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Nerdio raises $8 million to help businesses manage Microsoft Azure deployments

February 11, 2020 Big Data

Managing a cloud environment takes time, capital, and plenty in the way of domain expertise. This reality is the driving force behind a category of startups developing tools to automate the deployment and orchestration work — the managed services market was valued at around $ 186 billion in 2019, with forecasts suggesting that it could grow to $ 356.24 billion by 2025.

One contender in the space is Nerdio, which offers a product for managed service providers to build and grow their cloud practices in Microsoft’s Azure. This morning, the Chicago, Illinois-based company announced that it has closed an $ 8 million series A round led by MK Capital, with participation from Nerdio’s own cofounder and CEO, Vadim Vladimirskiy, and chief revenue officer, Joseph Landes. The infusion brings Nerdio’s total raised to over $ 10 million, following a $ 2.4 million round in February 2014. Vladimirskiy says it’ll be used to further develop existing products while expanding operations and supporting partners.

“Public cloud adoption is on the rise. It’s not slowing down anytime soon, and neither are we,” said Vladimirskiy in a statement. “We have evolved alongside the cloud industry — starting out as a [managed service provider], offering a private cloud business, and now being all-in on Azure. Nothing illustrates our commitment to the cloud, Microsoft, and our growing impact on both more than this financial commitment to propel Nerdio’s path forward.”

Nerdio is a subsidiary of Adar, which Vladimirskiy, Stuart Gabel, and Niall Keegan founded in 2005 to provide online backup systems to small and mid-sized enterprises. Soon after it was founded, Adar transitioned into managing virtual infrastructure, which is how Nerdio and its services came about.

Nerdio’s solutions suite for Azure lets admins automatically provision an IT environment and manage their clients from a single portal. A cost estimator prices out products and services prior to deployment, while an autoscaling algorithm ensures clients only pay for what they use. From an app for iOS and Android, credential users can access customer information and view server logs or perform tasks like editing and removing groups, configuring shared mailboxes, and stopping or starting remote servers. They’re also able to clone existing virtual machines to Azure and inventory environments for migration and automatically invoice customers for both resources used and services rendered.

Nerdio also offers the eponymous Nerdio Private Cloud, a service that delivers IT support and infrastructure, including virtual hardware, software, security backup and disaster recovery. It creates a virtual server infrastructure in a cloud-hosted environment — access to which is provided through Virtual Desktop Infrastructure (VDI) — and an image to standardize the virtual desktop environment, including all applications needed to access network resources. All of its software is collated and managed by Nerdio and its partners, including patches, upgrades, and compatibility tests.

“We have deep experience in the software and cloud infrastructure sectors and interact with plenty of emerging and innovative startups in ITaaS, IaaS and DaaS — few of which are as impactful and impressive as Nerdio and their Nerdio for Azure offerings,” said MK Capital’s Bret Maxwell. “Rarely do we have the chance to invest side by side with executive leaders like Vadim and Joseph who believe so strongly in their company’s mission and trajectory, which only strengthens our viewpoint that it is an incredibly exciting time to propel Nerdio’s growth forward.”

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Big Data – VentureBeat

Cherre raises $16 million to analyze real estate data with AI

February 5, 2020 Big Data

Real estate data collection and analytics costs can total in the millions of dollars. That’s why in 2016, L.D. Salmanson founded Cherre, a startup that leverages AI to cost-effectively resolve property data from disparate public and private sources. After raising $ 9 million in October 2018, the New York-based company today announced that it’s snagged $ 16 million in venture and debt funding led by Intel Capital, with participation from Navitas Capital, Carthona Capital, Zigg Capital, Dreamit Ventures, and Silicon Valley Bank.

“Last year was marked by incredible growth for the entire market,” said Salmanson, who noted that this latest capital infusion brings Cherre’s total raised to $ 25 million. “This massive industry migration towards fully integrated data systems is just starting, and we’re incredibly proud to be leading the charge. We look forward to continue working with our most demanding clients on their mission-critical data needs.”

Cherre’s technology is modeled after high-frequency trading platforms, and as such, it continuously organizes and updates internal and external databases. With the help of machine learning models that programmatically identify and index real estate data, it provides relevant info on demand in a clean and readable format.

Cherre’s software-as-a-service (SaaS) suite consists of several core components: CoreConnect, CoreAugment, CoreExplore, and CorePredict. The first three fall under the category of data management — they integrate with new and existing on-premises and cloud stores to provide location, transportation, social, and demographic information via APIs, which get compiled into dashboard visualizations. Explore has thousands of filters including custom queries, which support programmatic overlays and help to identify property owners behind corporations.

As for CorePredict, it taps AI supervised by asset managers and underwriters to source and evaluate new opportunities. In addition to automated asset evaluation and recommendation, the tool handles automated risk analysis and mitigation, as well as return on investment modeling and real-time forecasting.

