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AI-powered voice transcription startup Verbit secures $250M

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Verbit, a startup developing an AI-powered transcription platform, today announced that it secured $250 million, bringing its total capital raised to $550 million. The round — a series E, made up of a $150 million primary investment and $100 million in secondary transactions — was led by Third Point Ventures with participation from Sapphire Ventures, More Capital, Disruptive AI, Vertex Growth, 40North, Samsung Next, and TCP.

With the fresh capital, Verbit, which is now valued at $2 billion, plans to expand its workforce while supporting product research and development as well as customer acquisition efforts. Beyond this, CEO Tom Livne said that Verbit will pursue further mergers and acquisitions and “provide enhanced value” to its media, education, corporate, legal, and government clients.

During the pandemic, enterprises ramped up their adoption of voice technologies, including transcription, as remote videoconferencing became the norm. In a survey from Speechmatics, a little over two-thirds of companies said that they now have a voice technology strategy. While they cited accuracy and privacy as concerns, 60% without a strategy said that they’d consider one within five years — potentially driving the speech and voice recognition market to $22 billion in value by 2022.

Livne cofounded New York-based Verbit with Eric Shellef and Kobi Ben Tzvi in 2017. Shellef previously led speech recognition at Intel’s wearables group, while Tzvi cofounded and served as CTO at facial recognition startup Foresight Solutions. As for Livne, who’s also a member of Verbit’s board, he was an early investor in counter-drone platform Convexum, which was acquired by NSO Group in 2020 for $60 million.

AI-powered transcription

Verbit’s voice transcription and captioning services aren’t novel — well-established players like Nuance, Cisco, Otter, Voicera, Microsoft, Amazon, and Google have offered rival products for years, including enterprise-focused platforms like Microsoft 365. But Verbit’s adaptive speech recognition tech can generate transcriptions that it claims achieve higher accuracy than its rivals.

Verbit users upload audio or video to a dashboard for AI-powered processing. Then, a team edits and reviews the material — taking into account customer-supplied notes and guidelines.

Finished transcriptions from Verbit are available for export to services like Blackboard, Vimeo, YouTube, Canvas, and Brightcove. A web frontend shows the progress of jobs and lets users edit and share files or define the access permissions for each, plus add inline comments, requesting reviews, or viewing usage reports.

“Verbit’s in-house AI technology detects domain-specific terms, filters out background noise and echoes, and transcribes speakers regardless of accent to generate … transcripts and captions from both live and recorded video and audio. Acoustic, linguistic, and contextual data is … checked by our transcribers, who [incorporate] customer-supplied notes, guidelines, specific industry terms, and requirements,” Livne told VentureBeat via email. “By indexing video content for web searches, Verbit [can help] companies improve SEO and increase their site traffic. [In addition, the platform can] provide audio visual translation to help global businesses with translations and to reach international audiences with their products and offerings.”

The transcriber experience

Like its competition, Verbit relies on an army of crowdworkers to transcribe files. The company’s roughly 35,000 freelancers and 600 professional captioners are paid in one of two ways, per audio minute or word. While Verbit doesn’t post rates on its website, a source pegs transcription pay at $0.30 per audio minute. Two years ago, transcription service Rev faced a massive backlash when it slashed minimum rates for its transcribers from $0.45 to $0.30 per word transcribed.

In some cases, pay can dip below $0.30 on Verbit, according to employee reviews on Indeed. The company reportedly started paying as low as around $0.24 cents per audio minute last year for a standard job.

Transcription platforms also don’t always have the technology in place to prevent crowdworkers from seeing disturbing content. In a piece by The Verge, crowdworkers on Rev said that they were exposed to graphic or troubling material on multiple occasions with no warning, including violent police recordings, descriptions of child abuse, and graphic medical videos.

A spokesperson told VentureBeat via email: “Currently, we employ a mix of full-time transcribers and captioners, as well as freelancers that are paid per audio minute. We’ve established a ranking system based on efficiency and accuracy to incentivize and reward freelancers with higher compensation in exchange for consistently delivering high-quality transcripts … The company’s transcribers have a support system — chat and forum — that constantly relays feedback to Verbit management, and it has a bonus program to ensure proper compensation for its top performers.”

The spokesperson continued: “In addition to competitive pay and opportunities for advancement, our staff of full-time transcribers and captioners are eligible to receive healthcare benefits … Our transcriber community follows a ranking system based on tenure and number of hours worked, allowing freelancers to earn promotions to roles such as editor, reviewer, and supervisor.”

On the subject of graphic content, the spokesperson said: “Verbit does not take on any business associated with violent or graphic content. For example, an adult entertainment company recently requested our services, but we chose not to accept them as a customer.”

Growth year

Verbit’s platform has wooed a healthy base of over 2,000 customers, bolstered by its acquisition of captioning provider VITAC earlier this year. In recent months, Verbit has pursued contracts with educational institutions like Harvard and Stanford, which have stricter accommodation standards than organizations in other sectors.

Auto captioning technologies on YouTube, Microsoft Teams, Google Meet, and like platforms aren’t beholden to the accommodations standards outlined in the Americans with Disabilities Act. In contrast, captioning must satisfy certain accuracy criteria in order to meet federal guidelines. A recent survey conducted by Verbit found that only 14% of schools provided captions as a default, while about 10% said that they only caption lessons when a student requests it.

