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Evisort embeds AI into contract management software, raises $100M

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For lawyers and the organizations that employ them, time is quite literally money. The business of contract management software is all about helping to optimize that process, reducing the time and money it takes to understand and manage contracts. 

As it turns out, there is big money in the market for contract management software as well. An increasingly integral part of the business is the use of AI-powered automation. To that end, today contract management vendor Evisort announced that it raised $100 million in a series C round of funding, bringing total funding to date up to $155.6 million. 

Evisort was founded in 2016 and raised a $15 million series A back in 2019. The company was founded by a team of Harvard Law and MIT researchers and discovered early on that there was a market opportunity for using AI to help improve workflow for contracts within organizations.

“If you think about it, every time a company sells something, buys something or hires somebody, there’s a contract,” Evisort cofounder and CEO Jerry Ting told VentureBeat. “Contract data really is everywhere.”

Contract management is a growing market

Evisort fits squarely into a market that analysts often refer to as contract lifecycle management (CLM). Gartner Peer Insights lists at least twenty vendors in the space, which includes both startups and more established vendors.

Among the large vendors in the space is DocuSign, which entered the market in a big way in 2020 with its $188 million acquisition of AI contract discovery startup Seal Software. Startups are also making headway, with SirionLabs announcing this week that it has raised $85 million to help add more AI and automation to its contract management platform.

The overall market for contract lifecycle management is set to grow significantly in the coming years, according to multiple reports. According to Future Market Insights, the global market for CLM in 2021 generated $936 million in revenue and is expected to reach $2.4 billion by 2029. MarketsandMarkets provides a more considerable number, with the CLM market forecast to grow to $2.9 billion by 2024.

Ting commented that while every organization has contracts, in this view many organizations still do not handle contracts with a digital system and rely on spreadsheets and email. That’s one of the key reasons why he expects to see significant growth in the CLM space as organizations realize there is a better way to handle contracts.

Integrating AI to advance the state of contract management

Evisort’s flagship platform uses AI to read contracts that users then upload into the software-as-a-service (SaaS)-based platform.

Ting explained that his company developed its own algorithm to help improve natural language processing and classification of important areas in contracts. Those areas could include terms of a deal, such as deadlines, rates and other conditions of importance for a lawyer who is analyzing a contract. Going a step further, Evisort’s AI will now also analyze the legal clauses in an agreement.

“We can actually pull the pertinent data out of a contract, instead of having a human have to type it into a different system,” Ting said. 

Once all the contract data is understood and classified, the next challenge that faces organizations is what to do with all the data. That’s where the other key part of Evisort’s platform comes into play, with a no-code workflow service. The basic idea with the workflow service is to help organizations collaborate on contract activities, including analysis and approvals.

What $100M of investment into AI will do for Evisort

With the new funding, Ting said that his company will continue to expand its go-to market and sales efforts. Evisort will also be investing in new AI capabilities that Ting hopes will democratize access to AI for contract management.

To date, he explained that Evisort’s AI works somewhat autonomously based on definitions that Evisort creates. With future releases of the platform, Ting wants to enable users to take Evisort’s AI and adjust and train the algorithm for specific and customized needs. The plan is to pair Evisort’s no-code capabilities into the future feature, in an approach that will make it easy for regular users and not just data scientists, to build AI capabilities to better understand and manage contracts.

“I think the 100 million dollar mark tells the market, hey, this company is a serious player and they’re here to stay,” Ting said. “It’s a scale-up, not a startup.”

The new funding round was led by TCV with participation from Breyer Capital as well as existing investors Vertex Ventures, Amity Ventures, General Atlantics and Microsoft’s venture capital fund M12.

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AI

ControlUp lands $100M to help enterprise IT teams manage remote software

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San Jose, California-based ControlUp, an IT infrastructure management, monitoring, and troubleshooting platform, today announced that it raised $100 million co-contributed by K1 Investment Management and JVP, bringing its total raised to $140 million. CEO Asaf Ganot says that the investment will enable ControlUp to expand its employee headcount while supporting ongoing product development efforts.

“This injection of capital will accelerate our ability to help more enterprises open the door to the limitless possibilities of a simpler, more reliable work-from-anywhere experience,” Ganot said in a statement. “We give IT real-time visibility into system status, with the ability to resolve help desk calls faster, and even handle potential system issues before they happen. All this translates to fewer headaches, lower costs, higher productivity, and happier people.”

ControlUp’s tranche comes as IT teams struggle to contend with remote and hybrid work setups emerging during the pandemic. According to a PricewaterhouseCoopers survey, 17% of employers say that the shift to remote work hasn’t been successful for their company. Among the other headaches are security and governance vulnerabilities — 45% of professionals expect their company to suffer a data breach during the pandemic due to staff using personal devices that aren’t properly protected.

ControlUp aims to address the growing challenges with a software-as-a-service product that collects device metrics (e.g., CPU, RAM, bandwidth, and I/O usage; protocol latency; and app load time) to help customers troubleshoot and remediate software issues. The platform collects up to one year of virtual desktop interface, server, and device environment data, analyzing it to proactively warn of potential issues with the availability of enterprise resources including domain name servers, file shares, and print services.

Device monitoring

ControlUp was founded in 2008 by Ganot and Yoni Avital, who began their careers at a Citrix services company implementing end-user computing projects. While there, they built tools that helped expose common technical problems in virtual desktop environments, which became the cornerstone of ControlUp’s current product offering.