Cherre has one of the largest real estate knowledge graphs in the world, according to Salmanson, with over 315 unique data sets and two billion data points pertaining to 177 million properties and 84 million companies. Its predictive models take into account property characteristics like amenities, materials, and zoning; community and demographics such as population, languages, and schools; recorder and deed transaction information; lien and mortgages; geospatial information; boundaries; tax and assessment; and valuations derived from over 450 million transactions across 100 million properties.

“The global real estate industry is undergoing a transformation, catalyzed by massive data flows and the application of artificial intelligence. Despite its substantial impact on the global economy, this sector is still in its infancy when it comes to data-centric investing and underwriting decisions,” said Intel Capital vice president Trina Van Pelt. “We see Cherre as critical infrastructure to accelerate the future of this industry. Our customer diligence repeatedly indicated Cherre’s AI-enabled platform was a foundational pillar — a data system of record for large enterprises across the real estate segment. We’re excited to help accelerate Cherre’s global growth trajectory.”

Cherre might not be the first in the $ 8.5 trillion real estate market with sophisticated AI-powered analytics tools — properties startup Compass recently raised $ 400 million for its big data platform, following hot on the heels of Skyline‘s $ 18 million round and other raises by HomeLight, Reonomy, and Geophy. But it has managed to attract a few notable Fortune 500 clients, including The Real Estate Board of NY (REBNY), Keller Williams NYC, Stratus Data Systems, August Partners, and Platinum Properties.

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Big Data – VentureBeat

Iguazio raises $24 million for AI development and management tools

January 27, 2020 Big Data

AI adoption levels are higher than they’ve ever been in the enterprise. According to a January survey conducted by Gartner, corporate use of AI grew 270% over the past four years. But developing, deploying, and managing AI applications at scale requires a platform that supports doing so, which is what startup company Iguazio provides. Its investors believe it has legs: Iguazio announced that it has secured $ 24 million in a funding round led by INcapital Ventures with participation from existing and new investors including Samsung SDS, Kensington Capital Partners, Plaza Ventures, and Silverton Capital Ventures, bringing its total raised to around $ 75 million following a $ 33 million series B in July 2017.

“This is a pivotal time for AI. Our platform helps data scientists push the limits of their real-time AI applications and see their impact in real business environments,” said CEO Asaf Somekh, who cofounded Iguazio in 2014 with former senior software developer and architect Orit Nissan-Messing and Voltaire colleagues Yaron Haviv and Yaron Segev. “With support from INcapital, Kensington Capital Partners, and our other investors, we are ready to expand our international team and reach our ambitious goals.”

Iguazio’s product suite collects data and preps it offline or offline, accelerating and automating AI model training for deployment via APIs. Event-driven streaming, time series, NoSQL, SQL, and files are among the types supported, which can be explored and manipulated by a real-time data layer that lets admins use a variety of protocols and concurrently read the data with third-party analytics and data science frameworks.

Nuclio, Iguazio’s open source serverless framework, streamlines machine learning pipeline steps like packaging, scaling, tuning, instrumentation, and continuous delivery with features like rolling upgrades, A/B testing, logging, and monitoring. Its MLRun open source framework parallelizes work within a single pod by wrapping engines around tools like Spark, Google’s Tensorflow, Horovod, and Nuclio, and it handles a range of triggers including HTTP and cron.

The Iguazio suite — which runs as in-memory databases on flash memory — continuously trains models in a production-like multi-cloud or hybrid cloud environment, dynamically scaling graphics cards, processors, and memory and automatically tracking code, metadata, inputs, and outputs of executions in a reproducible fashion. (Admins can track the elements of all running jobs as well as historical jobs and store them in a single report.) The platform can run multiple experiments simultaneously and select the best model, and it facilitates the migration of this model from an integrated development environment to production.

Enterprise customers get an integrated offering they set up through wizards that help configure administration policies and notifications. Managers securely share data by providing direct access to it, so that employees are exposed to different elements according to predefined rules. The data layer classifies transactions with a built-in firewall that provides fine-grained policies to control access, service levels, multi-tenancy, and data life cycles, and that enables organizations to collaborate and govern across apps and business units without compromising security or performance.

There’s no shortage of orchestration platforms in the over $ 1.5 billion global machine learning market. Amazon recently rolled out SageMaker Studio, an extension of its SageMaker platform that automatically collects all code and project folders for machine learning in one place. Google offers its own solution in Cloud AutoML, which supports tasks like classification, sentiment analysis, and entity extraction as well as a range of file formats, including native and scanned PDFs. Not to be outdone, Microsoft recently introduced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps, and IBM has a comparable product in Watson Studio AutoAI.

That’s not to mention rival startups like San Francisco-based Databricks and Explorium, which provide suites of enterprise-focused scalable data science and data engineering tools. But Somekh is confident in the robustness of the Iguazio platform, which he claims prevents 60% of faults and serves up to 500 real-time recommendations per second.

To this end, one new customer — digital payment platform Payoneer — recently deployed a proactive fraud prevention solution across four million accounts using Iguazio’s tools.

