Aug
31
2021
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Peak raises $75M for a platform that helps non-tech companies build AI applications

As artificial intelligence continues to weave its way into more enterprise applications, a startup that has built a platform to help businesses, especially non-tech organizations, build more customized AI decision-making tools for themselves has picked up some significant growth funding. Peak AI, a startup out of Manchester, England, that has built a “decision intelligence” platform, has raised $75 million, money that it will be using to continue building out its platform, expand into new markets and hire some 200 new people in the coming quarters.

The Series C is bringing a very big name investor on board. It is being led by SoftBank Vision Fund 2, with previous backers Oxx, MMC Ventures, Praetura Ventures and Arete also participating. That group participated in Peak’s Series B of $21 million, which only closed in February of this year. The company has now raised $119 million; it is not disclosing its valuation.

(This latest funding round was rumored last week, although it was not confirmed at the time and the total amount was not accurate.)

Richard Potter, Peak’s CEO, said the rapid follow-on in funding was based on inbound interest, in part because of how the company has been doing.

Peak’s so-called Decision Intelligence platform is used by retailers, brands, manufacturers and others to help monitor stock levels and build personalized customer experiences, as well as other processes that can stand to have some degree of automation to work more efficiently, but also require sophistication to be able to measure different factors against each other to provide more intelligent insights. Its current customer list includes the likes of Nike, Pepsico, KFC, Molson Coors, Marshalls, Asos and Speedy, and in the last 12 months revenues have more than doubled.

The opportunity that Peak is addressing goes a little like this: AI has become a cornerstone of many of the most advanced IT applications and business processes of our time, but if you are an organization — and specifically one not built around technology — your access to AI and how you might use it will come by way of applications built by others, not necessarily tailored to you, and the costs of building more tailored solutions can often be prohibitively high. Peak claims that those using its tools have seen revenues on average rise 5%, return on ad spend double, supply chain costs reduce by 5% and inventory holdings (a big cost for companies) reduce by 12%.

Peak’s platform, I should point out, is not exactly a “no-code” approach to solving that problem — not yet at least: It’s aimed at data scientists and engineers at those organizations so that they can easily identify different processes in their operations where they might benefit from AI tools, and to build those out with relatively little heavy lifting.

There have also been different market factors that have played a role. COVID-19, for example, and the boost that we have seen both in increasing “digital transformation” in businesses and making e-commerce processes more efficient to cater to rising consumer demand and more strained supply chains have all led to businesses being more open and keen to invest in more tools to improve their automation intelligently.

This, combined with Peak AI’s growing revenues, is part of what interested SoftBank. The investor has been long on AI for a while; but it also has been building out a section of its investment portfolio to provide strategic services to the kinds of businesses in which it invests.

Those include e-commerce and other consumer-facing businesses, which make up one of the main segments of Peak’s customer base.

Notably, one of its recent investments specifically in that space was made earlier this year, also in Manchester, when it took a $730 million stake (with potentially $1.6 billion more down the line) in The Hut Group, which builds software for and runs D2C businesses.

“In Peak we have a partner with a shared vision that the future enterprise will run on a centralized AI software platform capable of optimizing entire value chains,” Max Ohrstrand, senior investor for SoftBank Investment Advisers, said in a statement. “To realize this a new breed of platform is needed and we’re hugely impressed with what Richard and the excellent team have built at Peak. We’re delighted to be supporting them on their way to becoming the category-defining, global leader in Decision Intelligence.”

It’s not clear that SoftBank’s two Manchester interests will be working together, but it’s an interesting synergy if they do, and most of all highlights one of the firm’s areas of interest.

Longer term, it will be interesting to see how and if Peak evolves to extend its platform to a wider set of users at the organizations that are already its customers.

Potter said he believes that “those with technical predispositions” will be the most likely users of its products in the near and medium term. You might assume that would cut out, for example, marketing managers, although the general trend in a lot of software tools has precisely been to build versions of the same tools used by data scientists for these less technical people to engage in the process of building what it is that they want to use.

“I do think it’s important to democratize the ability to stream data pipelines, and to be able to optimize those to work in applications,” Potter added.

Jul
20
2021
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How we built an AI unicorn in 6 years

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.

And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here’s how we did it, and what we learned along the way.

Build upon a fresh technological breakthrough

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”

Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.

The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.

Search for complementary co-founders who will become your best friends

I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.

But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.

Find potential customers early so you can work out market fit

Tractable started at EF with a head start — a paying customer. Our first use case was … plastic pipe welds.

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.

And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.

Jul
12
2021
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Quantexa raises $153M to build out AI-based big data tools to track risk and run investigations

As financial crime has become significantly more sophisticated, so too have the tools that are used to combat it. Now, Quantexa — one of the more interesting startups that has been building AI-based solutions to help detect and stop money laundering, fraud and other illicit activity — has raised a growth round of $153 million, both to continue expanding that business in financial services and to bring its tools into a wider context, so to speak: linking up the dots around all customer and other data.