ControlUp provides telemetry dashboards, updated every few seconds, that highlight and help to fix problems with virtual desktops and apps — for example, slow app response. IT admins can leverage search and grouping options to show resources as they change states or opt for automated actions and scripts that clean up temp directories, expand disk size, log off idle users, and more.

ControlUp’s “top insights” pane summarizes findings through widgets that spotlight anomalies, key performance indicators, and other metrics that could impact performance. The metrics are compared against both internal averages and a “global benchmark” consisting of anonymized data aggregated from all ControlUp’s enterprise customers. ControlUp uses the data to, among other things, provide AI-driven recommendations for increasing or decreasing assigned CPU and RAM to machines, and to answer granular questions like “Is my user’s profile load time phase long or short compared to other organizations with SSD storage?”

ControlUp

Above: ControlUp’s monitoring dashboard.

“By analyzing data from tens of thousands of troubleshooting sequences and continuously improving its machine learning algorithms, [ControlUp] recommends the shortest drill down path to uncover the root cause of [a] problem,” the company says on its website. “ControlUp’s … real-time engine connects to a multitude of data sources using flexible and expandable data collectors that cover a wide array of architectures and technologies. It utilizes a high performance in-memory database in order to digest, associate, and correlate hundreds of thousands of records in a single node.”

Expanding market

A 2021 Omdia survey predicts that, going forward, only 24% of knowledge workers will be permanently based in a office and working from a single desk. This is likely to further strain IT departments already struggling to adapt to the new norm. According to Riverbed, 94% of companies experienced technology problems that impacted their business while employees worked remotely, particularly disconnections from corporate networks, slow file downloads, and long response times when loading apps.

Against this backdrop, business has been booming for 250-employee ControlUp, which says it’s seen 50% revenue growth and 67% growth in enterprise accounts year-over-year, with over 1 million new seats deployed around the world. While it competes with 1E, Nexthink, and Lakeside, ControlUp notes that it currently supports over 5 million devices across 1,500 customers including four of the top five U.S. health insurance companies and five of the top eight US health care companies.

“The pandemic amplified the complexities of supporting employees when they started working from remote locations. These issues have been significant — and our solutions help,” Ganot told VentureBeat via email. “While [the pandemic] did not create the ‘work from anywhere’ trends, we have seen this experience accelerated. Enterprises across industries and around the world are looking closely at how they solve these problems. ControlUp is focused on helping companies give their people the freedom and flexibility to work from anywhere. We do this by empowering IT teams to optimize remote environments, prevent user downtime, and resolve issues faster. Ultimately, we enable businesses deliver an outstanding digital employee experience.”

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H2O.ai secures $100M, lands partnership with Australian bank

Mountain View, California-based H2O.ai, which provides a cloud platform for AI system development, today announced that it raised $100 million in a series E round led by Commonwealth Bank of Australia (CBA) with participation from Goldman Sachs, Pivot Investment Partners, Crane Venture Partners, Celesta Capital, and others. It brings H2O’s total raised to over $251 million and values the company at $1.6 billion, as H2O partners with CBA to boost the latter’s AI capabilities and generate” better customer and community outcomes at a more rapid pace and … scale.”

H2O (originally Oxdata) was founded in 2012 by Sri Satish Ambati, who previously served as a research assistant at the Indian Space Research Organization. It sprung up from an open source project designed to integrate with data science workflows written in the programming language R. Forced to lay off a portion of its workforce early on, H2O pivoted in 2016 to working closely with a few major customers and launching cloud services that could be promoted through conventional sales and marketing channels.

H2O’s products today are designed to simplify machine learning deployment across verticals like financial services, insurance, health care, telecommunications, retail, pharmaceutical, and marketing. The company offers prebuilt models and apps for use cases like customer churn prediction, sales targeting, spend optimization, credit risk scoring, anti-money laundering, predictive maintenance, customer monitoring, malicious domain detection, and more.

“Our AI cloud platform delivers a deep set of capabilities that not only help data scientists evaluate their models … but also help explain [the] models to business users and executives. In addition, the integrated platform hosts and monitors the ethically built and understood models, to ensure ongoing business success,” Ambati told VentureBeat via email. “We work closely with our customers to ensure the success of their AI initiatives and are able to help our customers move from ‘lab experiments’ to real business value.”

AutoML

H2O’s flagship product, which can be used to create a range of statistical models and algorithms, runs on top of existing datacenter clusters. Its AutoML functionality automatically runs through models and their parameters to produce a leaderboard of the best models, tapping technologies like distributed systems and in-memory computing to accelerate data processing.

H2O guides customers through the process of creating their own AI-powered apps and services. They can create recipes that extend the platform as well as add administration and collaboration features for model management and implementation, such as health checks and data science metrics around drift detection, model degradation, A/B testing, and alerts for recalibration and retraining.

H2O.ai

Above: H2O’s model development and monitoring dashboard.

For customers with specific requirements, H2O offers enterprise support with training, account managers, and prioritized issue resolution. Subscription plans include access to tools for orchestrating machine learning models across larger datacenter clusters.

H2O recently partnered with AT&T to build and launch an AI feature store that manages and reuses data, housing and distributing the features needed to build AI models. (In machine learning, features — variables that act like input data– are used by models to make predictions.) H2O also recently announced platform integrations with data analytics tool provider Teradata and AI platform KNIME to enable “workflow management across the entire data science lifecycle,” in H2O’s words.