“Iguazio’s unique technology facilitates the data science creation process from start to finish, enabling enterprises to deploy AI applications that create real business impact,” said Kensington Capital Partners chairman Tom Kennedy. “With the opportunities we are seeing for machine learning technology and the global success stories emerging from this high-tech nation, Iguazio represents a great first investment for Kensington in an Israeli company.”

Iguazio is based in Herzliya, Israel, and has offices in the U.S., U.K., and Singapore.

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Big Data – VentureBeat

AISense raises $10 million for AI transcription tool Otter

January 24, 2020 Big Data

There’s plenty in the way of competition with respect to the audio transcription market, which is estimated to be worth $ 31.82 billion by 2025. But AISense — the startup behind speech-to-text service Otter — managed to carve out a niche for itself in the four years since its founding. Its funding reflects this — the company today confirmed to VentureBeat that it’s raised $ 10 million in a financing round led by strategic backer NTT Docomo (specifically Docomo Ventures, its venture arm), with participation from Fusion Fund, GGV, Dragon Dragon Fund, Duke University Innovation Fund, Harris Barton Asset Management, Slow Ventures, and others. It brings AISense’s total venture capital raised to $ 23 million following previous rounds totaling $ 13 million.

As part of NTT DOCOMO’s investment, the Tokyo-based mobile phone operator says it’s piloting Otter in language education and leadership training company Berlitz’s English language classes in Japan. According to AiSense founder and CEO Sam Liang, students there are using Otter to transcribe and review the content of lessons, click on sections of text, and start voice playback.

“Otter is pleased to partner with market leader DOCOMO to bring innovative business collaboration offerings to Japan that transcend language and transform the way people work,” said Liang in a statement. “DOCOMO’s support and commitment have enabled Otter to quickly address business needs in Japan for a highly accurate meeting note taking collaboration app.”

AISense raises $10 million for AI transcription tool Otter

Above: Top growth (Pokémon GO vs. Harry Potter Wizards Unite)

The capital infusion comes nearly a year after AISense brought Otter to the education market with Otter for Education, which affords school instructors control over access to recorded transcripts and complements student disability services and accessibility technologies. More recently, AISense launched Otter for Teams, a subscription solution designed to accommodate the needs of small- and medium-sized businesses with account management, provisioning, reporting, and other such features, as well as single sign-on support and collaborative tools that let users highlight bits of conversations.

Otter’s core technology, which was developed by a team hailing from Google, Yahoo, Facebook, MIT, Stanford, Duke, and Cambridge, is optimized for conversations. It can distinguish among speakers using a technique called diarization, generating a unique print for each person’s voice. Transcriptions are processed in the cloud and made available from the web, in Dropbox, or in Otter’s mobile app for iOS and Android devices, where they can be searched, copied and pasted, scrolled through, edited, and shared. A word cloud at the top of each recording tracks the most-used terms.

Otter competes with Microsoft’s Microsoft 365, which can host live events with AI-powered features such as facial recognition of attendees and autonomous speech-to-text conversion, as well as comparable meeting transcription tools from Cisco and startups Voicera, Verbit, Trint, Simon Says, and Scribie.

But it seeks to differentiate its service with competitive pricing. Otter for Teams starts at $ 12.50 per month, or only slightly pricier than its premium and student tiers — those cost $ 10 per month and $ 5 per month, respectively. (AISense offers a free plan with 600 minutes of transcription per month and unlimited cloud storage.) It’s also licensing its tech to third parties like video conferencing company Zoom, which enables conversations recorded by Zoom users to automatically upload to Otter.

The growing list of organizations who’ve piloted or are actively using Otter includes Vice’s Virtue Worldwide creative agency, auditing firm Moss Adams, design and production agency BRC Imagination Arts, property tech firm Amne, Bridgewater Associates, Tulane University, Western Kentucky University, the University of California-Los Angeles, and Harvard University.

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Big Data – VentureBeat

TypingDNA raises $7 million for AI that identifies people by how they type

January 5, 2020 Big Data

The risk of online accounts being compromised increases by the day — and it’s costly. A hacker attacks roughly every 39 seconds, and it’s estimated that 65% of companies have over 1,000 stale accounts. Worse still, the average expense of data breaches is expected to hit $ 150 million in 2020.

That’s perhaps why startups like TypingDNA have risen to prominence. TypingDNA — which was founded in Romania in 2016 by Adrian Gheara, Cristian Tamas, and Techstars alum Raul Popa, and which recently moved its headquarters to New York — provides typing biometrics authentication as a service, enabling companies to recognize people by the way they type. After raising $ 1.8 million in capital from the founders of companies such as UiPath and DataDog last year and attracting clients like large banks in Latin America and Romania, it’s gearing up for a major expansion in 2020.

TypingDNA announced this morning that it has raised $ 7 million in series A funding led by Gradient Ventures, Google’s AI-focused venture fund, with participation from Techstars Ventures and EU-based fund GapMinder. According to CEO Popa, the proceeds will fund the build-out of TypingDNA’s developer support network and tools to integrate its services with popular web development tools.