Verbit also says that it’ll continue to explore verticals in the insurance, financial, media, and medical industries. The company — which currently has 470 employees, a number that it expects will grow to 750 by 2023 — recently launched a human-in-the-loop transcription service for media outlets and inked an agreement with the nonprofit Speech to Text Institute to invest in court reporting and legal transcription.

“With six times year-over-year revenue growth and close to $100 million in annual recurring revenue, Verbit continues to expand into new verticals at a hyper-growth pace. The shift to remote work and accelerated digitization amid the pandemic has been a major catalyst … and has further driven Verbit’s rapid growth,” Livne added. “In today’s digital era where audio and video content is a given, and many times the main method of conveying information, these AI tools are crucial to ensure that individuals and organizations of all sizes and forms can engage with their audiences and stakeholders more efficiently and effectively.”

Livne previously said that Verbit plans to file for an initial public offering in 2022.

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AI

Engineering software startup nTopology lands $65M

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more


NTopology, a company that develops software used in the design and manufacturing of 3D-printed parts and products, today announced that it raised $65 million in a series D round led by Tiger Global, bringing the company’s total financing to $135 million at a $400 million post-money valuation. Oldslip Group, Root Ventures, Canaan Partners, Haystack, and Insight Partners participated in the tranche, which cofounder and CEO Bradley Rothenberg says will be put toward expanding the types of apps nTopology serves in the product development process.

The pandemic accelerated digital transformation in the engineering sector. Shifts that likely would’ve happened over years instead happened within a matter of months, as engineers found themselves with more time to pilot different design software. In many respects, this was a positive development. According to a report by the Xpera Group, 95.5% of all data captured goes unused in the engineering and construction industry. In 2016, the World Economic Forum predicted that full-scale digitization within 10 years could lead to savings between $0.7 and $1.2 trillion in design, engineering, and construction and $0.3 and $0.5 trillion in construction operations.

nTopology 3.0 Review - DEVELOP3D

Founded in 2015 by Bradley Rothenberg and Greg Schroy, nTopology’s software helps engineers create parts with functional requirements built in. The company’s tools give its users control over spatial variations of shape, allowing for a range of designs and analyses.

“I founded nTopology … as I set out to find a design software solution that was specifically tailored to the needs of advanced manufacturing,” Rothenberg told VentureBeat via email. “NTopology’s digital engineering software, which combines generative design (for example, lattices and topology optimization), workflow management, and production preparation, is bridging the limitations of existing computer-assisted design (CAD) programs for the advanced manufacturing sector.”

Augmented CAD

According to Rothenberg, the main verticals nTopology operates in are aerospace and defense, health care, automotive, and consumer applications. The company services thousands of users across over 300 customers including Ford, Lockheed Martin, Honeywell, Emerson, and Wilson, which use its modeling systems to design the components of their products.

In less than a year, nTopology claims to have doubled revenue and increased its headcount to 120. The company anticipates increasing its employee count 150% by the end of 2022.

Looking ahead, Rothenberg says that nTopology will explore how AI and machine learning can assist engineers in discovering new product designs. “While advanced manufacturing opens a new and exciting design space for engineers, it doesn’t come for free,” he added. “One of the core uses of our new capital will be to expand our applications within our primary verticals and beyond as we head into Q1, 2022. The areas we plan to focus on include heat exchangers, medical implants, architectural materials, lightweighting, and industrial design.”

Boding well for nTopology and its competitors, which include Siemens NX, Catia, and Ansys, the global engineering software market is expected to exceed $46 billion by 2024.

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AI-driven background check startup Intelligo raises $22M

Intelligo, an Israeli startup providing AI-driven background check solutions to investors, financial institutions, and corporations, today announced it has raised $22 million from Felicitas Global Partners, one of its long-standing clients. The company said that the growth round, which takes its total fund-raise to $44 million, will drive the development of its AI-powered risk intelligence platform — Clarity — and help set up shop in additional geographies.

For years, due diligence processes such as background verification have been manual and time-consuming (typically taking 10-15 days). Agencies providing these services often struggle with delays and backlogs, leaving end-users — people requiring the due diligence — little option but to provide temporary clearances. In some cases, analysts can also miss out on important sources of information such as news reports.

Automated background checks

Founded in 2014 by Shlomo Mirvis and Dana Rakovsky, Intelligo tackles this problem through automation, claiming to bring speed and accuracy into the whole process. The company’s Clarity intelligence system employs multiple layers of AI and automation to comb through more than 400,000 public data sources and gather all relevant information (legal, professional, regulatory, financial, online media) required to make a decision on a particular individual or company.

Then, a team of in-house experts reviews this information to create the final report for the customer.

“Each client has an account where they can log in and run a report at any time from any place, share and collaborate with other team members, and manage the account in a very simple way,” Mirvis, who is also the CEO of the company, told VentureBeat. The dashboard gives a 360-degree view of the gathered data, with full information of the sources and links to original documents, allowing customers to vet potential red flags in the risk profile.

Currently, the platform returns reports between 30 minutes and five days, and it is used by hundreds of customers, primarily investment banks, private equity firms, and hedge funds with combined assets under management of over $500 billion. Plus, even after producing the report, Clarity can continue to monitor the target and provide alerts as and when something changes in its background profile.

