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Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale.
Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. Vashisth further noted that innovations including edge AI, computer vision, decision intelligence and machine learning will have a transformational impact on the market in coming years.
However, while AI-powered technologies are helping to build more agile and effective enterprise systems, they usher in new challenges. For example, Gartner notes that AI-based approaches if left unchecked can perpetuate bias leading to issues, loss of productivity and revenue. AI is fueled by data and if there are errors along the data pipeline, AI models will produce biased results. Only 53% of AI projects make it from prototype to production, according to Gartner research.
But it’s not all doom and gloom for the ecosystem. A new survey by McKinsey revealed AI high performers following the best practices are deriving the most benefits from AI and professionalizing or industrializing their capabilities. As more startups ride the next wave of AI to innovate for the enterprise, some startups look poised to lead the pack in 2022 and beyond.
Tracking the pack
A report published last month by Statista showed the number of AI-focused startups worldwide was 3,465 in 2018, with 1,393 in the US alone. Another State of AI report from CBS Insights last year said AI startup funding hit a record high of $17.9 billion in Q3. Many players in the ecosystem jostling to lead the pack with enough investment dollars. But which startups in the ever-evolving AI startup space might require a closer look for enterprises?
Below are 10 AI startups that are demonstrating upward growth trajectories in a fast-paced market and whose CEOs articulate to VentureBeat over the past few months a broader context to their key differentiators, strategies and traction.
Below are vital details on these 10 AI startups that are worth watching across diverse industries, including retail, finance, cyber security, devops and more. Each company is ranked by its total funding to date, with quotes and metrics supplied during interviews with VentureBeat.
Founder(s): Jonathan Ellis, Matt Pfeil
Headquarters: California, USA
Total funding to date: $227.6 million
Real-time data company, DataStax, says it helps enterprises to unleash the value of real-time data to quickly build the smart, high-growth applications required to become data-driven businesses. Some of the leading digital services used daily for streaming, gaming, social networks, ecommerce, and many others are built on DataStax. Companies like Verizon, Audi, ESL Gaming and many others are using DataStax solutions — including DataStax NoSQL cloud database, Astra DB and unified event streaming technology, Astra Streaming — to build real-time, high-scale applications that power their businesses.
According to DataStax Chairman and CEO, Chet Kapoor, DataStax provides an open stack for all the real-time data built on the world’s most scalable database (Apache Cassandra) and the most advanced streaming technology (Apache Pulsar), in an open, cloud-native architecture. The company’s open stack helps developers easily build real-time applications that run their businesses.
Kapoor said these developers continue to tap the power of advanced event streaming technology based on Apache Pulsar to act instantly on data, drive dynamic customer experiences and take advantage of ML and AI — all on a single data stack that works. He said DataStax uses modern APIs that allow developers to skip the complexity of multiple OSS projects and APIs that don’t scale.
DataStax claims its modern data APIs “power commerce, mobile, AI/ML, IoT, microservices, social, gaming and interactive applications that must scale-up and scale-down based on demand.” Kapoor noted that DataStax has an edge over other players in the industry because it’s only open stack that unifies data in motion and data at rest for real-time use, available on any cloud and with pay-as-you-grow pricing.
Founder(s): John Schwarz, Ryan Wong
Headquarters: Vancouver, Canada
Total funding to date: $216.5 million
Canadian SaaS company Visier Inc. (also Visier) is a HR analytics platform offering cloud-based solutions for workforce analytics and workforce planning. To achieve better team and business management outcomes, leaders need to start by asking the right questions about their workforce. Ryan Wong, cofounder and CEO at Visier, told VentureBeat that Visier provides solutions that relay fast, accurate people data so businesses can enhance productivity and performance, increase employee satisfaction and retention, ensure profitable career planning and ethically upgrade future decision making.
Wong said Visier develops its solution with a combination of Scala, Angular, open source algorithms and proprietary technologies. He said Visier use AI to enrich the data of an organization with standardized information, enabling organizations to better compare and understand trends over time. He also said Visier provides proven ML predictions that have been verified across hundreds of enterprises.
“The prediction models learn patterns from the employee data or organizations and synthesize them into easy-to-understand and actionable information. Visier also uses AI to support analysts in the organization by analyzing the data of an organization as it is created, highlighting and alerting users to new patterns, outliers and potential issues.”
While Visier has competition in niche people analytics vendors like One Model and Crunchr, Wong said the company is designed to help organizations accelerate their people analytics strategy in three key areas where other systems and analytics processes fail or fall short. These areas include data management, deployment and user experience. Visier’s list of competitors also includes HCM suite analytics vendors like Workday and Oracle as well as DIY people analytics using generic BI tools like Tableau and PowerBI.