“We’ve diversified outside of financial services and working with government, healthcare, telcos and insurance,” Vishal Marria, its founder and CEO, said in an interview. “That has been substantial. Given the whole journey that the market’s gone through in contextual decision intelligence as part of bigger digital transformation, was inevitable.”

The Series D values the London-based startup between $800 million and $900 million on the heels of Quantexa growing its subscriptions revenues 108% in the last year.

Warburg Pincus led the round, with existing backers Dawn Capital, AlbionVC, Evolution Equity Partners (a specialist cybersecurity VC), HSBC, ABN AMRO Ventures and British Patient Capital also participating. The valuation is a significant hike up for Quantexa, which was valued between $200 million and $300 million in its Series C last July. It has now raised over $240 million to date.

Quantexa got its start out of a gap in the market that Marria identified when he was working as a director at Ernst & Young tasked with helping its clients with money laundering and other fraudulent activity. As he saw it, there were no truly useful systems in the market that efficiently tapped the world of data available to companies — matching up and parsing both their internal information as well as external, publicly available data — to get more meaningful insights into potential fraud, money laundering and other illegal activities quickly and accurately.

Quantexa’s machine learning system approaches that challenge as a classic big data problem — too much data for a human to parse on their own, but small work for AI algorithms processing huge amounts of that data for specific ends.

Its so-called “Contextual Decision Intelligence” models (the name Quantexa is meant to evoke “quantum” and “context”) were built initially specifically to address this for financial services, with AI tools for assessing risk and compliance and identifying financial criminal activity, leveraging relationships that Quantexa has with partners like Accenture, Deloitte, Microsoft and Google to help fill in more data gaps.

The company says its software — and this, not the data, is what is sold to companies to use over their own data sets — has handled up to 60 billion records in a single engagement. It then presents insights in the form of easily digestible graphs and other formats so that users can better understand the relationships between different entities and so on.

Today, financial services companies still make up about 60% of the company’s business, Marria said, with seven of the top 10 U.K. and Australian banks and six of the top 14 financial institutions in North America among its customers. (The list includes its strategic backer HSBC, as well as Standard Chartered Bank and Danske Bank.)

But alongside those — spurred by a huge shift in the market to rely significantly more on wider data sets, to businesses updating their systems in recent years, and the fact that, in the last year, online activity has in many cases become the “only” activity — Quantexa has expanded more significantly into other sectors.

“The Financial crisis [of 2007] was a tipping point in terms of how financial services companies became more proactive, and I’d say that the pandemic has been a turning point around other sectors like healthcare in how to become more proactive,” Marria said. “To do that you need more data and insights.”

So in the last year in particular, Quantexa has expanded to include other verticals facing financial crime, such as healthcare, insurance, government (for example in tax compliance) and telecoms/communications, but in addition to that, it has continued to diversify what it does to cover more use cases, such as building more complete customer profiles that can be used for KYC (know your customer) compliance or to serve them with more tailored products. Working with government, it’s also seeing its software getting applied to other areas of illicit activity, such as tracking and identifying human trafficking.

In all, Quantexa has “thousands” of customers in 70 markets. Quantexa cites figures from IDC that estimate the market for such services — both financial crime and more general KYC services — is worth about $114 billion annually, so there is still a lot more to play for.

“Quantexa’s proprietary technology enables clients to create single views of individuals and entities, visualized through graph network analytics and scaled with the most advanced AI technology,” said Adarsh Sarma, MD and co-head of Europe at Warburg Pincus, in a statement. “This capability has already revolutionized the way KYC, AML and fraud processes are run by some of the world’s largest financial institutions and governments, addressing a significant gap in an increasingly important part of the industry. The company’s impressive growth to date is a reflection of its invaluable value proposition in a massive total available market, as well as its continued expansion across new sectors and geographies.”

Interestingly, Marria admitted to me that the company has been approached by big tech companies and others that work with them as an acquisition target — no real surprises there — but longer term, he would like Quantexa to consider how it continues to grow on its own, with an independent future very much in his distant sights.

“Sure, an acquisition to the likes of a big tech company absolutely could happen, but I am gearing this up for an IPO,” he said.

May
19
2021
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Forecast nabs $19M for its AI-based approach to project management and resource planning

Project management has long been a people-led aspect of the workplace, but that has slowly been changing. Trends in automation, big data and AI have not only ushered in a new wave of project management applications, but they have led to a stronger culture of people willing to use them. Today, one of the startups building a platform for the next generation of project management is announcing some funding — a sign of the traction it’s getting in the market.

