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7 startups are riding a wave of innovation with artificial intelligence | Technology

7 startups are riding a wave of innovation with artificial intelligence | Technology
7 startups are riding a wave of innovation with artificial intelligence | Technology

7 startups are using artificial intelligence to riding a wave of innovation!

Organizations are increasingly adopting AI-enabled technologies to solve existing and emerging problems in enterprise ecosystems to meet changing market demands and drive business outcomes at scale.

AI innovation is accelerating, said Shubhangi Basishta, senior principal research analyst at Gartner. Innovations such as Edge AI, computer vision, decision intelligence and machine learning will transform the market in the coming years, Vashisht added.

However, as AI technologies help create more resilient and efficient business systems, they face new challenges. For example, Gartner notes that if left unchecked, AI-based approaches can remain biased, which can lead to problems and lost productivity and revenue. AI is data-driven, and when there are errors in the data pipeline, AI models produce biased results. According to a study by Gartner, only 53% of AI projects go from prototype to production.

But it's not all bad and dark for the ecosystem. A new McKinsey study shows that high-performing AI adopters who follow best practices benefit the most from AI and become professional or industrialized. As more startups that innovate for businesses mount the next wave of AI, some startups appear poised to lead the way in 2022 and beyond.

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Package tracking: A report by Statista last month showed that the number of AI-centric startups worldwide reached 3,465 in 2018, with 1,393 in the US alone. Another state-of-the-art intelligence report from CBS Insights last year said funding for artificial intelligence startups reached a record $17.9 billion in the third quarter. Some players in the ecosystem are waiting to lead the group with sufficient investment capital. But what are some startups in the growing space of AI-driven startups that companies may need to scrutinize?

Demonstrating the upside of the fast-paced market, here are 10 AI startups whose CEOs have shared with VentureBeat the broad context of their key paradoxes, strategies, and appeal over the past few months.

Here are the important details of these 10 AI startups to research in various industries like retail, finance, cybersecurity, devops, etc. Each company is ranked by its total funding, including quotes and metrics from interviews with VentureBeat.

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1. DataStax

  • Established: 2010
  • Founders: Jonathan Ellis, Matt Pfeil
  • Headquarters: California, USA
  • Total funding to date: $227.6 million

Real-time data company DataStax says it helps companies express the value of real-time data to build the high-growth intelligent applications needed to transform it into a data-driven business. DataStax is built on some of the leading digital services used every day for streaming, gaming, social networking, e-commerce and many other services. Companies like Verizon, Audi, ESL Gaming and others use DataStax solutions to build large-scale, real-time applications to improve their businesses - DataStax NoSQL Cloud Database, Astra DB and Unified Event Streaming technologies, including Astra Streaming.

According to Chet Kapoor, Chairman and CEO of DataStax, DataStax offers an open stack with all real-time data based on the world's most scalable database (Apache Cassandra) and cutting-edge streaming technology (Apache Pulsar) an open cloud-native architecture... The open The company's packages help developers create real-time applications that keep their businesses running smoothly.

These developers continue to harness the power of advanced event streaming technology powered by Apache Pulsar to process instant data, drive dynamic customer experiences, and integrate ML and AI into a data stack, Kapoor said. DataStax uses modern APIs that allow developers to avoid the complexity of multiple OSS projects and non-scalable APIs, he said.

DataStax claims that its high-level data APIs are "energy trading, mobile, AI/machine learning, IoT, microservices, social, gaming, and interactive applications that should be scaled and compressed as needed." Kapoor noted that DataStax along with others in the industry competitors have an advantage as it is an open-only stack that integrates mobile data and data-at-rest for real-time consumption, can be used on any cloud, and pays per growth rate.

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2. Visier

  • Established: 2010
  • Founders: John Schwartz, Ryan Wong
  • Headquarters: Vancouver, Canada
  • Total funding to date: $216.5 million

Visier Corporation Canada SaaS (aka Visier) is a workforce analytics platform that provides cloud-based energy analytics and planning solutions. To achieve good team and business results, leaders must first ask the right questions about their people. Ryan Ong, co-founder and CEO of Visor, told VentureBeat that Visier provides solutions that move people's data quickly and accurately so companies can improve productivity and performance, employee satisfaction and retention, and ensure profitable career planning and morale. May escalate future decision making.

