in the complex world of insurance,” said Thomas Weisman, an Advent director, in a statement. “Shift’s advanced suite of SaaS products is helping insurers to reshape manual and often time-consuming claims processes in a safer and more automated way. We are proud to be part of this exciting company’s next wave of growth.”

Apr
13
2021
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SambaNova raises $676M at a $5.1B valuation to double down on cloud-based AI software for enterprises

Artificial intelligence technology holds a huge amount of promise for enterprises — as a tool to process and understand their data more efficiently; as a way to leapfrog into new kinds of services and products; and as a critical stepping stone into whatever the future might hold for their businesses. But the problem for many enterprises is that they are not tech businesses at their core, so bringing on and using AI will typically involve a lot of heavy lifting. Today, one of the startups building AI services is announcing a big round of funding to help bridge that gap.

SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.

The round is being led by SoftBank, which is making the investment via Vision Fund 2. Temasek and the government of Singapore Investment Corp. (GIC), both new investors, are also participating, along with previous backers BlackRock, Intel Capital, GV (formerly Google Ventures), Walden International and WRVI, among other unnamed investors. (Sidenote: BlackRock and Temasek separately kicked off an investment partnership yesterday, although it’s not clear if this falls into that remit.)

Co-founded by two Stanford professors, Kunle Olukotun and Chris Ré, and Liang, who had been an engineering executive at Oracle, SambaNova has been around since 2017 and has raised more than $1 billion to date — both to build out its AI-focused hardware, which it calls DataScale, and to build out the system that runs on it. (The “Samba” in the name is a reference to Liang’s Brazilian heritage, he said, but also the Latino music and dance that speaks of constant movement and shifting, not unlike the journey AI data regularly needs to take that makes it too complicated and too intensive to run on more traditional systems.)

SambaNova on one level competes for enterprise business against companies like Nvidia, Cerebras Systems and Graphcore — another startup in the space which earlier this year also raised a significant round. However, SambaNova has also taken a slightly different approach to the AI challenge.

In December, the startup launched Dataflow-as-a-Service as an on-demand, subscription-based way for enterprises to tap into SambaNova’s AI system, with the focus just on the applications that run on it, without needing to focus on maintaining those systems themselves. It’s the latter that SambaNova will be focusing on selling and delivering with this latest tranche of funding, Liang said.

SambaNova’s opportunity, Liang believes, lies in selling software-based AI systems to enterprises that are keen to adopt more AI into their business, but might lack the talent and other resources to do so if it requires running and maintaining large systems.

“The market right now has a lot of interest in AI. They are finding they have to transition to this way of competing, and it’s no longer acceptable not to be considering it,” said Liang in an interview.

The problem, he said, is that most AI companies “want to talk chips,” yet many would-be customers will lack the teams and appetite to essentially become technology companies to run those services. “Rather than you coming in and thinking about how to hire scientists and hire and then deploy an AI service, you can now subscribe, and bring in that technology overnight. We’re very proud that our technology is pushing the envelope on cases in the industry.”

To be clear, a company will still need data scientists, just not the same number, and specifically not the same number dedicating their time to maintaining systems, updating code and other more incremental work that comes managing an end-to-end process.

SambaNova has not disclosed many customers so far in the work that it has done — the two reference names it provided to me are both research labs, the Argonne National Laboratory and the Lawrence Livermore National Laboratory — but Liang noted some typical use cases.

One was in imaging, such as in the healthcare industry, where the company’s technology is being used to help train systems based on high-resolution imagery, along with other healthcare-related work. The coincidentally-named Corona supercomputer at the Livermore Lab (it was named after the 2014 lunar eclipse, not the dark cloud of a pandemic that we’re currently living through) is using SambaNova’s technology to help run calculations related to some COVID-19 therapeutic and antiviral compound research, Marshall Choy, the company’s VP of product, told me.

Another set of applications involves building systems around custom language models, for example in specific industries like finance, to process data quicker. And a third is in recommendation algorithms, something that appears in most digital services and frankly could always do to work a little better than it does today. I’m guessing that in the coming months it will release more information about where and who is using its technology.

Liang also would not comment on whether Google and Intel were specifically tapping SambaNova as a partner in their own AI services, but he didn’t rule out the prospect of partnering to go to market. Indeed, both have strong enterprise businesses that span well beyond technology companies, and so working with a third party that is helping to make even their own AI cores more accessible could be an interesting prospect, and SambaNova’s DataScale (and the Dataflow-as-a-Service system) both work using input from frameworks like PyTorch and TensorFlow, so there is a level of integration already there.