“We use our AI algorithms to weed out irrelevant data and deduplicate data to keep only the most relevant data in the report and help individuals arrive at the right conclusions. Our AI also uses advanced natural language processing analysis to identify adverse news articles written about our subjects,” Mirvis said.

Competitive space

Intelligo is not the only one solving the problem of legacy background checks with technology. Over the past few years, a number of players have come up in the same space, including Checkr, HireRight, Kroll, K2 Integrity, Paycom, and Exiger.

“There are a number of companies in the background check and human resources technology space targeting different ends of the market. Most are focused on general checks for hiring at all levels — from minimum wage positions on up. Intelligo, however, is focused on the high end of the market for both pre-hire and pre-investment decision making by firms, as part of their risk intelligence,” Mirvis added.

Targeting accuracy and revenue growth

Moving forward, the company aims to double its headcount and expand the capabilities of its risk intelligence platform to draw more customers.

While the CEO didn’t share how exactly the company plans to improve the platform, he did note that Intelligo continues to take recommendations from clients and improve the accuracy of the solution. Part of the effort is likely to be driven toward covering an even broader base of data sources, as background check reports are only as good as the data they are based on. The company has seen 200% revenue growth over the past three years and expects to clock 300% growth in the current year.

“The last 18 months have been very positive for Intelligo as remote and hybrid work structures have made due diligence processes and recruiting even more essential. A lack of face-to-face interaction takes away the ability to make intuitive, personal connections, so organizations are even more reliant on getting accurate and thorough intelligence that provides a 360-degree view of who candidates really are,” Mirvis said.

Globally, the overall market for due diligence, pre-investment and pre-hiring, is estimated to be valued at $17 billion.

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Headless BI solution released by data startup Supergrain

A new data integration startup, Supergrain, today launched a headless business intelligence (BI) tool, designed for data teams who prefer interoperable, API-first solutions as they build out their modern data stacks.

The company, which was founded by George Xing, Supergrain’s CEO, and Thomas Chen, cofounder, and chief technology officer — both former engineering first hires at Uber and Lyft — also announced that it has raised $6.8 million in seed funding led by Benchmark.

“With the launch of our headless BI platform, we’re providing modern data teams with better tools to manage their metrics and analytic workflows,” Xing said. “Starting today, Supergrain’s API-first approach to BI will enable data teams to build trust in data and empower businesses to make data-driven decisions with confidence.”

The solution offers decision-makers a headless and interoperable BI solution that they can use to create a centralized repository of metric definitions that they can link to downstream tools, such as BI platforms and spreadsheets to create shared metric definitions.

Decision-makers often struggle with data analysis with traditional BI tools, like Microsoft Power BI, and Oracle BI, because there are no universal definitions of metrics. Metrics have different definitions within each tool and dashboard, making for a complex monitoring experience that reduces data reliability.

Supergrain helps address this challenge by providing a solution for creating universal metric definitions that the user can store in a centralized repository and then push to downstream applications.

API-first business intelligence 

A look inside code of Supergrain's business intelligence solution.

Above: A look inside the features of Supergrain’s business intelligence solution.

Image Credit: Supergrain

In an exclusive interview with VentureBeat, Xing explained that Supergrain enables teams to manage and integrate source-of-truth business metrics across all their data applications, and so provides a simple workflow for developing, testing, and publishing metric definitions.

Supergrain provides those sources of truth metrics via our APIs into the tools of their choice, be it BI tools, notebooks, custom data applications, or spreadsheet interfaces, Xing said.

Essentially, the user connects Supergrain to an existing data source, such as a database, produces a central definition for each metric, and then uses Supergrain’s API and query language (SGQL) to build workflows and analytics applications, so they can consume that data.

“Our approach is developer-centric, and what we call API-first, which means that we enable the data teams to define a single source of truth for metrics in a single repository, and then we expose all those metrics to the data consumers via APIs that they can query from anywhere, and so our approach is interoperable by design versus the traditional BI tools which are very locked in and application-specific,” Xing said.

Augmenting other BI providers 

Supergrain’s emphasis on accessibility is something many BI providers have tried to focus on over the past years to eliminate siloing, most notably with Tableau recently announcing a Slack integration for its BI platform and Microsoft Teams releasing an integrated Microsoft Power BI tab at the start of 2020.

However, while many of these providers attempt to create vendor-specific BI ecosystems, Supergrain has taken an interoperable approach with a solution that’s not intended to replace existing BI solutions but to augment them so that users can access insights generated by these tools via APIs.

Supergrain’s approach is different from many of these tools because it doesn’t seek to replace existing BI solutions; it seeks to enhance them and make them more accessible to users so that they’re not locked into a single application or solution to consume data.

While Supergrain is technically competing with other providers like Microsoft and Tableau that are building vendor-specific BI ecosystems, Supergrain is also compatible with them.

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Enterprise process automation startup Workato nabs $200M

Mountain View, California-based Workato, an enterprise process automation platform, today announced that it raised $200 million at a $5.7 billion valuation, bringing its total funding to over $420 million to date. The series E — which was led by Battery Ventures with equal participation from Insight Partners, Altimeter Capital, and Tiger Global — will be used to support global expansion as well as future mergers and acquisitions, according to CEO Vijay Tella.

