Categories
AI

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

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!


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

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

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

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

Contract management is a growing market

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

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

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

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

Integrating AI to advance the state of contract management

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

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

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

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

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

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

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

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

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

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.

Repost: Original Source and Author Link

Categories
AI

Automated accounts payable platform Tipalti raises $270M

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


Tipalti, a platform used by major enterprises to automate common accounts payable tasks, has raised $270 million in a series F round of funding, valuing the company at a cool $8.3 billion.

Accounts payable (AP) refers to any money owed by a company to its various suppliers. Processing and reviewing all internal and external transactions (e.g., paying invoices and reimbursing expenses), ensuring that all liabilities are met, is a resource-intensive process — one that typically requires a lot of manual data capture and management across various internal systems.

The accounts payable software market was pegged as a $8.77 billion market last year, a figure that’s predicted to more than double within seven years. And with Tipalti’s valuation more than quadrupling from the $2 billion at its previous fundraise last year, this serves to underscore the size of the pie Tipalti is chasing. Cofounder and CEO Chen Amit said that Tipalti’s target market constitutes nearly 700,000 companies, with only 4% of that currently penetrated.

“The addressable market is large, as solutions for payables and finance operations are not widely adopted, and none are as integrated as Tipalti’s approach,” Amit told VentureBeat. “Many organizations still struggle with manual processes. The pandemic’s impact on remote work and scalability issues also accelerated the need to turn finance processes into digital workflows — a trend that will not be reversible.”

Payments automation

Founded in 2010, Tipalti offers tools that enable companies such as Twitter, GoDaddy, and Twitch to automate most of their AP tasks, spanning invoice management, supplier management, a purchase order (PO) matching, payment reconciliation, tax compliance, fraud detection, and more. With invoices, for example, suppliers can upload their bills either through Tipalti’s portal or by email, and track the progress online. On the AP (i.e., payer’s) side, optical character recognition (OCR) serves to remove manual data entry, so that the details within all invoices are automatically extracted ready for review.

This automated workflow includes various smarts such as duplicate invoice alerts, which help ensure that a company doesn’t inadvertently pay the same invoice twice. And Tipalti also leans on machine learning (ML) to improve over time, so that if it detects frequent manual data overrides carried out by someone in AP, it will apply that similar logic to future invoices.

Elsewhere, Tipalti also uses historical and real-time data to carry out risk checks on payees — this includes establishing whether they are connected in any way to other blocked payees, for example, or whether there are multiple different accounts with the same associated payment or contact details.

High-velocity enterprises

Automation is playing an increasingly bigger role in the financial services and software sphere, with countless companies getting in on the act. Back in October, Stripe acquired Recko, a platform that automates the payments’ reconciliation process by comparing internal accounting records against external bank statements to ensure there are no discrepancies. And in the past year, we’ve seen businesses such as automated spend-management platform Ramp raise gargantuan sums at billion-dollar valuations.

Tipalti, for its part, had raised some $295 million before now, including its $150 million series E round last October. Today, the San Mateo, California-based company claims that it’s processing more than $28 billion in annual payments for two thousand-plus customers, representing a 100% year-on-year growth.

According to Amit, Tipalti’s focus is more on fast-growing, “high-velocity” enterprises, because they don’t want or have the kind of expenses and resources that larger enterprises typically consume on maintaining complex architectures, often constituting a mix of custom integrations and IT outlays.

“The key challenge our customers face is that they themselves would rather focus on something else — the product, sales, customer experience, and so on — than on the back-office and suppliers,” Amit explained. “And the back office must keep up with and enable the front office’s growth goals. They’re more modern in thinking, and adopt best-in-class, highly scalable solutions that don’t require a lot of maintenance.”

With another $270 million in the bank from backers including lead investor G Squared and funds managed by Morgan Stanley’s Counterpoint Global, the company is well-positioned to “accelerate its product roadmap” and global expansion plans. This will include rolling out new ways to manage spending through a corporate credit card, as well as a feature that will help teams “use invoices as a point of social engagement,” according to Amit. This will be less about morphing into a social network than it will be about making it easier to glean answers from across an organization around “specific areas of spend.”

Looking further into the future, Amit said that the company plans to look beyond accounts payable. “We’ll be developing more product offerings that improve finance operations even more — right now, we’re focused on accounts payable as it is the least efficient process in finance, but we’re also expanding into other areas with the same approach,” Amit explained.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

NewtonX raises $32M for AI that connects customers with subject-matter experts

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


According to Forrester research, between 60% and 73% of all data within an enterprise goes unused for analytics. The challenge is often surfacing knowledge from people with experience in a particular field, who might be distributed throughout branches of an organization. The average large business loses $47 million in productivity each year as a result of inefficient knowledge sharing, according to Panopto. In fact, Panopto estimates that knowledge workers waste 5.3 hours each week either waiting for information from their colleagues or recreating existing knowledge.

The inefficiencies inspired Germain Chastel and Sascha Eder — former McKinsey analysts — to cofound NewtonX, which leverages search technologies to connect companies with subject-matter experts both inside and outside of the organization. NewtonX today announced that it raised $32 million in series B funding led by Marbruck Investments with participation from Gaingels and Level One Fund, which brings the company’s total raised to more than $45 million.

CEO Eder says that the new funds will be used to expand NewtonX’s headcount and further grow its technology platform. “We have grown 40 times since founding the company in 2017 and have plans to more than double revenue in 2022 and in 2023,” he told VentureBeat via email. “The company had several months at profitability, but is currently prioritizing growth over profitability. That being said, it’s great to have a clear path to it.”