“Advancing the research and distribution of typing biometrics is of global importance. Keyboards are incorporated [into] almost any device today, making typing behavior the most widely available user biometric,” he added. “This round of funding will allow us to further our mission to provide user-friendly, non-intrusive biometrics and increased security to people around the world.”

TypingDNA’s platform records dynamic statistics about pressed keyboard keys and turns them into typing patterns, which its proprietary engine analyzes and verifies against patterns collected from real-world users. As Popa explains, the way a person types on a keyboard is unique and fairly difficult to replicate — in point of fact, it’s behaviorally rich enough to reveal biometric traits like gender and age.

Local clients train models on users’ patterns silently in the background, focusing on 44 commonly used letter and number keys. Stats like individual key presses and flight times are stored for later algorithmic ingestion, but not sequences between two or more keys — that’s to ensure text can’t be recreated.

Popa says most intruders are caught in under 100 typed characters, or on a typical sentence.

TypingDNA offers a number of client-side products, including a PC tool that continuously authenticates users based on their typing patterns. It alerts administrators or automatically locks down the system when it detects anomalous keystrokes, and it optionally signs out accounts, captures screenshots, takes webcam snapshots, and sends alerts (via email or SMS) if it detects anomalous behavior. A complementary Chrome extension serves up two-step verification codes for third-party services like Amazon and Facebook in the browser, and TypingDNA’s API and SDK provide a means of securing logins and enforcing reset passwords on mobile, as well as web apps.

The biometric authentication market is projected to generate $ 65.3 billion by 2024, and TypingDNA is far from the only player in the lucrative space. Pindrop, a firm developing biometric voice authentication solutions, raked in $ 90 million in funding in December 2018. Global identity verification provider Onfido nabbed $ 50 million in April. And Buguroo, which taps deep learning and behavioral biometrics to detect banking fraud, recently secured $ 11 million.

But Gradient Ventures general partner Darian Shirazi isn’t concerned. “We’re excited about TypingDNA’s developer-first approach to [enabling] people to authenticate securely based on how they type,” he said. “With global regulation impacting face-recognition-based authentication and hackers targeting SMS-based two-factor authentication, typing biometrics is the best form of identifying people without compromising privacy or security.”

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Big Data – VentureBeat

PrecisionHawk raises $32 million for drone management and analytics tools

December 11, 2019 Big Data

There’s a reason firms in agriculture, energy, and industry are increasingly opting to adopt drone-based monitoring and analytics products: They promise to cut down on operational expenses while removing human inspectors from harm’s way. According to a recent report, flying drones cost between $ 200 and $ 300 per mile compared to conventional helicopter flights that cost upwards of $ 1,200 to $ 1,600 per mile. And the Centers for Disease Control reports that from 2003 to 2013, the number of work-related fatalities in the oil and gas extraction industry increased 27.6%, with a total of 1,189 deaths.

A cottage industry has emerged to meet the demand, and one of the startups at the forefront is Raleigh, North Carolina-based PrecisionHawk. Founded as WineHawk Labs in 2010, an unmanned aerial systems and remote sensing company for wine producers, it pivoted in 2013 to general drone solutions and consultancy. This was prescient — the Federal Aviation Administration (FAA) forecasts that the drone industry could triple between now and 2023, with an estimated 823,000 drones flying by that time. And perhaps unsurprisingly, PrecisionHawk hasn’t had much trouble convincing investors to fund its ventures.

PrecisionHawk today announced that it’s raised $ 32 million from venture investors including Millennium Technology Value Partners, Third Point Ventures, Eastward Capital Partners, and others, bringing its total raised to over $ 130 million following a $ 75 million series D in January 2018. CEO Michael Chasen, who was recently appointed by U.S. Secretary of Transportation Elaine Chao to chair the FAA’s Drone Advisory Committee, said the fresh capital will fuel the continued development of PrecisionHawk’s software offerings and the company’s sales and market expansion initiatives.

PrecisionHawk raises $32 million for drone management and analytics tools

Above: PrecisionHawk: Roof damage assessment

PrecisionHawk offers an integrated platform of drone and sensor hardware, flight and analytics software, and services for businesses. The company helps clients to define their objectives and flight plans as well as their technology mix, and to tap turnkey flight software and a drone pilot network with over 15,000 licensed operators to collect and verify drone data. Clients can post offers for pilots on PrecisionHawk’s jobs board, which finds those with the appropriate skill level and facilitates payments and quality assurance. And they gain access to PrecisionAnalytics, a mapping and inspection solution that offloads data aggregation and annotation to machine vision, leveraging high-resolution imagery and GPS to produce models, measurements, and stitched-together maps.

For each customer, PrecisionHawk identifies the costs of solutions and forecasts a return on investment. Based on the area of interest, its flight operations leaders specify the necessary drones, sensors, and software before developing an approach that integrates with current operations and safety protocols. Then, they account for baseline quality requirements in orchestrating the overall program and — through partnerships with SAS, CapGemini, Salesforce, and consulting firms like Deloitte, McKinsey, and Booz Allen Hamilton — use third-party and in-house services and analytics to derive insights from collected data.