Wong said Visier has developed solutions that combine Scala, Winkel, open-source algorithms and proprietary technologies. He said Vizier uses artificial intelligence to enrich a company's data and high-quality information, allowing companies to better compare and understand trends over time. He added that Visor provides proven ML predictions that have been validated across hundreds of organizations.

"Predictive models learn patterns from employee or organizational data and combine them into easy-to-understand functional information. Visor uses AI to help business analysts by analyzing the creation, highlighting, and new user patterns, spend, and potential of enterprise data. Question.

While Visier competes with professionals like One Model and Cruncher, Wang said the company aims to help them accelerate their employees' analytics strategy in three key areas where analytics processes and other systems are failing or inadequate. These areas include data management, delivery, and user experience. Vizier's list of competitors includes do-it-yourself analytics, which use analytics providers like Workday and Oracle's HCM suite, and general-purpose business intelligence tools like Tables and PowerBI.

The company remains focused on answering the key questions business owners need to understand how to build better business models overall. Vizier, which raised $125 million in a Series E round last year, is expected to expand its global reach.

Clients include Electronic Arts, Uber, Adobe and more. Visor is expanding its presence in 75 countries and has plenty of room for growth.

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3. Vic.ai 

  • Established: 2016
  • Founders: Alexander Hagerup, Kristoffer Roil, Rune Loyning
  • Headquarters: New York, USA
  • Total funding to date: $62.7 million

The founders of Vic.ai want to use autonomy and artificial intelligence to reinvent accounting. Kristoffer Roil, co-founder and COO of Vic.ai, said Vic.ai ushered in a new era of intelligent accounting, in addition to manual data entry and fully automated invoicing - the most manual and efficient work in accounting.

According to Alexander Hagerup, co-founder and CEO of Vic.ai, VCAI uses it own AI technology and algorithms to handle various types and formats of cargo after being trained on 5 billion units of data. AI works with 99% accuracy, and customers can see process improvements of up to 80%. Vic.ai provides commercial information to customers. By retrieving valuable information from real-time financial transactions, executives can quickly gain financial advantage by making better decisions.

Compared to RPA solutions, Roil said the Vic.ai system does not require any rules, templates or configuration to work because it is trained on more than 5 billion computations and learns from data every day. He said the bill is easy to read, but it requires intelligence to properly classify it - either by a single person or more efficiently by an AI solution such as VCAI.

“With Vic.ai pre-training on historical data, you can start off with incredibly high accuracy. Over time, the system learns, adapts and improves where it can handle most of the invoices independently. It just doesn’t Read it.Invoices, but it can select multiple invoices and the correct cost type.

While Vic.ai's biggest competitors are AppZen, ABBYY, Smartli and Mineraltree, the company continues to lead the way in using autonomy and intelligence to improve productivity, decision making and ROI in its accounting and finance operations.

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4. BUDDI.AI    

  • Established: 2013
  • Founders: Ram Swaminathan, Sudarsun Santhiappan, Venkatesh Prabhu
  • Headquarters: New York, USA
  • Total Funding: Undisclosed

The use of AI in the healthcare industry is growing astronomically, and a Gartner report shows that healthcare organizations’ strategic understanding of AI is maturing rapidly. New York-based deep learning platform company BUDDI.AI is trying to use artificial intelligence to digitally transform the healthcare industry. BUDDI.AI provides automated clinical and dosing cycle solutions for healthcare. The company claims that its AI-enabled solutions help transform healthcare organizations into organizations with insights into everyone along the chain of unorganized data care.

Ram Swaminathan, co-founder and CEO of BUDDI.AI, told VentureBeat that the capabilities explored by the BUDDI.AI platform could bring clinical context and improve patient care, improve clinical documentation, improve medical coding accuracy and increase compensation. All of these are part of the basic BUDDI.AI loop. Eat healthy.