Growth in AI adoption

As companies face pandemic headwinds including worker shortages and supply chain disruptions, they’re increasingly turning to AI for efficiency gains. According to a recent Algorithmia survey, 50% of enterprises plan to spend more on AI and machine learning in 2021, with 20% saying they will be “significantly” increasing their budgets. In a 2020 report, analysts at McKinsey wrote, “[S]ome companies are capturing value from AI at the enterprise level, and many are generating revenue and cost reductions at least at the function level.”

But top challenges around AI remain, particularly when it comes to ingesting, processing, and managing training data. Data scientists spend the bulk of their time cleaning and organizing data. And respondents to Alation’s latest quarterly State of Data Culture Report said that inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.

That’s one of the reasons that managed and automated AI development platforms like H2O have gained ground in recent years. (The global AutoML market along generated $270 million in revenue in 2019.) H2O’s competitors include Amazon SageMaker, Azure Cognitive Services, and Google’s Cloud AutoML, as well as startups like DataRobot and Abacus.ai. But H2O has managed to nab over 20,000 organizations as customers to date, including over half of the Fortune 500.

For example, jewelry insurer Jewelers Mutual is using H2O’s platform to build models that can identify which customers need additional physical security personnel to protect their inventory during California wildfires. Nationwide, another customer, has tapped H2O’s tools to create systems for customer retention, call routing, and fraud prevention. And İşbank, a private bank in Turkey, has developed models for income prediction, cash forecasting, and check default prediction leveraging H2O’s solutions.

CBA CEO Matt Comyn said that the investment in 300-employee H2O will boost the bank’s ability to offer AI-powered products and services to customers. Specifically, he expects it’ll bolster the bank’s analytics capabilities and help enhance the predictive accuracy of its existing models, so that CBA can offer “more personalized and targeted solutions” at a faster rate.

“One of the strengths of CBA, with its large market share and broad coverage across all aspects of the Australian economy, is the large amount of data that it securely holds and the technological infrastructure it has to capture data,” Comyn said in a press release. “Customers trust us to use that for good, and this partnership will help us accelerate that.”

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Notable aims to improve AI in health care with new $100M

This article is part of a VB special issue. Read the full series: AI and the future of health care


Notable, an intelligent automation company focused on health care, today announced it received a $100 million series B funding round. The investment, led by ICONIQ Growth with participation from Greylock Ventures, Oak HC/ FT, and F-Prime, will be used to expand access to more health care providers and enhance its capabilities, so partners achieve a higher return on investment.

The reality is that many health care providers still use repetitive, manual workflows, which cost over $1 trillion in administrative overhead per year. A patient may spend seven minutes with a physician – but that visit could result in hundreds of minutes of administrative work per clinician, according to Pranay Kapadia, cofounder, and CEO of Notable. Using AI, Notable can eliminate more than 700 minutes of that administrative work, including creating clinical documentation and adding billing codes for the insurance claim processing.

The investment points to a larger industry trend toward using AI to improve patient care and streamline processes. Care sites like Intermountain Healthcare and CommonSpirit Health already use Notable, which automates everything from patient scheduling and check-in to post-visit follow-up, as well as creating clinical documentation and adding billing codes.

Demand for AI continues to increase as patients expect a digital-first experience due to the COVID-19 pandemic, as well as the “great resignation” that has left every industry — including health care — short-staffed. “Technology needs to drive ten times the efficiency at a quarter of the cost,” said Kapadia.

“Technology is the future of everything, and health care is no exception,” said Andrew J. Scott, founding partner of 7percent Ventures. “Artificial intelligence is already having a positive impact. Companies like Kheiron Medical can already perform mammography analysis for breast cancer better than a human.”

7percent Ventures invests in AI technology including Limbic, which uses AI for mental health triage and support, and Kherion Medical, which provides improved breast cancer diagnosis. These “are the sorts of transformative technologies that have a positive impact and improve the way we live,” he said.

Will AI Provide All Diagnoses?

Going all-in on AI in a health care setting may speed up a diagnosis – but it also takes away a physician’s autonomy in making the diagnosis and recommending treatment, according to Robert Wachter, MD, professor, and chair of the Department of Medicine at the University of California, San Francisco.

“There are a lot of sources of pushback, from the physician’s ego to worries about malpractice and who is liable, to ethical issues around AI,” such as whether the data is biased, he said. For example, the data may note that patients of one race don’t need as much medication as patients of another, without taking into account that particular patient’s situation.

AI will tackle more tractable problems like workflows before heading into the more difficult ones like diagnosis and prognosis, but there won’t be a real “AI moment,” Wachter said. “You start …where the stakes are less high, with business and operational problems.”

Instead, AI will augment what physicians are doing and provide options, including triage, but ultimately leave the decision up to the physician’s discretion.

“I see AI working silently behind the scenes of the busy clinician,” said Chris Larkin, chief technology officer at Concord Technologies. “The models will continue to gather data on patient diagnosis and trajectories and update the clinician when it’s appropriate. This is more like modern avionics, working on behalf of the pilot of the aircraft.”

For example, ICU nurses hear thousands of patient alarms on their shifts, many of which are false. AI can help the nurses decide which ones are most pressing based on the patient’s diagnosis and attend to them first, Larkin said.