PrecisionHawk’s drone portfolio includes models from DJI, BirdsEyeView, and BFD. Sensors from MicaSense and others include lidar (with point clouds up to 500 points per square meter), thermal (which collects radiometric data), hyperspectral (which captures minerals and vegetation information), multispectral (which can see both visible and invisible light radiation), methane detection lasers, optical gas indicators, and RGB cameras. PrecisionFlight Pro, the complementary flight management software, helps to plan routes using a combination of digital elevation and 3D models.

PrecisionHawk’s drone management system lets program supervisors and operators input flight logs and track aircraft usage and maintenance, plus view hardware and pilot deployments as well as incident reports. On the analytics side of the equation, the firm’s solutions suite targets industry segments like distribution, transmission, oil and gas, and agriculture, automating the identification of component damage by cross-referencing uploaded imagery against distribution data. Furthermore, they automatically spot areas of concern such as cracked insulators, damaged cross arms, corrosion, leaning poles, pests, drought, and weeds, and they power services like plant counting, livestock health monitoring (from elevated body temperature), grove maturity estimation, volume measurements, and estimating plant vigor, leaf area, canopy cover, tree crown sizing, water quality, and more.

For instance, PrecisionAnalytics is able to generate a vegetative index, or an indicator of where plants in a field are healthy versus browning as a result of flooding, over-fertilization, or disease. A client can apply the index over collected aerial maps, which PrecisionHawk’s software calculates by ingesting the spectral values of each pixel and assigning a number representing plant health. PrecisionAnalytics offers a library of vegetation for a range of crops, and it sources from several vegetative indices (e.g., GRVI, VARI, NDVI, NDRE, SAVI, and ENDVI) to provide various means of assessing crop status.

The commercial drone market is edging toward becoming a $ 127 billion industry by 2020, according to PricewaterhouseCoopers, and there’s been a corresponding uptick in venture capital investment in drone-related companies. Indeed, an estimated $ 454 million was plowed into UAV startups in 2016 alone, which included the likes of San Francisco-based DroneDeploy and San Carlos-based Prenav. More recently, Kespry raised $ 33 million in December to help companies capture aerial imagery through drones; Iris Automation raised $ 8 million to help unmanned aircraft avoid collisions; and DroneDeploy raised $ 35 million to further develop its drone management and logistics tools.

PrecisionHawk has done well for itself despite the competition, with strategic investments from enterprise customers and partners including Comcast Ventures, DuPont, NTT Docomo, and Yamaha Motor. It now counts among its customer base five of the top 10 utility companies, the largest provider of communications infrastructure in the U.S., major providers of seed and agricultural chemicals, the United States Department of Agriculture, and North Carolina State University.

“This is an exciting time at PrecisionHawk and the commercial drone industry overall as adoption of drones continues to grow at exceptional rates,” said Chasen. “Our unique software and services deliver incredible value to our customers, equipping them with the highest level of aerial intelligence to meet their current and future asset management and operating needs. We are pleased with this latest round of funding which will help us better address this surging demand, particularly in the energy and agriculture markets, and lead us into sustained profitability and growth.”

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Big Data – VentureBeat

Appier raises $80 million for AI that improves marketing decisions

November 26, 2019 Big Data

Getting a customer onto a company’s website to check out its products is challenging enough in itself, but getting them to complete a transaction is a whole different ball game. Last year, 75% of all ecommerce orders were abandoned before finalizing the purchase — reasons include an overly complicated checkout process, limited payment options, hidden costs, registration friction, security concerns, and more.

This is one of the problems that Taiwan-based Appier is looking to help online retailers solve. Its AI-powered platform tracks customers’ activities across a website to improve the chances of them completing a transaction.

Today, Appier announced it had raised $ 80 million in a series D round of funding from Insignia Venture Partners, HOPU-Arm Innovation Fund, TGVest Capital, Temasek’s Pavilion Capital, JAFCO Investment, and UMC Capital. This takes the company’s total funding to around $ 160 million, following its 2017 series C round of funding which saw big names including SoftBank join the fray.

Tracking

Founded in 2012, Appier uses machine learning to crunch myriad data points in real time, such as the cursor position of a mouse, how the customer taps or swipes a screen, the amount of scrolling, and more — this is then used to determine their purchase intent. In tandem, Appier can also A/B test different campaigns to determine which ones are more effective in converting a casual window-shopper into a customer, which may include customized promotional offers. This is part of a product offering it calls AiDeal, which recently launched as a result of Appier’s acquisition of Japan-based Emin.

Appier raises $80 million for AI that improves marketing decisions

Above: AiDeal allows companies to A/B test different promotions.