Surfacing knowledge

Customers start with a business problem, which NewtonX turns into a custom search query across its database of 1.1 billion experts in 140 industries (e.g., “CRM software decision makers who work at Fortune 500 health care companies”). The database spans professional networks and third-party websites with publicly available professional profiles, like LinkedIn.

Once a list of experts who might be able to help with the problem are identified, NewtonX’s consultants craft personalized messages to pique the experts’ interest and verify the experts’ identities. To customers, NewtonX arranges in-person meetings and relays raw data, survey results from 10 to 10,000 respondents, and analyses from the experts that it ultimately contacts.

“[W]e start with one-on-one interviews and use those qualitative in-depth insights to inform a survey to refine the initial findings at scale and quantify them. Then we take the survey results and follow up with individual respondents who provided interesting insights to discuss further. Since every professional that works with NewtonX goes through a two-step ID check, we can reconnect with them to drive deeper learnings,” Eder explained. “Even though we’re a startup, we compete for business-to-business research projects against the leading market research companies who are often very established, large organizations. That’s because nearly all market research companies specialize in business-to-consumer, and they sometimes have difficulty with reaching business-to-business audiences.”

To prevent conflicts of interest and breaches of confidentiality, NewtonX says it’s implemented a system that allows organizations to register guidelines concerning any consulting activity. Automated tools handle expert consultation scheduling and billing.

A growing network

While 98% of leaders acknowledge the need for high-quality data, only 51% say they have access to the data they need, according to Experian.

NewtonX claims to have provided insights into fields as disparate as quantum computing, the oil industry, fashion, and titanium ore extraction. For one client, it set up 10 videoconferencing sessions with data monetization experts to answer questions about best practices. For another, it recruited a team of engineers and salespeople to develop strategies around the virtual reality market.

NewtonX competes with Zurich-based Starmind, which has a platform that automatically forwards questions to domain experts within companies. Another rival, GrowthEnabler, connects large corporations with people and companies that match their needs.

But NewtonX claims its roughly 200 customers include “decision-makers at top institutional investment firms,” Fortune 500 enterprises, financial services firms, banks, big tech companies (including Microsoft, Pinterest, and Fortune), and consultancies. The 75-person company grew 100% in size year-over-year and has plans to hire over 200 employees by the end of 2022.

“We fared well despite the pandemic. Our work easily transferred to a remote setting because of the way we already managed the fielding and delivery of surveys and interviews in a remote and digital setting,” Eder said. “As a lot of businesses were being very careful with their resources, we saw an uptick in research requests. Companies were taking more calculated risks with their budgets. They were willing to spend real dollars on solid research to be sure about their decision, before launching a new product or initiative with millions on the line.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Simpro raises $350M as demand grows for field service automation software

Join gaming leaders, alongside GamesBeat and Facebook Gaming, for their 2nd Annual GamesBeat & Facebook Gaming Summit | GamesBeat: Into the Metaverse 2 this upcoming January 25-27, 2022. Learn more about the event. 


Field service management, which refers to the management of jobs performed in the field (e.g., telecom equipment repair), faces continued challenges brought on by the pandemic. While the number of customer service inquiries has increased as enterprises have adopted remote work arrangements, worker availability has decreased. Forty-seven percent of field service companies say that they’re having trouble finding enough quality technicians and drivers to meet business goals, according to a Verizon survey. The shortfall in the workforce has increased the burden on existing staff, who’ve been asked to do more with fewer resources.

Against this backdrop, Simpro, a field service management software company based in Brisbane, Australia, today announced that it raised $350 million from K1 Investment Management with participation from existing investor Level Equity. The new funding brings Simpro’s total capital raised to nearly $400 million, which CEO Sean Diljore says will be put toward product development and customer support with a particular focus on global trade and construction industries.

Simpro also revealed today that it acquired ClockShark, a time-sheeting and scheduling platform, as well as AroFlo, a job management software provider. The leadership teams of Simpro, ClockShark, and AroFlo will operate independently, Diljore says, including continued work on existing services.

How to Set Up Maintenance Planner | simPRO

Above: Simpro’s maintenance-planning dashboard.

“This investment marks the next stage of Simpro’s exciting growth journey. Our mission is to build a world where field service businesses can thrive,” Diljore said in a statement. “We’re thrilled to welcome ClockShark and AroFlo to the Simpro family. Both companies are leaders in their spaces and have incredibly valuable product offerings that will benefit our combined customer bases and help our customers increase revenue. We look forward to growing together and building a range of solutions for the field service and construction industries.”

Managing field service workers

Field service workers feel increasingly overwhelmed by the amount of tasks employers are asking them to complete. According to a study by The Service Council, 75% of field technicians report that work has become more complex and that more knowledge — specifically more technical knowledge — is needed to perform their jobs now versus when they started in field service. Moreover, 70% say that both customer and management demands have intensified during the health crisis.

Simpro, which was founded in 2002 by Curtis Thomson, Stephen Bradshaw, and Vaughan McKillop, claims to offer a solution in software that eases the burden on field workers and their managers. The company’s platform provides quoting, job costing, scheduling, and invoicing tools in addition to capabilities for reporting, billing, testing assets, and planning preventative maintenance.

Bradshaw, a former electrical contractor, teamed up with McKillop, an engineering student, in the early 2000s to build the prototype for Simpro in the early 2000s. Working out of Bradshaw’s garage, they started with the development of job list functionality before adding new features, including a scheduling tool for allocating resources.