Some clinicians already are using AI and machine learning exactly this way. “I’ve used VIDA Insights as an AI agent to assist me in interpreting chest CTs,” said John Newell, MD, professor of radiology and biomedical engineering, director of the Radiology Image Phenotyping Laboratory, and the co-director of the Iowa Institute for Biomedical Imaging.

Additionally, AI can help lower costs for both patients and health care organizations while providing better care. “If AI can help us to diagnose disease earlier and with more accuracy, the impact on reducing the cost of patient care can be significant,” Newell said.

“For example, a patient with early-stage COPD spends about $1,600 [per] year on care versus a patient with advanced-stage COPD who spends nearly $11,000 [per] year. COPD is often diagnosed later in the disease process, so any tools that can help providers identify it early can have a massive impact on population health care costs.”

Despite the opportunities AI provides for the health care industry, humans will always be needed — and AI doesn’t aim to entirely displace them. “With all the AI in the world, [there’s still] a certain level of empathy that comes in health care,” Kapadia said, noting that, like comforting a child with a sore throat, AI isn’t needed for that.

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Automated document processing platform Rossum raises $100M

Rossum Ltd., a platform used by enterprises to automatically process and “understand” all their inbound documents, has raised $100 million in a series A round of funding. Sources say that Rossum is now valued at somewhere between $500 million and $1 billion.

Some 550 billion invoices alone change hands globally each year, spanning all manner of formats across paper and digital. Processing these documents, and extracting key data, is typically a time-consuming endeavor, something that Rossum is setting out to solve.

Cognitive data capture

Founded out of the Czech Republic in 2017 by a trio of AI PhD students, Rossum leans on deep learning to automate manual document processing and data entry. Powered by integrations with myriad enterprise resource planning (ERP), robotic process automation (RPA), and document management systems (DMS), Rossum uses “cognitive data capture” to read and understand the content of a document just like a human does.

Rossum facilitates not only the data extraction, but also the data validation and correction through what it calls “spatial OCR (optical character recognition),” which learns to recognize different structures and patterns of different documents — an invoice number might be on the top left-hand side in one invoice, but somewhere completely different in another. Using a “human-in-the-loop” system to learn, Rossum also promises to improve over time.

Above: Rossum: “Understanding”

Back in 2019, the Prague-based company announced that it had raised $4.5 million over several rounds of funding since its inception, and it later went on to extend that to $9.5 million the following year — this extension previously went unannounced until today. Rossum also now claims more than 150 corporate clients, including PepsiCo, Bosch, and Siemens.

With another $100 million in the bank from General Catalyst, LocalGlobe, Seedcamp, Miton and Elad Gil, Rossum said that it will double down on its support for various industries and use-cases, spanning finance, logistics, insurance, order management, and more. It will also open new hubs in the U.S., Europe, and Asia, with plans to also open a research center in Prague.

A document processing ‘gateway’

Perhaps the biggest development since Rossum’s last funding announcement nearly two years ago is that it has transitioned from a “document understanding engine” into what it now calls itself an “all-in-one document gateway.”

This includes receive, which is all about unifying all mail channels across paper and digital by connecting inboxes, scanners, and DMS systems; understand, which is concerned with data capture; and communicate and act, which automates workflows such as notifying the document’s senders that it has been received or whether there are any issues.

There are a number of players operating in the broader document automation space already, including Kofax and Abbyy, while newer VC-backed startups such as Zuva and Hyperscience also offer something similar. But by focusing on the full document journey, looking beyond the data capture and extraction, Rossum hopes to set itself apart.

“Rossum allows customers to ingest and read any type of document, understand what is in that document or what might be missing, and then act or communicate on it,” Rossum CEO and cofounder Tomas Gogar told VentureBeat. “Document types could be invoices, compliance documents, shipping manifests, and waybills or insurance claims data in any format shared via any channel.”

Above: Rossum: Communicate and act

In terms of how Rossum might be used in a real-world scenario, consider a sales department belonging to a major multinational manufacturer — they might receive hundreds of thousands of purchase orders from countless retailers. Some might send their orders in by PDF or email, others perhaps by mail or even fax. Without a standardized format, this makes it difficult for companies to use automated tools, meaning having to manually check each document and proactively reach out to the sender to communicate receipt or any problems.

By unifying paper and digital, trying to “understand” each document, and automating many of the laborious workflows that follow, Rossum can save companies a considerable amount of time.

“The retailers do not need to change anything, they [can still] use their process to place the order through the channels they are used to,” Gogar explained. “But the manufacturer completely automates the process via Rossum — they can receive and understand the incoming documents in all the formats. If there is a mistake in the document, Rossum automatically informs the sending party about the mistake and navigates them [through] how to correct that.”

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Mindtickle raises $100M to gamify sales training

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Mindtickle, which provides a “sales readiness” platform for enterprises, today announced it has closed a $100 million series E funding round led by SoftBank, with participation from Chimera Capital, Norwest Venture Partners, Canaan, NewView Capital, and Qualcomm Ventures. The funds, which bring the company’s total raised to $281 million at a $1.2 billion post-money valuation, will be used to expand Mindtickle’s sales enablement, revenue operations, and training teams, according to cofounder and CEO Krishna Depura.