Over six years, BUDDI.AI has developed a suite of proprietary algorithms for natural language processing, clinical correlograms, natural language creation, navigation detectors, optical character detection, table column extraction, and more, Swaminathan said. The company has more than 50 artificial intelligence-as-a-service (AIaaS) products specifically designed to automate healthcare functions and, according to Swaminathan, offers the best manufacturing capabilities in the industry.

BUDDI.AI's competitors include traditional manual medical coding and medical billing shops that count almost any other semi-automated company like Optum, 3M, EPIC, Cerner, Clinicalworks or Athena Health as a partner. However, Swaminathan said BUDDI.AI is different from other companies because it independently performs medical coding and medical billing for all mHealth specialties. He said BUDDI.AI uses deep learning algorithms combined with state-of-the-art systems created by experts and provides the token with a contractual guarantee of over 95% accuracy and over 70% of monthly bid claims.

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5. Verikai   

  • Founding date: 2018
  • Founders: Brett Coffin, Hari Sundram
  • Headquarters: San Francisco, California, USA
  • Total funding to date: $6 million

Vericai is predictive risk assessment software for the insurance industry. The company uses ML to help insurers and insurers assess risk and says it's the only predictive data tool in the "insurance technology market" right now. With more than 1.3 trillion data markers, 5,000 behavior patterns, and a multi-factor database affecting more than 250 million people, Vericai offers insurance companies unparalleled insight into individual and group risk.

Hari Sundaram, Founder and CEO said Vericai is a predictive data and risk tool for insurers and brokers. He said alternative data and machine learning are at the core of Verikai's products and will always have a major impact on the tools the company offers.

By using big data to calculate clinical outcomes and behavioral characteristics, insurance companies can now make accurate and affordable predictions. Verikai's real-time census risk report helps professionals reduce losses, define strategies and improve the overall underwriting process. The company provides its commercial customers with the right insurance products to ensure staff and employees have the coverage they need.

"As our machine learning models continue to grow and new data sources are discovered, our ability to provide our customers with the best possible product models has always been our top priority," said Sandrum.

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6. Prospero.Ai   

  • Founding date: 2019
  • Founders: George Kailsa, Adam Plante, Niles Plante
  • Headquarters: New York, USA
  • Total funding to date: Unpublished

Prospero.Ai says it is committed to creating a level playing field by investing in the backbone of AI and machine learning solutions. George Kaylas, co-founder of Prospero, created a platform at Adam's Factory and Nile Factory that aims to make money more just and richer for all. George Kaylas, former CEO of Hedge Fund World, strives to provide free, institutional quality investment research with no conflicts of interest.

Kailas said that other fintech companies don't offer their users the most valuable products - predictions based on their data - but Prospero does it differently. Prospero's joint IP with New York University, a proprietary artificial intelligence system, simplifies stock analysis for up to 10 key signals and teaches you how to use your predictions to make better investments.

"Prospero is the first completely free platform that fully protects user privacy. Currently in beta, it aims to counteract the decline of the middle class by providing financial resources and education for all," he said.

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7. HIVERY

  • Founding date: 2015
  • Founders: Frankie Chamaraki and Jason Hoskin
  • Headquarters: Sydney, Australia
  • Total funding so far: 4.6 million

HIVERY hopes that consumer packaged goods (CPG) companies and retailers will fundamentally change the way they collaborate on inventory and location decisions. HIVERY Curate uses machine learning and mathematical algorithms developed and sourced by the Australian National Institute of Science (CSIRO's Data61). With HIVERY Curate, the six-month process has been reduced to around six minutes thanks to the power of AI/ML and the application of mathematical techniques.

HIVERY's ML form uses a recommendation system. These ML models can learn from customer datasets to provide deal recommendations at the store level or in any required number of group stores. HIVERY combines applied mathematical methods with ML, commonly referred to as "Operation Research" or "OR". Although HIVERY's ML model recommends products, its OR algorithm takes into account real-world rules or constraints to ensure all recommendations are practical, functional, and business-wide.

Source: Kolawole Samuel Adebayo, Venturebeat, Direct News 99