Today, Simpro supports over 5,500 businesses in the security, plumbing, electrician, HVAC, and solar and data networking industries. It has more than 200,000 users worldwide and more than 400 employees in offices across Australia, New Zealand, the U.K., and the U.S.

An expanding product

With Simpro, which integrates with existing software including accounting and HR analytics software, customers can use digital templates to build estimates and convert quotes into jobs. From a dashboard, they can schedule field service workers based on availability and job status, plus perform inventory tracking, connect materials to jobs, and send outstanding invoices.

Diljore expects the purchases of ClockShark and AroFlo to bolster Simpro’s suite in key, strategic ways. ClockShark, a Chico, California-based company founded by brothers Cliff Mitchell and Joe Mitchell in 2013, delivers an app that lets teams clock in and out while recording the timesheet data needed for payroll and job costing. Ringwood, Australia-based AroFlo, on the other hand, provides job management features including field service automation, work scheduling, geofencing, and GPS tracking.

Reece is now available for Automatic Catalog and Invoice Sync | simPRO

AroFlo and ClockShark claim to have over 2,200 and 8,000 customers, respectively. AroFlo’s business is largely concentrated in Australia and New Zealand, where it says that over 25,000 workers use its platform for asset maintenance, compliance, and inventory across plumbing, electrical, HVAC, and facilities management.

Somewhat concerningly from a privacy standpoint, AroFlo offers what it calls a “driver management” feature that uses RFID technology as a way of logging which field service worker are driving which work vehicles. Beyond this, AroFlo allows companies to track the current and historical location of devices belonging to their field workers throughout the workday.

While no federal U.S. statutes restrict the use of GPS by employers nor force them to disclose whether they’re using it, workers have mixed feelings. A survey by TSheets showed that privacy was the third-most important concern of field service workers who were aware that their company was tracking their GPS location.

In its documentation, AroFlo suggests — but doesn’t require — employers to “speak to [field] users about GPS tracking.”

Aroflo GPS lets you monitor your field technicians across the entire day,” the company writes on its website. “You’ll always know where they are, what they’re working on, and when they finish.”

A spokesperson told VentureBeat via email: “Simpro will continue offering GPS services and also has its own vehicle GPS tracking add-on, SimTrac. Implementation of GPS fleet tracking can help reduce risks, remain compliant with licenses and vehicle upkeep, and reduce costs in the business. It also benefits the technicians by improving their safety, spending less time in traffic and improving time management. Overall, GPS tracking provides improved visibility of staff and understanding of their location, introduces opportunities to reduce costs associated with travel, schedule smarter and even improve driver safety (by limiting their need to race across to another side of town to complete a job).”

A growing field

The field service management market is rapidly expanding, expected to climb from $2.85 billion in value in 2019 to $7.10 billion in 2026. While as many as 25% of field service organizations are still using spreadsheets for job scheduling, an estimated 48% were using field management software as of 2020, Fieldpoint reports. Customer demand is one major adoption driver. According to data from ReachOut, 89% of customers want to see “modern, on-demand technology” applied to their technician scheduling, and nearly as many would be willing to pay top dollar for it.

“The pandemic made many business owners realize how crucial it is to have the right technology in place for remote work. Trades businesses couldn’t afford to abandon projects or lose out on service and maintenance calls because of delayed response times or drawn-out time to complete,” Diljore told VentureBeat via email. “For these businesses, cloud-based software became a necessity for survival when previously it was a ‘nice to have.’”

Simpro competes with Zinier, which last January raised $90 million to automate field service management via its AI platform. The company has another rival in Workiz, a field service management and communication startup, as well as augmented reality- and AI-powered work management platform TechSee.

According to Tracxn, of the over 3,400 companies developing “field force automation” solutions (which include customer service tracking, order management, routing optimization, and work activity monitoring), more than 700 attracted a cumulative $5.8 billion from investors from 2018 to 2020.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Rescale raises $50M more to meet demand for high-performance compute

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


San Francisco, California-based Rescale, a startup developing compute infrastructure for scientific research simulations, today announce that it raised $105 million in an expanded series C that included Jeff Bezos, OpenAI CEO Sam Altman, Richard Branson, Paul Graham, and Peter Thiel. The proceeds bring the company’s total capital raised to $155 million, which CEO Joris Poort says will be put toward growing Rescale’s platform, service offerings, and workforce.

Workloads across scientific R&D often benefit from hybrid cloud and on-premises computing technologies. Powerful computers allow researchers to undertake high volumes of calculations in epidemiology, bioinformatics, and molecular modeling — many of which would take months on traditional computing platforms. But less than 20% of high-performance compute (HPC) workloads currently run in the cloud. Even today, cloud adoption in the science and engineering community remains largely on-premises, relegated to private datacenters.

Founded in 2011 by Poort and Adam McKenzie, former aerospace engineers at Boeing, Rescale enables organizations to run scientific simulations on public clouds like Amazon Web Services, Microsoft Azure, Google Cloud Platform, IBM, and Oracle. The company’s network spans 8 million servers with over 80 specialized architectures and resources like Nvidia Tesla P100 GPUs, Intel Skylake processors, and over 1TB RAM, delivering a combined a 1,400 petaflops of compute.

“Traditionally, HPC was limited to massive players with massive capital spending budgets to buy and build the latest clusters on-premises,” Rescale chief product officer Ed Hsu told VentureBeat via email. “Now, workloads can run across multiple public clouds and Rescale charges for use — not upfront — for physical purchase of machines and computing infrastructure.”