There’s a real and present need for sales readiness solutions. According to Forbes, 58% of buyers report that sales reps are unable to answer their questions accurately or effectively. Moreover, an estimated 84% of all sales training is lost after 90 days due to the lack of information retention among sales personnel. Perhaps unsurprisingly, high-performing sales teams use nearly 3 times the amount of sales technology as underperforming teams, one source found.

Mindtickle’s platform offers continuous learning modules, like simulated scenarios, structured coaching programs, and quizzes and polls. It gamifies lessons and skill-building activities with points, badges, certifications, and leaderboards, which it funnels to a dashboard to expose potential knowledge gaps. On the admin side, Mindtickle creates competency maps that identify problem areas and automatically assigns training based on results, tracking real-time engagement and readiness while delivering personalized feedback to reps as they progress through course materials.

“Throughout our professional lives, I and my cofounders, Nishant Mungali and Mohit Garg, experienced the challenges of unengaging, ineffective training and coaching, firsthand. To solve that problem, we first built a gamification platform that could be used by HR leaders to engage and inform their teams,” Depura told VentureBeat via email. “Over time, through innumerable discussions with customers and prospects, we discovered that the customer-facing teams, particularly the sales teams, could leverage our platform to become more effective and achieve better business results. From that point on, we engaged deeply with revenue leaders across the globe to solve specific use cases in the sales organization and help them create high-performing sales teams.”

Supercharging sales

Mindtickle taps machine learning models to optimize administrative tasks like data entry, aiming to identify knowledge and skill gaps that could impact customer interactions. Conversational intelligence capabilities provide insight into what’s happening across all real-world deal interactions. By learning what’s important to both sellers and buyers, the models deliver opportunities for salespeople to be coached and to reinforce their knowledge, Depura says.

“Perhaps the most impactful AI for sales is focused on seller preparation and call execution to reduce or eliminate the need for human review, pattern detection, and decision-making … [Our AI can] give sellers more time to sell and extends to helping them become more effective,” he added. “AI can help sellers get ready for every customer interaction, [preparing] sellers so they’re ready with the right knowledge, skills, and execution at every stage of the sales process.”

In 2020, Mindtickle claims to have doubled the number of Fortune 500 and Forbes Global 2000 companies it counts as customers, which span health and life sciences organizations, insurance carriers, and tech brands. In total, it has more than 1 million users and 220 brands on the platform, 9 out of 10 of which expanded the scope of their workforce readiness programs after adopting Mindtickle.

MindTickle

“The pandemic accelerated the digitization trends of business-to-business buying and selling, with fewer on-site sales meetings, convergence of inside and field sales, and increased adoption of digital tools. [R]evenue leaders must ensure their sales teams are agile enough to adapt to the shifting landscape by equipping them with the knowledge, skills, and behaviors needed to be successful,” Depura added. “Combined, these trends have resulted in an increased demand for remote-first approaches and technologies that enable and prepare customer-facing employees … [With Mindtickle], revenue leaders can partner with their enablement organizations to define a singular measurement that sets a baseline for what knowledge, skills, and capabilities each sales rep in your organization should possess … Mindtickle’s sales content management capabilities allow prescriptive guidance on not only what content to use, but how it should be deployed and when.”

San Francisco, California-based Mindtickle currently has around 480 employees and says it’s hiring “aggressively” across all areas of the business. By the end of the year, it expects to employ “well north” of 500.

Mindtickle competes in a sales enablement market that’s anticipated to be worth $2.6 billion by 2024, according to Markets and Markets. Rival startup Seismic has raised tens of millions of dollars to roll out its automated sales and marketing enablement suite, as has Showpad. There’s also Outreach, which is creating a semiautomated sales engagement software, along with AI-powered sales enablement toolset developer Highspot.

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Amperity raises $100M to unify customer data

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Amperity, a Seattle, Washington-based customer data platform, today announced it has raised $100 million in series D funding, valuing the company at more than $1 billion post-money. Founder and CEO Kabir Shahani said the company will use these funds, which bring its total raised to $187 million, to expand sales and marketing, grow internationally, and invest in R&D.

The pressures of the pandemic made knowing consumers a matter of survival for many businesses. Faced with the challenge of optimizing performance without third-party data, brands recognized the necessity of having an identity graph as part of an effort to sustain customer relationships. These graphs, which are at the foundation of customer data platforms, allow enterprises to make sense of trillions of data points from customers across in-store visits, web traffic, mobile usage, loyalty programs, and other touchpoints.

Amperity was founded in 2016 by Shahani and Derek Slager, who aimed to free engineers from integration duties while giving businesses access to the data they need to build loyalty. Prior to founding Amperity, Kabir and Derek worked together to launch enterprise marketing management startup Appature, where Kabir was CEO and Derek was VP of engineering. Appature was purchased in 2013 by IT medical services provider IMS Health, which has since merged with Quintiles.

“Amperity has created the first-party data graph and customer toolset for many of the world’s most loved consumer brands. There’s a vast universe of solutions that might provide an element of what we do, but no one can deliver a more complete customer [view] with an agnostic approach we provide at both ends — data in, data out,” Shahani told VentureBeat via email.

Behind the scenes

Customer data platforms are collections of software that create persistent, unified customer databases accessible to other systems. Data is pulled from multiple sources, cleaned, and combined to create a single customer profile. This structured data is then made available to other marketing systems.

The customer data platform market is anticipated to be worth $10.3 billion by 2025, according to Markets and Markets, up from $2.4 billion in 2020. Amperity’s competitors include homegrown solutions, as well as legacy on-premises systems from Acxiom and Epsilon Independent and cloud-based services like ActionIQ and Redpoint Marketing.