The company continues to focus on answering the important questions business owners need to grasp how to shape a better business model overall. Having raised $125 million in a Series E funding round last year, Visier is on the path to expanding its global influence.
Customers include Electronic Arts, Uber, Adobe and more, Visier is expanding its presence in 75 countries with much room to grow.
Founder(s): Alexander Hagerup, Kristoffer Roil, Rune Løyning
Headquarters: New York, USA
Total funding to date: $62.7 million
The founders of Vic.ai set out to reimagine accounting using autonomy and AI. Kristoffer Roil, cofounder and COO at Vic.ai, said Vic.ai is ushering in a new era of intelligent accounting by eliminating manual data entry and completely automating invoice processing — the most manual and inefficient task in accounting.
According to Vic.ai cofounder and CEO, Alexander Hagerup, Vic.ai uses proprietary AI technology with algorithms that, having been trained on more than half a billion pieces of data, can handle invoices of all types and formats. The AI operates at up to 99% accuracy, and customers see up to 80% process improvement. Vic.ai also provides customers with business intelligence. By deriving valuable information from financial transactions in real-time, leaders can gain a financial edge by making better decisions faster.
Unlike RPA solutions, Roil said Vic.ai’s platform doesn’t require rules, templates or configuration to work as it’s been trained on over half a billion invoices and continues to learn from data every day. Reading an invoice is easy, he said, but classifying it correctly requires intelligence — either by a human or more efficiently by an AI solution like Vic.ai.
“By pre-training Vic.ai with historical data, you start with incredibly high accuracy rates. Over time, the system learns, adapts and improves to the point where a large percentage of invoices can be processed autonomously. It isn’t only able to read the invoice, but it’s also able to classify a number on an invoice and the correct type of cost,” said Hagerup.
While Vic.ai’s biggest competitors include AppZen, ABBYY, Smartli and Mineraltree, the company will continue to pioneer the use of autonomy and intelligence to improve productivity, decision-making, and ROI within accounting and finance processes.
Founder(s): Ram Swaminathan, Sudarsun Santhiappan, Venkatesh Prabhu
Headquarters: New York, USA
Total funding to date: Undisclosed
The healthcare industry is seeing an astronomical increase in the use of AI, with a report by Gartner saying healthcare organizations’ strategic understanding of AI has matured rapidly. New York-based deep learning platform company, BUDDI AI, is on the quest to bring digital transformation to the healthcare industry with AI. BUDDI AI provides clinical and revenue cycle automation solutions for healthcare. The company claims its AI-enabled solutions help to turn unstructured data in healthcare organizations into actionable insights for those along the continuum of care.
BUDDI AI cofounder and CEO, Ram Swaminathan, told VentureBeat that BUDDI AI’s platform extracts clinical context and automates functions that improve patient care, enhance clinical documentation, streamline medical coding accuracy and improve reimbursements — all of which are integral to a healthy revenue cycle.
Swaminathan said since the last 6+ years BUDDI AI has innovated an ensemble of proprietary algorithms to perform natural language processing, clinical contextual graphs, natural language generation, negation detectors, optical character recognition, tabular column extraction and several more. The company has 50+ AI as a Service (AIaaS) offerings specifically designed for automating healthcare functions, while offering one of the industry’s best efficacies for production use, according to Swaminathan.
BUDDI AI’s competitors include traditional manual medical coding and medical billing shops that consider practically all other semi-automation companies like Optum, 3M, EPIC, Cerner, Eclinicalworks or Athena Health as collaborators. However, Swaminathan said BUDDI AI is differentiated from all of them because it autonomously performs medical coding and medical billing across all out-patient medical specialties. He said BUDD AI does this by using deep learning algorithms combined with sophisticated systems built by experts — offering contractual guarantees of over 95% accuracy on codes and claims for more than 70% of the monthly volumes.
Founder(s): Craig Unger
Headquarters: Washington DC., USA
Total funding to date: $22.3 million
Hyperproof is a compliance operations SaaS platform that aims to make it easier for companies to follow security and compliance protocols. CEO and founder, Craig Unger, began Hyperproof to ensure businesses could complete their compliance work without the redundant, time-consuming and faulty manual processes that often exist.
According to Unger, Hyperpoof plans to leverage ML in several different ways — including eliminating repetitive compliance tasks and providing meaningful risk insights to users so that they can make better, more strategic decisions.