Scaling up compute resources

Whether they leverage compute from Rescale’s infrastructure or from a third-party provider, Rescale customers gain access to software that supports simulation for aerospace, automotive, oil and gas, life sciences, electronics, academia, and machine learning. The company delivers both on-demand and long-term computing environments and pricing, allowing customers to launch single batch jobs, optimization jobs, and large designs of experiments with programmatic bursting.

Beyond this, Rescale helps to manage on-premises HPC resources, schedulers, and software licenses as well as the transfer, organization, and storage of simulation input and output files.

One of Rescale’s more unique features is its recommendation engine, which leverages the metadata from millions of workloads, tens of thousands of apps, and hundreds of compute architectures. Trained on billions of computational core hours, the engine provides suggestions for optimizing performance across different compute clusters.

“[We] see our main competitors as legacy datacenter on-premises clusters,” Hsu said. “[Rescale] creates a long-tail opportunity for AI and machine learning workloads, since it’s an operating expense and delivers supercomputing capabilities. AI and machine learning benefits from access to the newest chip technologies, fast I/O, and compute that Rescale delivers on its platform; AI can be used on Rescale to abstract many aspects of computing to run their workloads.”

Growth segment

Some analysts forecast an annual HPC market spend of more than $60 billion by 2025, with HPC cloud services showing a compound annual growth rate of nearly 80%. The broad HPC market finished 2020 at $38.9 billion in revenue, down just 0.2% from 2019, according to Intersect360 Research.

Workloads in the scientific research and development category — Rescale’s bread and butter — were estimated to be worth $185 billion in 2020.

Since its most recent February funding round, Rescale claims that it’s added over a hundred new customers and expanded its software catalog to more than 800 apps. The company’s client base now stands at 200 enterprise subscribers and 400 subscribers overall, including several Fortune 50 businesses.

In 2020, Google and Microsoft kicked off a program with the startup to offer resources at no cost to teams working to develop COVID-19 testing and vaccines. Rescale provides the platform that researchers launch experiments and record results on, while Google and Microsoft supply the backend computing resources.

“Rescale believes it is doubling the size of the HPC market with its platform,” Hsu added. “[The pandemic has caused an uptick] in in life sciences [especially] as new customers [have] embraced the platform to accelerate drug discovery.”

Rescale’s latest funding round also included participation from Fort Ross Ventures, Gaingels, Gopher, Hitachi Ventures, Initialized Capital, Keen Venture Partners, Microsoft M12, Nautilus Venture Partners, Nvidia, Prometheus Capital, Republic Labs, Samsung Catalyst Fund, Solasta Ventures, Yield Capital Partners, and more. The company currently has 200 workers and expects to grow that number to 300 in a year.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Deliverr raises $250M to grow its ecommerce fulfillment network

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


While ecommerce sales are on the rise — with revenue projected to increase from $2.3 billion in 2018 to $4.5 billion by the end of 2021 — fulfillment remains a challenge as the pandemic snarls the supply chain. As early as July, a U.S. Census Bureau survey f0und that 38.8% of U.S. small businesses were experiencing domestic supplier delays. Shoppers tend not be very understanding of of disruptions, unfortunately, with 38% saying that they’ll abandon their order if the delivery is estimated to take longer than a week.

Against this backdrop, Deliverr, an ecommerce fulfillment startup headquartered in San Francisco, today announced that it raised $250 million in series E funding, bringing its total raised to over $500 million. The round, which was led by Tiger Global with participation from 8VC, Activant, GLP, Brookfield Technology Partners, and Coatue, values Deliverr at $2 billion post-money.

CEO Harish Abbott says that the proceeds will be put toward growing Deliverr’s shipping network, supporting product development, and expanding headcount.

“The most effective way to address supply chain congestion is to move inventory closer to the end customer. Deliverr is the only company working to solve this problem through stronger inventory placement, while leveraging cutting-edge machine learning and optimization technology to build a smarter fulfillment network,” Abbott said in a press release. “With this new capital, Deliverr will focus on scaling next-day fulfillment for ecommerce merchants and grow our world-class team of engineers, data scientists, and operations experts.”

AI-powered fulfillment

Deliverr was cofounded by former Symphony Commerce colleagues Abbott and Michael Krakaris in 2017. Prior to Symphony, Krakaris spend time working with product marketing teams at Twilio. Abbot was the chief product officer at Lulu.com and a senior program manager at Amazon.

Using predictive analytics and machine learning, Deliverr anticipates the demand for products based on demographics, geography, and other variables. The platform then uses the analysis to “pre-position” items close to areas of demand, stocking items across a network of over 80 warehouses, cross-docks, and sort centers.

Deliverr

Deliverr rents out — rather than purchases — warehouse space, using warehouses’ fulfillment departments to pick and pack ecommerce orders. The company’s software determines which products to send to which warehouses and then finds the best delivery method to ship to customers, with either two-day or next-day delivery guarantees.

Deliverr’s platform integrates with retailers’ listing tools and allows managers to explore cost previews for each SKU in their catalog. It also syncs with sales channels so that orders flow in automatically.

Growth market

One in three companies claim to have incorporated AI capabilities like those offered by Deliverr into their supply chain management processes and one in four is working toward that goal, a study from Symphony RetailAI found. A separate report suggests that within the next two years, retailers plan to upgrade their predictive inventory planning, predictive labor planning, and robotic systems for picking and material handling.

Deliverr is a beneficiary of the tech boom. The company’s network — which Deliverr claims is within 100 miles of half of the U.S. population — is on track to power a more than $2.5 billion gross merchandise volume (GMV) run rate by the end of 2021. (For retailers, GMV refers to the average sale price per item charged to a customer multiplied by the number of items sold.) Current customers include large retailers on marketplaces from Shopify, Walmart, Amazon, eBay, and Target.