Remote work, support of remote business, and other pandemic headwinds have prompted enterprises to adopt — or at least consider adopting — automation solutions. According to Forrester, 20% of companies will in the coming year expand their use of technologies like intelligent document extraction, which can extract and analyze data from a mix of digital and physical documents. The time and cost savings can be substantial — a 2017 study found that 53% of employees could save up to two work hours a day through automation, equating to 240 hours per year.

Founded by Alexey Timanovskiy, Dimitris Kogias, Gautham Viswanathan, Harish Shetty, and Tella in 2013, Workato lets companies integrate a range of data and apps to automate backend and front-end business workflows. The company’s platform delivers robotic process automation (RPA), integration platform-as-a-service, business process automation, and chatbot capabilities in a solution designed to enable IT and business teams to collaborate — ostensibly without compromising security, compliance, or governance.

“I was fortunate to be a part of the team that created the very first integration platform in the late 1980s and early 1990s. It was called The Information Bus, or TIB, and led to TIBCO and a wave of middleware technologies. I went on to become the founding SVP of engineering of TIBCO and was a part of the team that took TIBCO public. After that, I was the chief strategy officer of Oracle Fusion Middleware,” Tella told VentureBeat via email. “I, along with my cofounders Viswanathan and Shetty, built Workato in 2013 from the ground up. Our founding team has been largely involved in building some of the earliest integration platforms. We created a fusion of our past teams — people with completely different backgrounds that were deep in the consumer or cloud and integration space.”

With Workato, users can create automations from scratch or opt for over 500,000 prebuilt recipes addressing marketing, sales, finance, HR, IT, and other processes. The company says its over 11,000 customers and partners are creating over 500 new connectors to apps and systems each month.

Workato

Above: Creating a workflow automation recipe using Workato’s tools.

Image Credit: Workato

“For HR, for example, Workato can automate HR onboarding and offboarding. This includes automatically generating accounts for new hires in HR apps like Workday or Namely, monitoring benefits like time off, and triggering app provisioning or deprovisioning or hardware provisioning,” Tella said. “Workato also helps customers streamline mission-critical business processes in the finance department by automating the entire revenue process end to end — for example, product configuration, pricing, quoting, contracts, invoicing, billing, orders, revenue recognition, and renewals. [And Workato has been] supporting customers in building automations to help facilitate navigating the workplace during the pandemic.”

Workato also makes extensive use of AI and machine learning. As Tella explained to TechCrunch in a 2018 interview: “Leveraging the tens of billions of events processed, hundreds of millions of metadata elements inspected and hundreds of thousands of automations that people have built on our platform — we leverage machine learning to guide users to build the most effective [automation] by recommending next steps as they build these automations. It recommends the next set of actions to take, fields to map, auto-validates mappings, [and more]. The great thing with this is that as people build more automations — it learns from them and continues to make the automation smarter.”

In May at its Automate 2021 Conference, Workato introduced a number of new services including Automation HQ, a set of capabilities encompassing federated workspaces, a business operations console, lifecycle management tools, and custom communities for companies. Automation Accelerators, another product announced in May, delivers prepackaged solutions containing prebuilt recipes, custom connectors, reference data, and instructional guides.

Expanding platform

Workato competes with a number of companies in a workflow automation market that’s anticipated to be worth $18.45 billion by 2023, according to Markets and Markets. (From November 2019 to November 2020, over $2.2 billion in venture capital was funneled into tech companies building workflow automation solutions.) AirSlate offers products that automate repetitive enterprise tasks like e-signature collection. Tonkean is expanding its no-code workflow automation platform. There’s also Tray.io, Daylight, Leapwork, DeepSee.ai, Kore.ai, Aisera, and Berlin-based Camunda, each of which have closed funding rounds in the tens of millions for their process automation toolkits.

For Workato’s part, the company says that it more than doubled new annual recurring revenue, its headcount, and its customer base with additions like Stitch Fix, GitLab, NYU, Nokia, and Lucid Motors. Tella also noted that Workato recently acquired Chennai, India-based RailsData — which specializes in connectivity between apps, databases, and devices — to create what he describes as an “app connector factory,” with the goal of scaling the number of connectors on Workato by over 10 times in the next few years.

Workato — which has 650 employees — plans to focus its expansion efforts particularly in Europe, the Middle East, and Africa after it saw a 289% surge in usage over the past 12 months in the region. Beyond this, the company intends to open additional datacenters in Asia and add support for more regional languages.

“The pandemic has driven an even greater need for business excellence, and companies have responded by automating core workflows; seeking out low-code tools that empower employees to work quickly and autonomously. This investment arrives at a time of rapid growth for Workato and the automation market as a whole, as enterprises recognize the urgent need to increase their agility, innovation and efficiency against a backdrop of transformation and change,” Tella continued. “There’s really nobody that does quite what we do. Existing solutions either focus on integration and are too complex for business users, or they focus on automation and support only specific use cases.”

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Report: AI startup funding hits record high of $17.9B in Q3

Even as economies struggle with the chaos of the pandemic, the AI startup space continues to grow stronger with increased investments and M&A deals.

According to the latest State of AI report from CB Insights, the global funding in the segment has seen a significant surge, growing from $16.6 billion across 588 deals in Q2 2021 (figures show $20B due to the inclusion of two public subsidiary fundings) to $17.9 billion across 841 deals in the third quarter. Throughout the year (which is yet to end), AI startups around the world raised $50 billion across 2000+ deals with 138 mega-rounds of 100+ million. As much as $8.5 billion of the total investment went into healthcare AI, $3.1 billion went into fintech AI, while $2.6 billion went into retail AI.