But Shahani claims Amperity is the only customer data platform with an “AI-driven customer 360.” Each of its products is built on what the company calls the DataGrid, a fully connected customer infrastructure that processes terabytes of data each day. DataGrid streams billions of rows of data at under 100 milliseconds per API call, enabling Amperity’s AI models to provide deterministic and probabilistic individual and household identity throughout customer segments.

Amperity works with customers across retail, travel, hospitality, and financial services, including Patagonia, Alaska Airlines, Starbucks, The Home Depot, Lucky Brand, and Crocs. Recently, Amperity teamed up with a lifestyle retail client to better understand the company’s customers. In 18 weeks, Amperity says it built a dashboard with transaction, marketing, store, product, and privacy data, correcting lifecycle status for 25% of the client’s customers with interaction and revenue attribution. This led to $7.7 million in campaign revenue from predictive segmentation and churn prevention campaigns.

Amperity

Above: Amperity’s customer profile dashboard.

Image Credit: Amperity

Over the past year, Amperity’s recurring revenue grew nearly 200%, Shahani says, thanks in part to business from new customers Williams-Sonoma, Tapestry, Under Armour, Smithsonian, and Airstream.

“The market opportunity for consumer data platforms continues to grow at a staggering rate, with 34% compound annual growth rate over the next five years … With an expected $10 billion total available market in 2025, we believe this category to be the next big emerging technology sector — on par with enterprise resource management [and] customer relationship management — signaling the importance of customer data platforms in the broader enterprise software ecosystem,” Shahani added. “Salesforce, Adobe, and Oracle … have been slowly rolling out their version of a customer data platform, but so far, there’s no proof they have a tangible product.”

He added that Amperity is not phased by the competition. “They operate within a closed ecosystem, so while we technically compete, we also believe that we’re complementary because we can fit into the larger marketing technology stack.”

Amperity’s latest funding round was led by HighSage Ventures, with participation from existing investors Tiger Global Management, Declaration Partners, Madrona Venture Group, and MaderaTechnology Partners. The company has 225 employees, and it expects to grow its workforce to over 300 by the end of the year.

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AI

Nvidia launches $100M supercomputer for U.K. health research

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Nvidia is launching the $100 million Cambridge-1, the most powerful supercomputer in the United Kingdom, and it is making it available to external researchers in the U.K. health care industry.

The machine will be used for AI research in health care, and it’s one of the world’s fastest supercomputers. Nvidia will make it available to accelerate research in digital biology, genomics, and quantum computing.

Nvidia is collaborating with AstraZeneca, maker of one of the COVID-19 vaccines, to fuel faster drug discoveries and creating a transformer-based generative AI model for chemical structures. Transformer-based neural network architectures, which have become available only in the last several years, allow researchers to leverage massive datasets using self-supervised training methods, avoiding the need for manually labeled examples during pre-training.

Kimberly Powell, vice president of healthcare at Nvidia, said that AstraZeneca, GSK, Guy’s and St Thomas’ NHS Foundation Trust, King’s College London, and Oxford Nanopore Technologies (ONT) are using the supercomputer to develop a deeper understanding of brain diseases like dementia, using AI to design new drugs, and improve the accuracy of finding disease-causing variations in human genomes.

“This is an Nvidia industrial supercomputer owned and operated by Nvidia, and it’s the first one that we’re opening up to public use,” Powell said. “We believe that there is a massive opportunity in the area of health as all the stars have aligned. We’ve been working on simulations for 15 years and AI is having a rapid amount of progress. We know how to build these computers and use them to their maximum capacity better than anyone in the world. And some of the world’s best researchers are in health care.”

I asked Powell if Nvidia was doing this in the hopes of convincing European Union regulators that they should approve Nvidia’s $40 billion acquisition of Cambridge, England-based Arm. But she said the supercomputer project is unrelated to that and it has been in the works for a long time.

At No. 41 on the top 500 supercomputers list, the Cambridge-1 uses an Nvidia DGX SuperPod supercomputing cluster. Nvidia hopes it could have a global impact on health care around the world and contribute to the continuing efforts to fight COVID-19. On top of that, a report by Frontier Economics, an economic consulting firm, estimated that Cambridge-1 will have an economic value of £600 million ($831 million) over the next 10 years.

If it is successful, the Cambridge-1 could be a model for other industries or supercomputers in other regions as well. It’s like a reference design or showroom where Nvidia can show off the best of its technology and get more people to adopt it, Powell said.

Powell said that Nvidia’s CUDA designs and graphics processing unit technology have enabled Moore’s law to progress a million times over the past decade, rather than just a thousand times if Moore’s law was left to itself with the normal evolution of chips. AI models have also grown at an exponential rate with the success of network architectures and the ability to train large language models.

“Over the last 15 years, we’ve literally increased the progress of modeling computational biology by 10 million times,” Powell said. “So that rate of progress is what we’re calling the super exponential. And that gives a sense of why this is applicable in the area of biology and health. And so being able to have this level of computing to work with the leaders in the health care industry is what the supercomputer is all about.”

Data for AI to understand dementia

Above: GPUs in the Nvidia Cambridge-1.