The explosive growth of online sales is expected to drive the ecommerce fulfillment services market to $86.44 billion in value this year, according to Grand View Research. Deliverr competes with on-demand logistics and fulfillment startup Flowspace, Bringg, ShipBob, Bond, and Shippo, among others.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Swish.ai raises $13M to automate IT service desk tasks

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


Swish, a Tel Aviv, Israel-based startup developing automation technologies for IT service management, today emerged from stealth with $13 million in a series A round led by Dell Technologies Capital with participation from Skywell, Samsung, StageOne, and AxessVentures. The funding will be put toward expanding the company’s headcount and for supporting go-to-market efforts, CEO Sebastien Adjiman says, as well as bolstering Swish’s product stack.

Enterprise help desk support is one of the most labor-intensive — and costly — IT functions. A 2020 BMC study found that the cost of manually handling a help ticket averages $22. Exacerbating the challenge, the acceleration of digital transformation during the pandemic has increased help desk ticket volume. In addition, the IT labor shortage is limiting the ability of enterprises to staff up to meet this growth. According to the latest data, U.S. IT job growth slowed in October because of too few candidates.

Swish uses AI to automate ticket orchestration in existing IT service management workflows. With Swish, tickets can be automatically routed to relevant, available agents based on skill set, load, and cost criteria, ideally improving the resolution time. The platform also provides management with analytics to help identify optimization opportunities in the organization, generated by a combination of natural language processing (NLP), business process mining, and machine learning algorithms.

“We founded Swish with the belief that [the] real value of [automation] isn’t just simple efficiencies but is instead the ability to turn the avalanche of data that’s being generated by today’s digital interactions into autonomous decisions which are smarter, faster, and more accurate,” Adjiman told VentureBeat via email. “We believe Swish is the perfect solution for any enterprise service and support leader who’s looking for a way to quickly utilize the benefits of [automation] to help them reinvent their current ticket process.”

A growing industry

Swish scores IT service reps on their expertise, knowledge, strengths, and weaknesses. Using an AI system, the platform automatically groups similar tickets based on the data contained in tickets, such as ticket descriptions and resolution notes.

Swish looks for inefficient patterns of behavior such as “ping pong,” “rework,” “pending abuse,” and poor workload allocation. It also flags service types leading to high or low satisfaction and costs among customers, informed by sentiment analysis from feedback forms.

“The Swish platform’s core AI engine consists of a unique combination of proprietary machine learning, NLP and business process mining algorithms, which are trained on all the historical tickets that are archived in the existing tools used by our clients,” Adjiman explained. “This historical goldmine of data is then used dynamically to train the models to capture insights about our client’s unique environment, even as it evolves. For example, our service language understanding goes beyond NLP to explore service-specific terms — improving the understanding of each underlying ticket issue and thus identifying the next best action more accurately.”

Efficiency gains

Against the backdrop of Swish’s relaunch, companies are looking broadly to increase their use of automation technology as a result of the pandemic. The BMC survey found that by automating help ticket desk resolution, 22% of tickets can be resolved at practically no cost — in part because of improved error handling and analysis tools like reporting. This is key, given that 95% of customers cite help desk support as important in their choice of and loyalty to a brand.

“The core use case of Swish’s platform is its autonomous ticket orchestration capability. Swish … suggests and provides resources to ensure [agents] have everything at their fingertips to resolve a ticket without the need to re-route or pause it,” Adjiman explained. “Since it’s agnostic by design, it can be deployed on any enterprise ticketing system such as ServiceNow or BMC. Once deployed … Swish can then be connected to additional workflow systems to accelerate any service and support area, such as customer service, HR, and facility management tickets.”

Of course, the employee monitoring aspects of Swish might be discomfiting to some companies. While 78% admit to using monitoring software to track their employees’ performance, 59% of workers say that they feel stress or anxiety as a result of their employer monitoring them, while 43% say that it feels like a violation of trust.

But 35-employee Swish pitches its analytics as a means to provide targeted training. Low-performing reps can be afforded opportunities like tutorials, guidance, and coaching, Adjiman says, or shifted to an area of service for which they might be better suited.

“Service management is an obvious target for the emerging [automation] industry due to the rapidly growing ticket volumes and labor-intensive processes enterprises rely on today,” Dell Technologies Capital managing director Yair Snir said. “The Swish team has already proven the value of [automation] for some of the largest companies in the world.”

Swish — which has raised a total of $15 million in capital and has 15 customers, including Fortune 500 companies — competes with a number of startups in the IT service automation space including Moveworks, Capacity, Electric, and Spoke. Underlining the segment’s growth, Zendesk recently acquired Cleverly, a service automation startup that creates AI-powered tools to solve common customer problems, for an undisclosed amount.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Grammarly raises $200M to expand its AI-powered writing suggestions platform

Join gaming leaders, alongside GamesBeat and Facebook Gaming, for their 2nd Annual GamesBeat & Facebook Gaming Summit | GamesBeat: Into the Metaverse 2 this upcoming January 25-27, 2022. Learn more about the event. 


San Francisco, California-based Grammarly, which develops an AI-powered writing assistant, today announced that it raised $200 million, valuing the company at $13 billion post-money. Baillie Gifford led the round with participation from funds and accounts managed by BlackRock. Grammarly global head of product Rahul Roy-Chowdhury says the funding will be put toward further developing the company’s technology and “accelerat[ing] efforts to help people communicate … in our digital-first world.”