The findings show how AI has become a driving force across nearly every industry and is drawing significant attention from VCs, CVCs, and other investors. In Q3 alone, there were 13 new AI unicorns globally, bringing the total number of billion-dollar AI startups to 119. Three startups also reached $2 billion in valuation — Algolia and XtaPi from the U.S. and Black Sesame Technologies from China.

Meanwhile, in terms of M&A exits, the quarter saw over 100 acquisitions like the previous one, putting the total exits for the year at 253. The biggest AI acquisition of the quarter was PayPal snapping up Paidly — a company determining creditworthiness using AI/ML — for $2.7 billion, followed by Zoominfo’s acquisition of Chorus.ai — a startup using AI to analyze sales calls — for $575 million.

U.S. AI startups continue to dominate

State of AI startup funding

Out of the $17.9 billion raised by AI startups worldwide in Q3, a significant $10.4 billion went to companies based in the U.S. and $4.8 billion into those in Asia. However, Asian firms raised this amount in nearly just as many deals (321) as in the U.S. (324), which signals that the average deal size was smaller there compared to U.S. Mega-round deals in the U.S. stood at 24 in Q3, while Asia saw 13 such deals.

Databricks, Dataiku, Olive, XtalPi, Datarobot, and Cybereason were the companies with the biggest rounds in the U.S. in the third quarter.

As compared to Asia and the U.S., funding in Canada, Latin America, and Europe regions was the lowest at $0.4 billion, $0.5 billion, and $1.6 billion, respectively. These regions cumulatively saw just eight mega-rounds.

Read the full report here.

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Robotics-powered ‘microfulfillment’ startup Fabric raises $200M

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Fabric, a startup developing a “microfulfillment” automation platform for retailers, today announced that it raised $200 million in series C funding led by Temasek with participation from Koch Disruptive Technologies, Union Tech Ventures, Harel Insurance & Finance, Pontifax Global Food and Agriculture Technology Fund, Canada Pension Plan Investment Board, KSH Capital, Princeville Capital, Wharton Equity, and others. With a valuation of over $1 billion and $336 million in capital raised to date, Fabric plans to expand its headcount and build a network of microfulfillment centers across major cities in the U.S.

According to McKinsey, ecommerce sales penetration more than doubled to 35% in 2020, the equivalent of roughly 10 years of growth within a few months. The surge in online shopping has been compounded by a desire for faster shipping — a tough ask in the midst of a pandemic. While the same-day delivery market in the U.S. is poised to grow by $9.82 billion over the next four years, a worldwide labor shortage — not to mention backups at critical ports of call — make the prospect daunting for merchandisers without economies of scale.

Fabric

Above: An isometric view of a Fabric fulfillment center.

Image Credit: Fabric

Fabric claims to level the playing field with a modular, software-led robotics approach to fulfillment. AI orchestrates robots within its microfulfillment centers’ walls to break orders into tasks and delegate them autonomously. Some robots bring items awaiting shipment in totes to teams of employees who pack individual orders. Operating in rooms with ceilings as low as 11 feet, other robots move packaged orders from temperature-controlled zones for fresh, ambient, chilled, and frozen products to dispatch areas, where they’re loaded onto a scooter or van for delivery.

Fabric’s customers choose either a platform model to run and operate independently on their real estate or a service model in which fulfillment is offered as a service with an investment.

“Fabric’s solution was designed from the ground up for local, on-demand ecommerce, which means it was designed to achieve high throughputs in small urban footprints, with low operational costs and maximum flexibility,” Fabric CEO Elram Goren told VentureBeat via email. “By combining our software, automated robotics, and logistics expertise, Fabric helps brands and retailers to future-proof their businesses with profitable unit economics. Robotics and automation bring a range of efficiencies to the ecommerce fulfillment space, increasing throughput per square footprint and decreasing the reliance on costly manual labor. Keeping fulfillment local speeds up delivery times while reducing shipping costs.”

Microfulfillment

Microfulfillment centers — located inside existing stores or structures that hold a market’s worth of goods — are increasingly being hailed as the answer to speedy shipping in space-starved city centers. For example, Calgary, Alberta-based Attabotics’ solution condenses aisles of warehouse shelves into single vertical storage structures that roving shuttles traverse horizontally.

As for Fabric, which was founded in 2015 and now employs over 300 people across its Tel Aviv, New York, and Atlanta offices, it’s among the most successful startups in the emerging segment. The company runs microfulfillment operations for grocery and retailers in New York City, Washington, D.C., and Tel Aviv and has partnerships with FreshDirect and Walmart as well as Instacart. For Instacart, Fabric plans to integrate its software and robotics solutions with Instagram’s technology and network of shoppers. And for Walmart, the company intends to add microfulfillment centers to dozens of store locations as part of a pilot involving other technology providers including Alert Innovation and Dematic.

“[W]e’re building our robots to be as robust and simple as possible from a hardware perspective, shifting the heavy lifting as much as possible to our software stack, to allow for scalability, lower costs, and robustness. At the same time, our software leverages our robotics architecture and topology, which allows it functionality and performance optimization opportunities that are unparalleled in the market,” Goren said.