“We’re quite comfortable in collaborating with others in this space,” Liang said. “We think the market will be large and will start segmenting. The opportunity for us is in being able to take hold of some of the hardest problems in a much simpler way on their behalf. That is a very valuable proposition.”

The promise of creating a more accessible AI for businesses is one that has eluded quite a few companies to date, so the prospect of finally cracking that nut is one that appeals to investors.

“SambaNova has created a leading systems architecture that is flexible, efficient and scalable. This provides a holistic software and hardware solution for customers and alleviates the additional complexity driven by single technology component solutions,” said Deep Nishar, senior managing partner at SoftBank Investment Advisers, in a statement. “We are excited to partner with Rodrigo and the SambaNova team to support their mission of bringing advanced AI solutions to organizations globally.”

Mar
30
2021
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6sense raises $125M at a $2.1B valuation for its ‘ID graph’, an AI-based predictive sales and marketing platform

AI has become a fundamental cornerstone of how tech companies are building tools for salespeople: they are useful for supercharging (and complementing) the abilities of talented humans, or helping them keep themselves significantly more organised; even if in some cases — as with chatbots — they are replacing them altogether. In the latest development, 6sense, one of the pioneers in using AI to boost the sales and marketing experience, is announcing a major round of funding that underscores the traction AI tools are seeing in the sales realm.

The startup has raised $125 million at a valuation of $2.1 billion, a Series D being led by D1 Capital Partners, with Sapphire Ventures, Tiger Global and previous backer Insight Partners also participating.

The company plans to use the funding to expand its platform and its predictive capabilities across a wider range of sources.

For some context, this is a huge jump for the company compared to its last fundraise: at the end of 2019, when it raised $40 million, it was valued at a mere $300 million, according to data from PitchBook.

But it’s not a big surprise: at a time when a lot of companies are going through “digital transformation” and investing in better tools for their employees to work more efficiently remotely (especially important for sales people who might have previously worked together in physical teams), 6sense is on track for its fourth year of more than 100% growth, adding 100 new customers in the fourth quarter alone. It caters to small, medium, and large businesses, and some of its customers include Dell, Mediafly, Sage and SocialChorus.

The company’s approach speaks to a classic problem that AI tools are often tasked with solving: the data that sales people need to use and keep up to date on customer accounts, and critically targets, lives in a number of different silos — they can include CRM systems, or large databases outside of the company, or signals on social media.

While some tools are being built to handle all of that from the ground up, 6sense takes a different approach, providing a way of ingesting and utilizing all of it to get a complete picture of a company and the individuals a salesperson might want to target within it. It takes into account some of the harder nuts to crack in the market, such as how to track “anonymous buying behavior” to a more concrete customer name; how to prioritizes accounts according to those most likely to buy; and planning for multi-channel campaigns.

6sense has patented the technology it uses to achieve this and calls its approach building an “ID graph.” (Which you can think of as the sales equivalent of the social graph of Facebook, or the knowledge graph that LinkedIn has aimed to build mapping skills and jobs globally.) The key with 6sense is that it is building a set of tools that not just sales people can use, but marketers too — useful since the two sit much closer together at companies these days.

Jason Zintak, the company’s CEO (who worked for many years as a salesperson himself, so gets the pain points very well), referred to the approach and concept behind 6sense as “revtech”: aimed at organizations in the business whose work generates revenue for the company.

“Our AI is focused on signal, identifying companies that are in the market to buy something,” said Zintak in an interview. “Once you have that you can sell to them.”

That focus and traction with customers is one reason investors are interested.

“Customer conversations are a critical part of our due diligence process, and the feedback from 6sense customers is among the best we’ve heard,” said Dan Sundheim, founder and chief investment officer at D1 Capital Partners, in a statement. “Improving revenue results is a goal for every business, but it’s easier said than done. The way 6sense consistently creates value for customers made it clear that they deliver a unique, must-have solution for B2B revenue teams.”

Teddie Wardi at Insight highlights that AI and the predictive elements of 6sense’s technology — which have been a consistent part of the product since it was founded — are what help it stand out.

“AI generally is a buzzword, but here it is a key part of the solution, the brand behind the platform,” he said in an interview. “Instead of having massive funnels, 6sense switches the whole thing around. Catching the right person at the right time and in the right context make sales and marketing more effective. And the AI piece is what really powers it. It uses signals to construct the buyer journey and tell the sales person when it is the right time to engage.”

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.

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