As enterprises increasingly embrace digitization, the over $1.2 billion AI writing assistant market is expected to grow at a compound annual growth rate of 27.6% from 2018 to 2028. According to a survey from John Snow Labs and Gradient Flow, 60% of tech leaders indicated that their budgets for natural language processing — which encompasses technologies like Grammarly’s — grew by at least 10% compared to 2020, while a third said that their spending climbed by more than 30%.

“From changes in our tone to workplace communication shifts at large, the global pandemic has left a lasting impact on how we communicate. The shift to remote-first everything — including school and work — has only increased the need for digital communication assistance like ours,” Roy-Chowdhury told VentureBeat via email. “Poorly-written communication was already estimated to cost businesses billions annually in lost productivity — and that was well before the pandemic forced people to start communicating remotely across all kinds of new connectivity and productivity tools.”

A brief history

The brainchild of Ukrainian developers Alex Shevchenko, Max Lytvyn, and Dmytro Lider, Grammarly was founded in 2009 as Sentenceworks shortly after the sale of Lytvyn and Shevchenko’s plagiarism-checking startup MyDropBox to Blackboard. One of Grammarly’s first products was a barebones WYSIWYG editor into which users could paste text, which evolved into an app that provided spelling and syntax suggestions.

In 2012, Grammarly pivoted from a largely academic customer base to the broader consumer market, rolling out subscription plans and launching ads on Facebook, Twitter, and YouTube. In 2014, Grammarly debuted plugins for Microsoft Office that integrated its grammar checker with Outlook and Word. And in 2015, the company launched the Grammarly browser extension for Chrome and Safari. A Firefox extension followed shortly after, as did an add-on for Google Docs; the message- and email-scanning Grammarly Keyboard for iOS and Android; and a desktop client for MacOS and Windows.

Today, Grammarly offers its entry-level, English-only Grammarly Premium service for $30 a month (or $144 a year), while the enterprise Grammarly Business tier starts at $25 per user per month. Both add style and vocabulary recommendations across all platforms as well as full-sentence rewrites and a tone detector that can identify “tempers” (e.g., “aggressive,” “annoyed,” and “excited”) in emails, documents, and blogs.

“Our business model is a freemium model, in which we offer a free version of our product as well as Grammarly Premium and Grammarly Business, which are paid upgrades,” a Grammarly spokesperson told TechCrunch in a 2019 interview. “The only way Grammarly makes money is through its subscriptions … We don’t sell or rent user data to third parties for any reason, including for them to deliver their ads. Period.”

As of October, eligible Samsung smartphones running the Samsung Keyboard benefit from Grammarly’s spell-checking technology. Grammarly’s writing suggestions are built into the Samsung Keyboard, although access to the company’s premium features requires a subscription.

“Looking toward the future, expanding our critical communication support starts with launching our latest product offering, Grammarly for Windows and Mac. This is a new way to experience Grammarly across any desktop or online application, and a big step in bringing our real-time writing assistance to more places people write — like Microsoft PowerPoint and Slack’s native app, as just a few examples,” Roy-Chowdhury said. “We’ll also double down on partnerships, our next frontier, to expand the reach of our writing assistance … And as more business leaders recognize the direct link between effective communication and business performance, we’re ready to meet the growing demand for Grammarly Business.”

Expanding segments

Spurred by digital transformations that accelerated during the pandemic, a larger share of companies are expected to adopt AI technologies that automatically recommend and tailor copy, particularly in marketing. According to a survey by Phrasee, 63% of marketing teams would consider investing in AI to generate and optimize ad copy. Sixty-five percent trust that AI can generate desirable brand language, the survey found, while 82% believe that their organization would benefit from data that provides insights into how consumers respond to that language.

In an effort to broaden its appeal within markets with specific editing needs, like sales, Grammarly recently introduced Grammarly for Developers, a development kit that enables companies to add Grammarly-powered recommendations to existing web-based apps. Grammarly for Developers, currently in closed beta, handles communication between apps and Grammarly’s cloud services, rendering underlines and suggestion cards, applying text transformations, and providing personal dictionaries.

“With Grammarly for Developers, we’re helping developers deliver a better writing experience to their users while saving them valuable time and resources needed to develop their own technologies,” Roy-Chowdhury added. “Our first Grammarly for Developers product, the Text Editor SDK, brings Grammarly’s communication assistance to any web-based application … Grammarly is [now] available on more than 500,000 applications and sites.”

In June, Grammarly rolled out additional features focused on enterprise communications, including support for up to 50 style guides, snippets, brand tones, and an analytics dashboard. Snippets are preset response templates for quick communication. Brand tones are “tone profiles” that specify which tones team members should use — and avoid. As for the analytics dashboard, it shows metrics that make it ostensibly easier for leaders to identify communication trends and strengths.

Grammarly has competition in Writer, Ginger, ProWritingAid, and Slick Write, which similarly provide an AI-powered writing assistant for a range of use cases. Pitted against Ginger, an English professor writing for Fast Company found that Grammarly’s tips “lack an understanding of nuance,” for example overzealously policing redundancies.

But Grammarly — which has over 600 team members, and is profitable — says it’s managed to attract 30 million users and 30,000 teams to date. New and existing customers include Frost & Sullivan, HackerOne, Lucid, Zoom, Cisco, and Expedia.

The company’s total capital stands at more than $400 million with the latest funding round.