Image Credit: Nvidia

King’s College London and Guy’s and St Thomas’ NHS Foundation Trust are using Cambridge-1 to generate synthetic image databases based on tens of thousands of MRI brain scans, from various ages and diseases. The goal is to use AI to gain a better understanding of diseases like dementia, cancer, and multiple sclerosis, enabling earlier diagnosis and treatment.

Early detection is imperative because existing medicines often are not able to treat these diseases given the severity of the neurological impact. This research will leverage the U.K.’s world-leading health care resources through close collaboration with the National Health Service and the UK Biobank, one of the richest biomedical databases in the world. King’s College London intends to share this dataset with the greater research and startup community.

Ian Abbs, CEO of Guy’s and St Thomas’ NHS Foundation Trust, said in a statement that AI in health care will speed up diagnosis for patients, improve services such as breast cancer screening, and support the way doctors assess risk for patients.

“It’s our investment to collaborate with the world’s leading health care institutions on large-scale computing problems,” Powell said. “Nvidia Cambridge-1 is an industrial supercomputer, and it’s going to be dedicated to AI and health care. What’s really cool about it is it’s built off of the Nvidia DGX SuperPod architecture.”

It has 80 DGX 80GB processors with eight ampere A100 tensor core GPUs with a total of 640 GPUs.

“What’s awesome about this is the DGX SuperPod architecture allows you to build datacenters in a matter of weeks,” Powell said. “The SuperPod architecture is a turnkey AI data center. We’ve already figured out the storage, networking, and compute cooling management tools because we have a digital twin of it called Seline, which is Nvidia’s industrial supercomputer.”

Most supercomputers take months or years to build. Nvidia wants to democratize AI computing for industry research and development, Powell said.

“We’re doing just that in health care. Not only is it a turnkey AI datacenter, it’s really a datacenter as a product,” Powell said. “The other really awesome feature to know about this is that it’s cloud native. And what that means is all the application development that Nvidia or our ecosystem does means that this system is going to get better over time. We can redeploy the full stack on these systems. So it’s going to get better over time.”

Scalable, real-time genomics

The Nvidia Cambridge-1 costs $100 million.

Above: The Nvidia Cambridge-1 costs $100 million.

Image Credit: Nvidia

ONT’s long-read sequencing technology is being used in more than 100 countries to gain genomic insights across a breadth of research areas, from human and plant health to environmental monitoring and antimicrobial resistance.

ONT deploys Nvidia technology in a variety of genomic sequencing platforms in the effort to create AI tools to improve not only the speed but the accuracy of genomic analysis. With access to Cambridge-1, researchers will be able to analyze the DNA samples in hours rather than days. That will help scientists gain more insights than ever before, said Rosemary Sinclair Dokos, vice president of product at ONT, in a statement.

The MegaMolBART drug discovery model being developed by Nvidia and AstraZeneca is slated to be used in reaction prediction, molecular optimization, and de novo molecular generation and will optimize the drug development process. It is based on AstraZeneca’s MolBART transformer model and is being trained on the ZINC chemical compound database — using Nvidia’s Megatron framework to enable massively scaled-out training on supercomputing infrastructure. This model will be open-sourced, available to researchers and developers in the Nvidia NGC software catalog.

Additionally, GSK is working with Nvidia to put their vast data sources to work toward the discovery of medicines and vaccines. GSK will use Cambridge-1 to help discover new therapeutics faster by combining genetic and clinical data for the next generation of drug discovery.

The Cambridge-1 has 80 Nvidia DGX A100 systems integrating Nvidia A100 GPUs, Nvidia BlueField-2 data processing units (DPUs), and Nvidia HDR InfiniBand networking. The Cambridge-1 is an Nvidia DGX SuperPod that delivers more than 400 petaflops of AI performance and 8 petaflops of Linpack performance. The system is located at a facility operated by Nvidia partner Kao Data, and it will use renewable energy.

Cambridge-1 is the first supercomputer Nvidia has dedicated to advancing industry-specific research in the U.K. The company also intends to build an AI Center for Excellence in Cambridge featuring a new Arm-based supercomputer, which will support more industries across the country.

“By improving the front end of the whole process, we’re going to definitely improve our chances of success and drug discovery going forward,” Powell said.

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Introhive raises $100M to automate customer relationship management

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Introhive, a software-as-a-service (SaaS) customer relationship management (CRM) automation platform, today announced that it raised $100 million in a series C funding round led by equity firm PSG. The funds, which bring the company’s total raised to over $140 million to date, will be put toward strategic acquisitions, the expansion of Introhive’s global footprint, and growth of the company’s engineering, sales, and marketing teams.

Forty-six percent of business-to-business (B2B) sales reps list lead quantity and quality as their top challenge. That’s perhaps why Gartner predicts that by 2025, 60% of B2B sales organizations will transition from experience- and intuition-based selling to data-driven selling, merging their sales process, sales applications, sales data, and sales analytics into a single operational practice. The stakes are high, given that suboptimal and delayed leads can have catastrophic effects. Harvard Business Review found that there’s a 10 times drop in lead qualification when reps wait longer than 5 minutes to respond and a 400% decrease when they respond within 10 minutes versus 5 minutes.

Founded in 2012, Introhive provides an AI-powered SaaS platform designed to help organizations identify customer relationships and data that might increase revenue and employee productivity. The company’s suite of solutions, which includes CRM software, marketing automation, and business intelligence technologies, syncs and enriches information from email (including email signatures) and other business systems and spotlights, scores, and maps “who knows who” across a business network in dashboards and regular digests.