It’s worth noting here that the platform isn’t exclusively about getting people to buy physical goods once they’re on a website. It can also be used to proactively target people who already have an app installed on their phone. For example, a video-streaming company that offers some free shows could use Appier’s AiQua platform to test and issue push notifications or in-app messages to drive subscription signups. Similar to AiDeal, Appier’s AiQua product was the result of its acquisition of an Indian startup called QGraph last year.

Elsewhere, Appier has long offered a product it calls CrossX Advertising, which can be used by retailers to, say, deliver better-targeted ad exposures to those most likely to convert — Audi, for example, used the platform to target test-drive ads at people aged 30 and over who had previously searched online for luxury cars.

In-house

Appier said that it develops its machine learning algorithms entirely in-house rather than using an “off-the-shelf” solution, and its models are trained through ingesting data from websites, apps, customer relationship management (CRM) software, and so on, which helps improve the machine learning model over time.

“The real-world environment — unlike that of a lab — is dynamic and diverse and ‘off-the-shelf’ algorithms don’t always cope with it well,” Appier CEO and cofounder Chih-Han Yu told VentureBeat. “Our clients need to be able to use our solutions to manage many different and fast-moving scenarios — different KPIs, varying data sources, etc. This means that our scientists spend a lot of time making sure our deep learning solutions can deliver optimal performance in any situation that our clients might face.”

With another $ 80 million in the bank, Appier said that it will push ahead with global market expansion and target its technology at new industries “beyond digital marketing.”

“Our latest investment brings with it new shareholders whose growth-stage experience will help us to scale faster towards our ultimate goal of revolutionizing the way enterprises adopt and leverage AI to grow, remain competitive, and manage continuous business transformation,” Yu added.

One example Yu provided was products to help companies automate the process of building AI models, enabling them to bolster their data science capabilities without having to hire a “complete data science team,” he said.

Data decisions

Appier’s methods of tracking customers on digital properties isn’t entirely a unique approach, with the likes of Contentsquare adopting similar techniques to tell companies why their customers may be abandoning their carts before completing a purchase.

Moreover, another thing Appier and Contentsquare have in common is that they’re both tapping a growing demand for platforms that crunch large amounts of data to improve decision making — this spans far beyond retail and marketing, and into areas such as insurance and even cities’ infrastructure projects.

“Appier is riding a strong long-term trend for enterprises leveraging data to make smarter decisions,” added TGVest Capital chairman DC Cheng. “Thanks to its unique use of AI technology in the digital marketing space, Appier has been a category leader since its inception and has the opportunity to expand into new corporate functions where data-based decisions are made.”

Including its Taiwan headquarters, Appier claims 400 employees across 14 offices in 12 markets in Asia Pacific (APAC), and Yu said the company is currently looking to expand to new markets, though it wouldn’t confirm whether one of those would be the U.S.

“We are planning to look beyond our current markets and explore opportunities in other parts of the world,” Yu said. “We look forward to sharing more news on this in the coming months.”

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Convoy raises $400 million at $2.75 billion valuation to make freight trucking more efficient

November 13, 2019 Big Data

On-demand trucking and freight marketplace Convoy has raised $ 400 million in a series D round of funding co-led by Generation Investment Management and T. Rowe Price Associates, with participation from Alphabet’s CapitalG, Lone Pine Capital, Baillie Gifford, Fidelity Management and Research, and Durable Capital Partners.

The Seattle-based company has raised a chunky $ 668 million since its inception in 2015 and now claims a post-money valuation of $ 2.75 billion. Its latest cash injection follows a $ 185 million series C round led by CapitalG last September, which elevated it to the much-coveted “unicorn” club with a valuation north of $ 1 billion. Other illustrious previous investors include Salesforce CEO Marc Benioff, Dropbox CEO Drew Houston, Instagram cofounder Kevin Systrom, Jeff Bezos’ Bezos Expeditions, Y Combinator, Greylock Partners, and — because why not — Bono and The Edge from U2.

By way of a quick recap, Convoy connects shippers with carriers, matching supply with demand to ensure trucks aren’t traveling long distances with empty trailers. It’s all about optimizing and streamlining cargo loads — like Uber for trucks. In fact, Uber itself offers a similar service called Uber Freight, which it recently committed another $ 200 million to as part of a major expansion.

Countless other trucking startups have raked in big venture capital (VC) bucks — San Francisco-based KeepTruckin recently secured $ 149 million in funding from a host of big-name investors, including Alphabet’s other investment arm GV, while Next Trucking closed a $ 97 million investment.

The reason for this road freight tech boom is simple — the trucking industry generated around $ 800 billion in revenue in the U.S. alone last year, according to recent figures from the American Trucking Associations — a number that represents more than 80% of the country’s total freight bill.

“We built this company from the beginning with a focus on creating a more efficient model for connecting shippers and truckers,” explained Convoy CEO and cofounder Dan Lewis. “Trucking has historically been a zero-sum game, born from a marketplace where when one side wins, the other side loses. The magic of applying technology to this decades-old industry is that we can create a true win-win for both sides, lowering the total cost to shippers with better service while removing the hassle of wasted time and miles for truck drivers, allowing them to earn more.”