In something of a proof of concept in December 2019, Fabric launched an 18,000-square-foot grocery site in Tel Aviv that’s now delivering orders to online customers. Fabric’s first sorting center, also in Tel Aviv, covers 6,000 square feet and services over 400 orders a day for drugstore chain Super-Pharm.

“We’re utilizing AI and machine learning in many different ways,” Goren added. “We have task resource allocation and planning that uses supervised machine learning to predict the duration, resource, and demand of each possible resource assignment which then works with other optimization algorithms such as genetic algorithms and Bayesian optimization. We enable retailers forecasting and prediction capabilities over their stock, to make sure they always have the right items in the right place at the right time. Stock level optimization is composed of two stages: First, time series forecasting predicts future demand for each product, and expected replenishment time. Second, an optimization algorithm maximizes stock availability for orders while minimizing the total costs of replenishment shipments and not exceeding available storage. These are just some of the software components that we’re continuing to develop.”

As logistics and fulfillment challenges continue to mount, companies are embracing automation across the entire supply chain. According to one estimate, 4 million commercial warehouse robots are to be installed in over 50,000 warehouses by 2025. Amazon alone uses over 350,000 autonomous robots to automate order fulfillment, the company recently reported.

The concept is catching on particularly quickly among grocers and convenience stores with small delivery radiuses. On-demand food and goods startup Gopuff employs hundreds of microfulfillment centers in its delivery network. And Kroger, Albertsons, and H-E-B are using — or actively exploring — microfulfillment for online customers.

Fabric rival Attabotics raised $25 million in July 2020 for its robotics supply chain tech, and InVia Robotics last summer nabbed $20 million to bring its subscription-based robotics to ecommerce warehouses. Softbank recently invested $2.8 billion in robotics and microfulfillment company AutoStore. In the European Union, supermarket chain Ocado deployed a robot that can grasp fragile objects without breaking them. And startup Exotec has detailed a system called Skypod that taps robots capable of moving in three dimensions.

“[The pandemic] has changed very little, really, and at the same time — it accelerated everything. People still like to get more, pay less, and get it faster. Retailers still like to sell more and make more. But there has been a leap of a decade in this past year, and this is what we’re seeing. COVID caught retailers and brands off guard and has forced them to move much faster than they had planned for,” Goren said.

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Intent-based networking startup Gluware secures $43M to automate repetitive tasks

Gluware, a company developing network automation tools for cloud environments, today announced that it raised $43 million in funding led by Bain Capital with participation from Acadia Woods Partners and existing investors. The mix of debt and equity — which values the company “well into the nine figures,” according to a source — will go toward scaling the company’s sales and marketing teams and product R&D, according to CEO Jeff Gray.

It’s estimated that networking practitioners spend up to 55% of their time and resources on repetitive equipment management tasks. According to Cisco, around 95% of network-related tasks are performed manually, causing operational costs of around 2 to 3 times more than the cost of the network. That’s why an increasing number of companies are turning to automation, which promises to free up IT teams while potentially reducing expenses. A 2019 survey found that nearly two-thirds of companies plan to deploy systems that will help them analyze network issues using automation and AI.

“The network … market is complex and fragmented, with legacy tools that cater to highly bespoke static configurations, small-scale single-vendor solutions, or do-it-yourself coding approaches that require significant resources. These approaches break under the scale, scope, and complexity of modern enterprise networks,” Gray told VentureBeat via email.

Founded in 2007 by Gray and Olivier Huynh Van as Glue Networks, Gluware offers a code-free, intent-based networking service for enterprise organizations. This design supports network equipment audits while helping to identify network changes, patch multivendor devices, and perform error checks and automatic remediation. (Intent-based networking, an emerging software category, deals with the planning and deployment networks that can improve availability, providing lifecycle management for infrastructure.)

Gray and Van met in London working for a managed service provider, where they developed an engine capable of automating Cisco-specific wide area networks, local area networks, datacenter, and cloud environments to reduce outages. This became the cornerstone of Gluware’s platform, which eventually expanded to support to multivendor, multi-domain network automation.

“Mounting pressure is forcing businesses to address manually induced outages, security breaches, failed audits, and the inability to provide network services to lines of business on required timelines. Yet, the magnitude and heterogeneity of the network automation problem make it harder to tackle than the markets above. The software layer needed to solve the network automation problem must automate any enterprise network design, any vendor, any device, and any custom configuration,” Gray said. “Gluware’s software acts as a robot network engineer in the cloud and engages in intelligent two-way communication with the most sophisticated network devices, keeping them in compliance and maintaining them in a safe and predictable manner.”

Automating network tasks

Gluware, with Fortune 500 and Global 2000 customers like Mastercard and Terracon, automatically turns running network configurations into policy. This keeps devices up to date with software upgrade orchestration. The company claims its discovery engine can index and inventory multivendor networks of almost any size, completed with a programmatic interface architected to integrate into third-party platforms via an API.

One Gluware customer — Merck — claims to have cut the time spent on global network configuration changes by 98%. Before adopting Gluware, Merck took up to nine months to manually tune its cloud applications.

“Gluware understands network configurations at a per character resolution level, abstracts the myriad configurations into overall data-model-driven policies, and maintains historical knowledge of every configuration change,” said Gray, who added that the company’s next phase of development will focus on AI capabilities such as robotic process automation to deliver “self-operating, self-remediating” networks for customers. “With the advent of Gluware’s data collection AI and machine learning platform and application, Gluware [will have] an unfair advantage in determining if the change was a good change or a bad change, providing recommended changes to IT, and delivering on self-operating use cases.”