“In 2020 alone, we delivered 1.2 trillion writing suggestions to our global user base.  Sixty-eight percent of the Forbes Global 2000 companies have at least one Grammarly user — and we expect that to increase significantly in the years to come,” Roy-Chowdhury said. “The number of Grammarly Business customers with large-dollar contracts increased by more than 250% this past year alone. We also support over 6,800 nonprofits and nongovernmental organizations from over 150 countries with a free offering, while Grammarly’s education division serves over 2,500 educational institutions with capabilities tailored to their needs.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Nexar, which sells analytics services on top of dash cam footage, raises $53M

Join gaming leaders, alongside GamesBeat and Facebook Gaming, for their 2nd Annual GamesBeat & Facebook Gaming Summit | GamesBeat: Into the Metaverse 2 this upcoming January 25-27, 2022. Learn more about the event. 


Nexar, a startup developing an analytics service on top of car dash cam footage, today announced that it raised $53 million in a series D round led by Qumra Capital with participation from State Farm Ventures, Catalyst Investments, Banca Generali, Valor, Atreides Management, Corner Ventures, Regah Ventures, Aleph, and others. The proceeds — which bring the company’s total raised to $96.5 million — will be used to expand Nexar’s sales and marketing efforts and support the launch of its flagship Nexar One line of dashboard cameras, according to CEO Eran Shir.

Shir and Bruno Fernandez-Ruiz founded Tel Aviv, Israel- and New York-based Nexar in 2015. An MIT graduate, Fernandez-Ruiz was previously global head of advertising personalization at Yahoo and managed Accenture’s center for strategic research. As a research affiliate at MIT, he studied large-scale computing for traffic estimation and prediction using statistical learning and simulation techniques. As for Shir, he became the head of Yahoo’s creative innovation center after the company acquired digital marketing firm Dapper, his second startup (the first being Cogniview Systems), in October 2020.

In 2015, Nexar launched a dash cam app for iOS and Android that uses a phone’s camera to monitor the road ahead, leveraging AI to detect footage of dangerous events and log sensor data to recreate a simulation of what happened to the car. A cursory web search turns up an abundance of dash cam apps on app stores, but in 2016, Nexar uniquely began providing crowdsourced data to help predict collisions by sharing info among users of its app.

“[We wanted] to turn the vehicles of the world into a massive live mesh network of smart cameras and sensors continuously mapping our public spaces. Nexar aims to crawl and index the physical world just like Google crawls and indexes the web,” Shir told VentureBeat via email. “The vision behind Nexar was to make the world collision-free, using crowd-sourced vision and connected cars. Nexar is built on using consumer-grade dash cameras that generate a fresh, high-quality understanding of the world at a street level and the transient changes in it.”

Leaning into crowdsourced data

Nexar isn’t the only company tapping the power of the crowd to analyze road data. In June 2020, Lyft announced that it would collect data from cameras mounted to some cars in its ride-hailing network to improve the performance of its autonomous vehicle systems. Both Mobileye and Tesla harvest information from millions of drivers to train their perception, planning, control, and coordination systems. So does startup Comma.ai, whose aftermarket assisted driving kit — Comma Three — leans on models developed against tens of millions of miles of crowdsourced driving data.

But Nexar now sells its own dash cam hardware and monetizes the data that it collects. According to Shir, the startup is among the top suppliers of consumer dash cams in the U.S. and plans to globally expand with the rollout of the aforementioned Nexar One, which features dedicated LTE connectivity.

“In the general sense of collecting vision data from a network of cars, our competitive situation is pretty unique,” Shir said. “Instead of expensive cameras on a few cars, Nexar shows how a massive fleet can be created to generate data with minimal costs.”

Nexar claims to have partnerships with — and customers in — insurance companies, drivers on ride-sharing services like Uber and Lyft, and automakers and municipalities. Several government agencies are using its tech, which can automatically extract road features from camera footage, to monitor traffic and street infrastructure — for example to spot potholes, cracks, and other potentially dangerous road damage. Most recently, Nexar teamed up with with Nevada public road transit authorities to create “digital twins” representing virtual models of road work.

Nexar

Above: Data from Nexar’s network.

Image Credit: Nexar

The annual investment required to maintain roads, highways, and bridges in the U.S. is roughly $185 billion a year, according to the National Surface Transportation Policy and Revenue Study Commission. Capital from recently passed infrastructure legislation is expected to bridge gaps in budgets, but analytics can — at least in theory — help funnel the money to where it’s needed the most.

In this area, Nexar competes with vehicle telematics startups like Tactile Mobility and NIRA Dynamics, which apply machine learning algorithms to vehicle sensors and controllers to measure grip, friction, and other metrics in real time. NIRA is working with authorities in Sweden and Norway to provide road condition forecasts, and Tactile is conducting a road-monitoring pilot with the City of Detroit and a major automaker. But Shir says that there’s no substitute for camera footage, which he argues can capture phenomena that other sensors are likelier to miss.

“At 150 million miles per month, Nexar ‘sees’ quite a lot of the world, and is the only company (other than Tesla) to have solved the question of the ingestion of vision data from a network of cars — and to do so scalably and economically,” Shir said. “Today, work zone detection — which is non-trivial from the AI standpoint and requires fresh dash cam coverage in any given area — applies to many markets in which we operate. For cities and departments of transportation, especially given the Biden administration’s infrastructure bill, work zones are set to proliferate, impacting safety and congestion on the roads.”