Automation

Machine learning (ML) plays a pivotal role in Convoy’s pitch — rather than relying on humans to match freight loads with trucks, the company announced earlier this year that 100% of the process is now automated in some of its biggest markets. This means that when a driver searches for a new load through the Convo app, they will be paired with the most suitable options. Moreover, Convoy’s ML smarts improve with each load as it learns from carrier behavior.

Additionally, ML plays a big role in pricing, though both parties must agree on a rate once they’ve been matched. Based on the load, route, and so on, Convoy automatically suggests a price, though carriers can counter this with a bid if they think the rate is too low.

Convoy raises $400 million at $2.75 billion valuation to make freight trucking more efficient

Above: Convoy’s carrier app

Convoy’s new gargantuan funding round will further the company’s goal of removing inefficiencies and automating processes that have historically consumed a lot of resources. This includes the recently launched Convoy Go service, a “drop-and-hook” marketplace that matches preloaded trailers with Convoy’s transport network infrastructure, reducing wait times for collecting a new load by up to two-thirds.

It’s a similar story elsewhere in the truck-tech sphere, with Uber Freight leaning heavily on machine learning as part of its offering, while upstarts such as Locus are pushing to automate logistics with supply chain platforms that help shippers optimize route planning.

Green machine

Another benefit of Convoy’s platform is helping shippers and carriers avoid “empty miles,” which can take a toll on the environment.

“Building a more efficient digital freight network also means we can dramatically reduce carbon emissions associated with empty miles, which is good for the planet,” Lewis said.

This has been a key theme to emerge across the technology industry, with the likes of Apple and Google increasingly touting their green credentials. On-demand transport giant Uber is pushing for more electric vehicles in some of its key locations, and it recently committed to powering each of its offices with renewable energy by 2025.

According to the U.S. Environmental Protection Agency (EPA), mid-weight and heavy-duty trucks accounted for 23% of all transport-based greenhouse gas emissions in 2017. And an American Transport Research Institute (ATRI) report on the operational costs of trucking found that of the 9.4 billion miles traversed annually, 20.7% involved empty loads, thought other reports — including research carried out by Convoy itself — place this figure at closer to a third.

Whatever the true figure, Convoy’s “empty mile” solution is clearly appealing to investors with commitments to both profit and ethics.

“Generation’s investment in Convoy is grounded in many years of research into the future of logistics,” said Generation Investment Management partner Joy Tuffield. “Through its use of data science, Convoy is driving the next evolution in efficiency across the industry. This is an exceptional opportunity to achieve a reduction in carbon emissions while simultaneously increasing earnings for truck drivers and increasing service quality for shippers.”

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Aurora Insight raises $18 million for sensors and AI that monitor radio frequency spectrum in near-real time

October 22, 2019 Big Data

Keeping tabs on the ever-changing global connectivity map isn’t easy, particularly for the businesses spending billions to secure wireless spectrum. It’s this gap in strategic intelligence that motivated the founding of Aurora Insight, a Washington, D.C. startup using a sensor network and machine-learned digital signals to continuously sample the full radio spectrum.

Aurora Insight has achieved a measure of success in the three years since it emerged from stealth, attracting unnamed clients across four continents. In anticipation of accelerated growth, the company today revealed that it’s raised fresh venture capital following a series A earlier this year led by Alsop Louie Partners and True Ventures. (True Ventures initially invested in 2017.) The round — which also saw participation from Tippet Venture Partners, Revolution’s Rise of the Rest Seed Fund, Promus Ventures, Alumni Ventures Group, ValueStream Ventures, and Intellectus Partners — brings its total raised to $ 18 million.

“The boundaries of wireless are being redrawn,” said cofounder and CEO Brian Mengwasser. “It’s not just about phones anymore. Buildings have become base stations, factories operate their own LTE and 5G networks, and connected cars have five integrated receivers for different networks. Our customers don’t have the time or money to deploy fleets of trucks and spectrum analyzers to determine if their wireless solutions are going to work. We enable more companies to design-to-reality and get more out of limited spectrum, which is part of everyone’s technology stack now.”

At a high level, Aurora Insight’s product suite taps a combination of maps, near-real-time radio spectrum data, and analytics to help clients identify gaps in service. Its products are designed to help network operators and carriers deploy and optimize their spectrum, and to anticipate rollouts in the course of planning for expansion.

Alsop Louie Partners and Aurora board member Gilman Louie asserts that conventional ad hoc spectrum measurement methods are “outdated” and provide only snapshots of connectivity. By contrast, Aurora Insight claims its approach reveals different patterns of spectrum occupancy (i.e., where operators have excess spectrum) and detect unused spectrum available for unlicensed use, as well as drill down to extract carrier and band information for individual cells (land areas served by at least one fixed-location transceiver) or clusters of cells.