The datacenter automation segment is expected to rise from $3.16 billion in 2014 to $7.53 billion in 2019, according to a Markets and Markets report. And the  industry remains red-hot, as evidenced by Juniper Networks’ purchase of intent-based networking startup Apstra last December. Gartner predicts automation of 60% of datacenter networking configuration activities, up from 30% in early 2020.

As for 61-employee Gluware, the company says it’s on track for 400% annual recurring revenue growth this year. To date, Gluware has raised $90 million in total capital.

“The pandemic has made Gluware automation a must-have. Network automation has increased in value since the start of the pandemic, as IT staff need more remote management and zero-touch provisioning capabilities, the ability to make changes with confidence for new traffic patterns to manage work from home initiatives, and the need to extend the life of current network infrastructure during supply chain disruptions,” Gray said. “Customers that implemented Gluware on their production networks have found tens of thousands of hidden security violations, hundreds of unapproved operating system versions, and dangerous network misconfigurations — all of which have caused or have the potential to cause major network outages or security events, resulting in loss of business continuity.”

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AI model optimization startup Deci raises $21M

Tel Aviv, Israel-based Deci, a company developing a platform to optimize machine learning models, today announced that it raised $21 million in a series A round led by Insight Partners with participation from Square Peg, Emerge, Jibe Ventures, Samsung Next, Vintage Investment Partners, and Fort Ross Ventures. The investment, which comes a year after Deci’s $9.1 million seed round, brings the company’s total capital raised to $30.1 million and will be used to support growth by expanding sales, marketing, and service operations, according to CEO Yonatan Geifman.

Advancements in AI have led to innovations with the potential to transform enterprises across industries. But long development cycles and high compute costs remain roadblocks in the path to productization. According to a recent McKinsey survey, only 44% of respondents reported cost savings from AI adoption in business units where it’s deployed. Gartner predicts that — if the current trend holds — 80% of AI projects will remain “alchemy,” run by “[data science] wizards” whose talents “will not scale in the organization.”

Deci was cofounded in 2019 by Geifman, Ran El-Yaniv, and entrepreneur Jonathan Elial. Geifman and El-Yaniv met at Technion’s computer science department, where Geifman was a PhD candidate and El-Yaniv a professor. By leveraging data science techniques, the team developed products to accelerate AI on hardware by redesigning models to maximize throughput while minimizing latency.

“I founded Deci in 2019 with Professor Ran El-Yaniv and Jonathan Elial to address the challenges stated above. With our talented team of deep learning researchers and engineers, we developed an innovative solution — using AI itself to craft the next generation of AI. By utilizing an algorithmic-first approach, we focus on improving the efficacy of AI algorithms, thus delivering models that outperform the advantages of any other hardware or software optimization technology,” Geifman told VentureBeat via email.

Deci achieves runtime acceleration on cloud, edge, and mobile through data preprocessing and loading, automatically selecting model architectures and hyperparameters (i.e., the variables that influence a model’s predictions). The platform also handles steps like deployment, serving, and monitoring, continuously tracking models, and offering recommendations where customers can migrate to more cost-effective services.

“Deci’s platform offers a substantial performance boost to existing deep learning models while preserving their accuracy,” the company writes on its website. “It designs deep models to more effectively use the hardware platform they run on, be it CPU, GPU, FPGA, or special-purpose ASIC accelerators. The … accelerator is a data-dependent algorithmic solution that works in synergy with other known compression techniques, such as pruning and quantization. In fact, the accelerator acts as a multiplier for complementary acceleration solutions, such as AI compilers and specialized hardware.”

AutoNAC

Machine learning deployments have historically been constrained by the size and speed of algorithms, as well as the need for costly hardware. In fact, a report from MIT found that machine learning might be approaching computational limits. A separate Synced study estimated that the University of Washington’s Grover fake news detection model cost $25,000 to train in about two weeks, and Google spent an estimated $6,912 training BERT.

Deci

Above: Deci’s backend dashboard.

Image Credit: Deci

Deci’s solution is an engine — Automated Neural Architecture Construction, or AutoNAC — that redesigns models to create new models with several computation routes, optimized for an inference device and dataset. Each route is specialized with a prediction task, and Deci’s router component ensures that each data input is directed via the proper route.

“[O]ur AutoNAC technology, the first commercially viable Neural Architecture Search (NAS), recently discovered DeciNets, a family of industry-leading computer vision models that have set a new efficient frontier utilizing only a fraction of the compute power used by the Google-scale NAS technologies, the latter having been used to uncover well-known and powerful neural architectures like EfficientNet,” Geifman said. “Such models empower developers with what’s required to transform their ideas into revolutionary products.”

The thirty-employee company, Deci, recently announced a strategic collaboration with Intel to optimize AI inference on the chipmaker’s CPUs. In addition to Intel, the startup says that “many” companies in autonomous vehicle, manufacturing, communication, video and image editing, and health care have adopted the Deci platform.