Forecast, a platform and startup of the same name that uses AI to help with project management and resource planning — put simply, it uses artificial intelligence to both “read” and integrate data from different enterprise applications in order to build a bigger picture of the project and potential outcomes — has raised $19 million to continue building out its business.

The company plans to use some of the funding to expand to the U.S., and some to continue building out its platform and business, headquartered in London with a development office also in Copenhagen.

This funding, a Series A, comes less than a year after the startup’s commercial launch, and it was led by Balderton Capital, with previous investors Crane Ventures Partners, SEED Capital and Heartcore also participating.

Forecast closed a seed round in November 2019 and then launched just as the pandemic was kicking off. It was a time when some projects were indeed put on ice, but others that went ahead did so with more caution on all sorts of fronts — financial, organizational and technical. It turned out to be a “right place, right time” moment for Forecast, a tool that plays directly into providing a technical platform to manage all of that in a better way, and it tripled revenues during the year. Its customers include the likes of the NHS, the Red Cross, Etain and more. It says over 150,000 projects have been created and run through its platform to date.

Project management — the process of planning what you need to do, assigning resources to the task and tracking how well all of that actually goes to plan — has long been stuck between a rock and a hard place in the world of work.

It can be essential to getting things done, especially when there are multiple departments or stakeholders involved; yet it’s forever an inexact science that often does not reflect all the complexities of an actual project, and therefore may not be as useful as it could or should be.

This was a predicament that founder and CEO Dennis Kayser knew all too well, having been an engineer and technical lead on a number of big projects himself. His pedigree is an interesting one: One of his early jobs was as a developer at Varien, where he built the first version of Magento. (The company was eventually rebranded as Magento and then acquired by eBay, then spun out, then acquired again, this time by Adobe for nearly $1.7 billion, and is now a huge player in the world of e-commerce tools.) He also spent years as a consultant at IBM, where among other things he helped build and formulate the first versions of ikea.com.

In those and other projects, he saw the pitfalls of project management not done right — not just in terms of having the right people on a project at the right time, but the resource planning needed, better calculations of financial outcomes in the event of a decision going one way or the other, and so on.

He didn’t say this outright, but I’m sure one of the points of contention was the fact that the first ikea.com site didn’t actually have any e-commerce in it, just a virtual window display of sorts. That was because Ikea wanted to keep people shopping in its stores, away from the efficiency of just buying the one thing you actually need and not the 10 you do not. Yes, there are plenty of ways now of recirculating people to buy more when you select one item for a shopping cart — something the likes of Amazon has totally mastered — but this was years ago when there was still even more opportunities for innovation than there are now. All of this is to say that you might very reasonably argue that had there been better project managing and resource planning tools to give forecasts of potential outcomes of one or another route taken, people advocating for a different approach could have made their case better. And maybe Ikea would have jumped on board with digital commerce far sooner than it did.

“Typically you get a lot of spreadsheets, people scattered across different tools that include accounting, CRM, Gitlab and more,” Kayser said.

That became the impetus for trying to build something that can take all of that into account and make a project management tool that — rather than just being a way of accounting to a higher-up, or reflecting only what someone can be bothered to update in the system — something that can help a team.

“Connecting everything into our engine, we leverage data to understand what they are working on and what is the right thing to be working on, what the finances are looking like,” he continued. “So if you work in product, you can plan out who is where, and what resourcing you need, what kind of people and skills you require.” This is a more dynamic progression of some of the other newer tools that are being used for project management today, targeting, in his words, “people who graduate from Monday and Asana who need something more robust, either because they have too many people working on a project or because it’s too complicated, there is just too much stuff to handle.”

More legacy tools he said that are used include Oracle “to some degree” and Mavenlink, which he describes as possibly Forecast’s closest competitor, “but its platform is aging.”

Currently the Forecast platform has some 26 integrations of popular tools used for projects to produce its insights and intelligence, including Salesforce, Gitlab, Google Calendar, and, as it happens, Asana. But given how fragmented the market is, and the signals one might gain from any number of other resources and apps, I suspect that this list will grow as and when its customers need more supported, or Forecast works out what can be gleaned from different places to paint an even more accurate picture.

The result may not ever replace an actual human project manager, but certainly starts to then look like a “digital twin” (a phrase I have been hearing more and more these days) that will definitely help that person, and the rest of the team, work in a smarter way.

“We are really excited to be an early investor in Forecast,” said James Wise, a partner at Balderton Capital, in a statement. “We share their belief that the next generation of SaaS products will be more than just collaboration tools, but use machine learning to actively solve problems for their users. The feedback we got from Forecast’s customers was quite incredible, both in their praise for the platform and in how much of a difference it had already made to their operations. We look forward to supporting the company to scale this impact going forward.”

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.

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