“The reality of … techniques employed by … networks means it’s both more difficult and more important to quantify the radio spectrum,” he added. “Having the accurate and near-real-time feedback on the radio spectrum that Aurora’s technology offers could be the difference between building a … network right the first time, or having to build it twice.”

True Ventures partner and fellow Aurora Insight board member Rohit Sharma added, “Wireless spectrum is one of the most critical and valuable parts of the communications ecosystem worldwide. To date, it’s been a massive challenge to accurately measure and dynamically monitor the wireless spectrum in a way that enables the best use of this scarce commodity. Aurora’s proprietary approach gives businesses a unique way to analyze, predict, and rapidly enable the next-generation of wireless-enabled applications.”

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Healx raises $56 million to combat rare diseases with AI

October 16, 2019 Big Data

Healx, a company that said it’s using artificial intelligence (AI) to discover new drug treatments for rare diseases, has raised $ 56 million in a series B round of funding led by Atomico, with participation from Intel Capital, Balderton Capital, Global Brain, Btov Partners, Amadeus Capital Partners, and Cambridge Innovation Capital’s Jonathan Milner.

Founded out of Cambridge in 2014, Healx’s core AI Platform — Healnet — applies a range of machine learning techniques to public and proprietary data sources covering literature, clinical trials, patents, drug targets, chemical structures, symptoms, and more. Part of this involves using natural language processing (NLP) to extract insights and knowledge from all the published sources around specific diseases.

The culmination of all this data, Guilliams said, results in a knowledge graph of rare diseases which could help pharmacologists or biologists unearth effective new treatments that would otherwise be much more difficult to spot.

“We use a variety of machine learning algorithms to solve the many tasks necessary to predict drug treatments and translate them in the clinic effectively,” Healx CEO and cofounder Dr. Tim Guilliams told VentureBeat. “We use deep learning AI algorithms that predict novel connections between, for instance, drugs and disease. This is known as knowledge base completion, and relies upon our comprehensive knowledge graph of rare disease data.”

Above: Founders with the Healx team in Cambridge, U.K.

Rare diseases

While rare diseases are generally defined as diseases that impact a small percentage of people, there are up to 8,000 such diseases globally, according to the World Health Organization (WHO), affecting 400 million people globally. And the vast majority — 95% — still don’t have a FDA-approved treatment.

It’s worth stressing here that Healx isn’t setting out to develop completely new drugs, rather, it’s about getting the most value out of drugs that have already been approved, through combining them in new ways.

“We don’t develop new chemistry and molecules, we maximize the value for the existing drugs — combining them in a clever way to improve their therapeutic effect when needed,” Guilliams continued.

Prior to now, Healx had raised around $ 12 million, the bulk of which came via a $ 10 million series A round 15 months ago, and with another $ 56 million in the bank, it said that it plans to build a clinical-stage portfolio for rare diseases such as Fragile X Syndrome (FXS), a known genetic cause of autism. To help, Healx is launching a global accelerator program that will strive to find clinic-ready treatments of rare diseases within two years, a significant reduction on the decade or more it can typically take using traditional drug discovery processes.

“The current, expensive, trial-and-error-based model of drug discovery hasn’t changed in a century,” added Atomico principal and former surgeon Irina Haivas. “And it especially fails rare disease patients. 50 percent of these patients are children, many living with highly debilitating symptoms. Healx has shown that doesn’t have to be the case, by combining AI with world-class pharmacological expertise and putting patients first.”

The Rare Treatment Accelerator, as Healx’s new program is called, is intended to serve as an easy way for Healx to collaborate with clinicians and patient groups that represent people with rare diseases, and will involve reviewing shared data and resources.

“To date, it’s been families and patient groups who have had to become experts in the diseases affecting their loved ones and have often been the ones driving forward the efforts into finding new treatments,” Guilliams said. “With our unique combination of in-house R&D, industry collaborations and now the Rare Treatment Accelerator, we look forward to supporting these groups in their mission.

The AI treatment

AI and machine learning are infiltrating pretty every industry, and the drug discovery realm is no different. In the past few months alone Utah-based Recursion Pharmaceuticals took its total funding to $ 230 million with a fresh $ 121 million raise; London’s BenevolentAI secured $ 90 million, taking its total inbound investment to nearly $ 300 million; and Maryland-based Insilico Medicine locked in a further $ 37 million. As it happens, earlier this year Insilico Medicine partnered with A2A Pharmaceuticals to launch Consortium.AI, a joint venture designed to treat rare diseases with AI.

While all these companies share a common goal, vis-à-vis they’re using machines to speed up drug discovery and development, Healx is carving its niche by “maximizing the value of already-approved drugs,” rather than developing new molecules, while simultaneously focusing on rare genetic diseases.

Looking further down the line, Healx said that it’s targeting 100 rare diseases treatments by 2025.

“Because we start from safe, existing drugs, we can quickly and cheaply translate them in the clinic, which is key to Healx’s mission,” Guilliams said. “The trials for our Fragile X treatments are just the start of the impact we believe our technology is capable of having on drug discovery.”

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