“Deci was founded to help enterprises maximize the potential of their AI-based solutions. Enterprises that are leveraging AI face an upward struggle, as research demonstrates that only 53% of AI projects make it from prototype to production,” Geifman said. “This issue can largely be attributed to difficulties navigating the cumbersome deep learning lifecycle given that new features and use cases are stymied by limited hardware availability, slow and ineffective models, wasted time during development cycles, and financial barriers. Simply put, AI developers need better tools that examine and address the algorithms themselves; otherwise, they will keep getting stuck.”

Deci has competition in OctoML, a startup that similarly purports to automate machine learning optimization with proprietary tools and processes. Other competitors include DeepCube, Neural Magic, and DarwinAI, which uses what it calls “generative synthesis” to ingest models and spit out highly optimized versions.

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Compliance and risk management startup Hyperproof lands $16.5M

Hyperproof, a compliance software provider, today announced that it raised $16.5 million in series A funding led by Toba Capital at a $72.5 million post-money valuation. The company says that it’ll use the new capital to support product R&D and grow its headcount as it looks to expand to new markets.

The need for risk and compliance management solutions is increasing as companies move their operations to digital channels. More than 900 regulatory agencies issued a combined more than 200 regulatory updates every day, on average, in 2017. Unsurprisingly, many enterprises don’t feel equipped to address the challenges from a talent or technology standpoint. According to a survey from The Financial Times Group, 57% of senior-level executives rank “risk and compliance” as one of the top two risk categories they’re least prepared to address.

Hyperproof was founded in 2018 by Craig Unger, whose previous startup — cloud-based automation company Azuqua — was acquired by Okta in 2019. Hyperproof offers a software-as-a-service (SaaS) compliance operations platform that lets customers track and manage organizational risks, such as vendor risks, on a continuous basis. Using collaboration tools and workflows as well as integrations with cloud services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, team members can leverage Hyperproof to complete tasks like evidence collection, dashboard creation, and reporting for more than 50 frameworks including FedRAMP, SOX, and CSA.

Hyperproof

Above: Hyperproof’s compliance dashboard.

Image Credit: Hyperproof

“The pandemic has really underscored the importance of maintaining strong security programs and compliance programs to organizations’ leaders, because it has made a number of risks more prominent and introduced new risks,” Unger told VentureBeat via email. “For instance, as [society] shifted to mass remote work, security teams had to maintain cybersecurity under such a radically different environment and implement new security controls. Moving to remote work meant that compliance work also had to be done remotely. Because Hyperproof supports … collaboration between compliance professionals and business units, compliance professionals found it to be a valuable platform that helped them get their work done efficiently.”

Applying AI to compliance

Hyperproof supports compliance projects and comes with a risk register and a place for tracking an organization’s vendors and conducting vendor assessments. Using Hyperproof, companies can document risk mitigation plans, map risks to controls and compliance requirements, and review software and technology vendors to assess their compliance and security posture.

“[Hyperproof can map] between different compliance standards and frameworks, so work such as control design, implementing and testing, and evidence gathering can be streamlined as much as possible,” Unger said. “[The platform keeps] people accountable for completing security and compliance tasks, [streamlines] audit preparation and efficiently comply with multiple standards and frameworks, [and] automate[s] evidence collection and testing.”

In the coming months, Hyperproof plans to launch and environmental, social, and governance software product designed to help organizations manage their businesses more sustainably while meeting reporting requirements. Beyond this, the 51-employee startup is investing heavily in automation solutions powered by AI and machine learning technologies.

“Hyperproof is planning to leverage machine learning to help our users automatically identify and flag the overlapping requirements across various compliance frameworks, so they can see areas where they’re already meeting requirements and reuse their compliance artefacts to satisfy new requirements,” Unger said. “[In addition, we] will use [AI] to address an area of compliance that’s really tedious: Gathering [compliance] evidence from different systems to show auditors that internal controls meant to enhance security and mitigate risks were operating correctly and effectively … In 2022, we plan to [tap] machine learning to automatically identify opportunities for our users to set up integrations that will pull in compliance data from third-party systems into Hyperproof.”

Hyperproof

In addition, Hyperproof aims to develop algorithms that intelligently scan the content of evidence attached to internal policies set up in a company’s account. The algorithm will review the evidence against a specific user-defined parameter or value range to see if the evidence falls within or outside of that acceptable range. According to Unger, Hyperproof is also exploring machine learning to help users gauge how prepared they are for an upcoming compliance audit. The goal is to analyze user activities like how many policies have been implemented and how many requirements have functioning controls linked to them to generate a preparedness score that’ll be updated in real time.

“We plan to leverage machine learning in our products in several different ways, both to eliminate much of the repetitive work compliance professionals have to deal with today and to surface meaningful risk insights to users so that they can make better, more strategic decisions,” Unger continued. “This will not only save compliance professionals hours every month — it will also provides organizations’ leaders peace of mind knowing that the system automatically detects issues as soon as they occur and alerts the right people.”

Bellevue, Washington-based Hyperproof competes with Vanta, LogicGate, MetricStream, and others in a global enterprise governance, risk, and compliance solutions market that was estimated to be worth $35.1 billion in 2020, according to Grand View Research. But Hyperproof claims it’s on track for 400% year-over-year growth thanks to contracts with clients including Sophos, ForgeRock, AppGate, Fortinet, 3M, and Motorola Solutions.

Existing investors participated in Hyperproof’s latest round, which brings the company’s total raised to $26.5 million. Hyperproof previously closed three seed rounds totaling $10 million.

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