Introhive

Above: The Introhive platform.

Image Credit: Introhive

“[Stewart Walchli and I] set out to create a platform that leverages AI and machine learning to automate mundane tasks for busy professionals and to identify valuable and previously unseen insights about their customers and business … [As Introhive has] grown, it has become apparent that this pain and challenge transcends beyond any one business or even industry,” cofounder and CEO Jody Glidden told VentureBeat via email. “The bottom line is that CRM is a big investment and the hard truth is people simply don’t take the time to use customer management relationship platforms properly, which hurts a business’ ability to grow and maintain their competitive edge.”

Growth potential

Salesforce’s 2019 State of Sales Report found that, on average, salespeople only spend 34% of their day selling products. Among the issues is the disconnect between enterprises’ need for a CRM and the fact that these platforms don’t always map to how salespeople work. According to a recent survey, one of the top barriers to CRM adoption is the amount of manual data entry required. Moreover, it’s estimated that sales professionals spend two-thirds of their office hours on administrative tasks like software management.

That’s perhaps why the CRM automation market is anticipated to be worth $96.5 billion by 2028, according to Grand View Research. Beyond Introhive, it encompasses platforms like Gong, Dooly, and Squelch.

“Throughout my career, relationships have been at the core of successful business growth and acceleration. Mapping those relationships has never been easy, and even with the key business systems like CRM and enterprise resource management, the task still isn’t straightforward or simple,” Glidden said. “Today, Introhive is helping leading enterprises in over 90 countries around the world successfully drive better adoption of their technology investments, eliminate mundane busywork for employees, while surfacing key business insights in real time to drive business growth.”

Introhive

Introhive’s customers include PricewaterhouseCoopers, Colliers International, and Wilson Sonsini Goodrich, as well as brands in industries ranging from technology and financial services to recruitment and professional services. The startup claims to have doubled its revenue during the pandemic, during which Introhive processed more than one trillion transactions, captured over 60 million contacts and relationships across more than 100,000 users (now approaching 250,000), and saved an estimated 9 million employee hours.

“One of our customers, Colliers International’s Canadian CRM team, generated 300% more relationships in Microsoft Dynamics across the firm’s over 500 brokers using Introhive’s relationship intelligence and CRM automation platform. [Colliers also had] 145,000 relationships mapped into Dynamics CRM across brokers [and] 33,200 email activities tracked and synced in CRM for [its] brokers,” a spokesperson told VentureBeat in a Q&A via email. “A primary differentiator is our ability to capture [a range of] new customers.”

The Business Development Bank of Canada, Evergreen Capital, and Mavan Capital Partners also participated in Introhive’s latest funding round. The company employs about 300 people across its 10 global office locations, and it intends to have around 400 by 2022.

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OpenAI launches $100M startup fund with Microsoft

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OpenAI today launched the OpenAI Startup Fund, a $100 million fund to, in the words of OpenAI, “help AI companies have a profound, positive impact on the world.” The fund is managed by OpenAI, with investment from Microsoft and other partners, and OpenAI says that companies selected for it will get early access to future OpenAI systems, support from OpenAI’s team, and credits on Microsoft Azure.

According to Sam Altman, CEO of OpenAI and the former president of Y Combinator, the OpenAI Startup Fund will make “big, early bets” on a relatively small number of companies, likely no more than 10. It’ll look to partner with early-stage startups in fields where AI can have a “transformative” effect — like health care, climate change, and education — and where AI tools can empower people by helping them be more productive, like personal assistance and semantic search.

“We think that helping people be more productive with new tools is a big deal. And we can imagine brand new interferences that weren’t possible a year ago,” Altman said. “We’re really excited about the opportunity for startups, for the industry and for people everywhere who can put AI to work improving their lives.”

Microsoft partnership

The OpenAI Startup Fund further extends Microsoft’s collaboration with San Francisco, California-based OpenAI. Roughly a year ago, Microsoft announced it would invest $1 billion in OpenAI to jointly develop new technologies for Microsoft’s Azure cloud platform and to “further extend” large-scale AI capabilities that “deliver on the promise” of artificial general intelligence. In exchange, OpenAI agreed to license some of its intellectual property to Microsoft, which the company would then package and sell to partners, and to train and run AI models on Azure as OpenAI worked to develop next-generation computing hardware.

In the months that followed, OpenAI released a Microsoft Azure-powered API that allows developers to explore GPT-3’s capabilities. And toward the end of 2020, Microsoft announced that it would exclusively license GPT-3 to develop and deliver AI solutions for customers, as well as create new products that harness the power of natural language generation.

This week, Microsoft announced that it would “deeply integrate” GPT-3 with Power Apps, its low-code app development platform — specifically for formula generation. The AI-powered features will allow a user building an ecommerce app, for example, to describe a programming goal using conversational language like “find products where the name starts with ‘kids.’”

Beyond Microsoft, GPT-3 is now being used in more than 300 different apps by “tens of thousands” of developers and producing over 4.5 billion words per day, according to OpenAI. A startup called Viable is using GPT-3 to analyze customer feedback, identifying “themes, emotions, and sentiment from surveys, help desk tickets, live chat logs, reviews, and more”; Fable Studio is leveraging the model to create dialogue for VR experiences; and Algolia is using it to improve its web search products.

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