Nexar is also looking to tap into the autonomous vehicle market — in 2017, signaling its ambitions, the company released a dataset of 55,000 street pics from 80 countries for an open autonomous vehicle perception competition. In addition, Nexar is investigating AI collision reconstruction techniques to create “forensically preserved” digital twins of an accident scene, dipping its toes in the insurance market. The company’s privacy policy hints at this — it mentions that insurance partners might consider Nexar collision reports as part of an offer or policy in the future.

Privacy and beyond

The volume of data that Nexar records and processes might rightly give some drivers pause. Nationwide found in a recent survey that more than 60% of consumers have privacy concerns about telematics — i.e., remote vehicle monitoring — despite the fact that interest in telematics is on the rise.

For its part, Nexar states that it’ll “never share any data belonging to individual users with a third party.” The company also asserts that it doesn’t “track individuals or allow third parties to use the Nexar system and violate the privacy of our individual users or the privacy of third parties whose activity was captured by Nexar.”

“[Users only] grant us a perpetual right to use parts of [their] uploaded content — once they are de-linked, de-identified, anonymized, and then aggregated with other user’s [sic] data —  so that it cannot be traced back to them,” Nexar writes in its privacy policy. “We make money by capturing facts about the world that are not private in any way. We actually do not care about your private drives. We care about potholes on the road and orange cones and the free parking spot or how the construction of a new townhouse is progressing. Or whether or not it rained near the stadium.”

Nexar claims to have trained a classifier model to differentiate between images captured inside of cars and road-facing images, as well as footage from garages and other compromising locations. Another model identifies and hides unique markers and objects visible on the car dashboard, including windshield elements. A third model blurs the faces of pedestrians and license plates within view of Nexar’s software and cameras.

Nexar

Even assuming that the models perform perfectly, Nexar admits that it might be compelled to hand over data to law enforcement official in certain circumstances. While the company pledges to disallow groups from using its network as a “digital panopticon,” in part by building its technology so that it can’t be used in conjunction with tools to track drivers, Nexar only promises that it won’t voluntarily share information with government agencies — not that it won’t ever.

This being said, Nexar offers users the option to permanently delete their data from its database. The company otherwise retains it for “as long as is required in order to comply with [its] legal and contractual obligations.”

Nexar — which has 165 employees — claims to service hundreds of thousands of customers and users around the world. Business-to-business annual recurring revenue tripled since its last funding announcement in January 2018, as dash cam sales grew by 280%.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

AI code discovery platform CatalyzeX raises $1.64M

California-based CatalyzeX, a startup that offers a platform AI/ML code discovery and know-how, today announced it has raised $1.64 million in a seed round of funding led by Unshackled Ventures, Darling Ventures, Kepler Ventures, On Deck, Abstraction Capital, Unpopular Ventures, and Basecamp Fund. The company said it plans to use the round — which also saw the participation of multiple angels — to further accelerate the development of its product, democratizing AI for builders worldwide.

Over the years, tens of thousands of AI researches have been conducted, building a huge repository of technical material for various use-cases and industries. However, finding relevant information from this huge chunk for a project at hand has long been a challenge for developers and data scientists around the world. They’d often end up spending hours on Google, searching papers that could contain code snippets and models to build on (only 10 to 12% share code) and other know-how that could accelerate the development of their AI project.

CatalyzeX for AI code discovery

CatalyzeX AI code discovery

Above: CatalyzeX AI code discovery platform

Image Credit: CatalyzeX

Prompted by this challenge in their own professional careers, brothers Gaurav and Himanshu Ragtah decided to start CatalyzeX in 2019. The startup offers a website that curates AI research papers and studies from the web, giving devs a one-stop-shop to discover ML techniques and know-how, along with the corresponding code, for their respective projects.

“CatalyzeX’s offering is powered by crawlers, aggregators, and classifiers we’ve built in-house to automatically go through technical papers as well as code platforms daily and to match and link machine learning models and techniques with various corresponding code implementations,” Gaurav told Venturebeat in an email. “We also allow code submissions and feedback from members of the CatalyzeX network.”

The free-to-access platform is a search engine of sorts, where a developer picks the recommendations or puts in a problem query, like cancer detection, in the search field. The results show all relevant available ML models/techniques — with full paper and code implementation — that could help with the problem. If the code is not publicly available, the platform also provides an option to get in touch with the authors to request it or get further questions answered.

In addition to this, CatalyzeX also offers a browser extension that automatically displays links to code implementations for ML techniques and papers appearing in Google Search results.

“Since code is the lingua franca for builders and makers, not walls of text, and given the sheer volume of developments in AI research every single day, surfacing relevant code implementations greatly saves time and effort for developers and technical non-experts in discovering and assessing viable options to leverage artificial intelligence in their products and processes,” the cofounder added.

Focus on addressing current status quo, growing user base

While platforms like 42papers and Deepai.org also offer AI research and know-how, CatalyzeX claims to differentiate with a much larger repository for model/techniques and code discovery. The platform currently serves over 30,000 users every week with more than 500,000 code implementations.

However, Gaurav emphasized that the real challenge is not to beat these sites but to address the current status quo, which is heavily fragmented and holding back significant technology development from reaching the real world.

This, he said, will be done through accelerating the development of the product, taking it to more developers and data scientists around the world. Gaurav did not share specific product development plans, but he did note that a part of the funding will go toward hiring product designers and engineers who would work upgrading the platform.

“We also have integrations and partnerships planned with several code-collaboration and AI research platforms,” he added while noting that they are also exploring monetization options such as introducing a paid tier with advanced search filters and integrations with development/deployment environments or connecting high-skill talent with global opportunities in AI.

According to PwC, AI could contribute up to $15.7 trillion to the global economy in 2030. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion from consumption-side effects.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link