“Hyperproof will use ML to help our users automatically identify/flag the overlapping requirements across various compliance frameworks — so they can see areas where they’re already meeting requirements and reuse their compliance artifacts to satisfy new requirements.”
Later this year, Hyperproof will unveil ML-enabled solutions that automatically identify opportunities for users to set up integrations that will pull in compliance and also help users to gauge how prepared they are for an upcoming audit.
Coalfire’s 2020 survey found that 51% of cybersecurity professionals are spending 40% or more of their budgets on compliance. With $16.5 million Series A funding raised in Q4 of 2021, Hyperproof is helping businesses scale and gain visibility by staying compliant. Unger said Hyperproof is the only platform laser-focused on compliance operations to support the people in the trenches who are overwhelmed with compliance/assurance demands from their organization’s customers and regulatory bodies.
Data privacy is a major concern in business today, according to Unger. He said the capability to efficiently track, implement and enforce ongoing compliance measures enables organizations to meet higher goals while securely protecting their employees, customers and shareholders. All of this contributes to risk management, audit preparedness and seamless operations.
Hyperproof has built dozens of integrations with cloud services that house compliance data including AWS, Azure, GitHub, Okta, Jamf, Jira, ZenDesk and others — enabling automated evidence gathering and seamless collaboration between organizational stakeholders.
Founder(s): Keith Graham and Stephen Cox
Headquarters: Virginia, USA
Total funding to date: $11.3 million
Keith Graham and Stephen Cox claim they are reinventing the Customer Identity and Access Management (CIAM) space by putting the “C” back in CIAM. Legacy vendors in this space built their solutions primarily for B2E use — prioritizing security and compliance above all else, usually leaving customer experience as an afterthought.
Strivacity provides a low-code solution that adds secure customer identity and access management (CIAM) capabilities to a brand’s online properties fast so they can scale to customer demand, grow revenue, stay compliant with fast-changing privacy regulations and personalize their service. Strivacity ingests data derived from ML-based behavioral models as a risk indicator at any point in the consumer lifecycle — helping companies make critical decisions like whether to allow a particular lifecycle event to proceed, or shut down an event entirely when it seems too risky and more.
Companies that believe that customer experience, security and compliance are equally vital to the success of their business benefit from Strivacity’s approach to CIAM. For example, a technology company that works with Strivacity noted that Strivacity provides a comprehensive approach to CIAM, and they’re intentional about making sure they’re meeting all the right stakeholders’ needs, from customers to security teams to marketing.
“We hear from our customers that, on average, using Strivacity versus another provider reduces development and operational costs by 50% with our workflows and APIs that you can drop right into your apps,” said Graham.
Founder(s): Gudmundur Kristjansson
Headquarters: Reykjavík, Iceland
Total funding to date: $8.1 million
Lucinity iCEO and founder, Gudmundur Kristjansson, told VentureBeat Lucinity is on a quest to change the world with its anti-money laundering (AML) technology which empowers banks, fintechs and others in the financial services ecosystem to make data-driven decisions.
Today, hardly 1% of money laundering instances are detected or recovered, despite growing regulations and strains on compliance professionals, according to Kristjansson. He said Lucinity’s API-first approach enables it to deploy cutting-edge tech throughout the company’s stack — such as Spark, Kubernetes and React — which has shown to be a successful scale strategy.
Lucinity’s unique experience in banking, compliance, regulation and data science has helped them develop a new approach to tackling money laundering — harnessing the best of human intelligence and augmenting it with advanced AI. Their proprietary SaaS platform helps banks quickly identify suspicious behaviors and risk exposures. Lucinity’s behavioral detection empowers compliance teams to not only observe customers’ activity, but to understand them holistically and in-depth, ensuring a leading position in compliance.
Other companies try to solve money laundering with AI for AI’s sake, said Kristjansson, but Lucinity focuses on the intersection of humans and machines instead.
“At Lucinity, we use Human AI to explain AI findings so that every compliance professional can take on financial crime with the help of technology. We evolve our models with every new client and our programs get better every day. We work with clients to future-proof their business,” he said.
With a focus on simple-to-use systems that work with analysts, not against them, Lucinity helps banks and fintech’s to get that time and money back with a beautiful, efficient, and effective interface designed around the specific needs of modern compliance.
Founder(s): Brett Coffin, Hari Sundram
Headquarters: San Francisco, California, USA
Total funding to date: $6 million
Verikai stands as a predictive risk assessment software for the insurance industry. Using ML to help insurance companies and underwriters assess risk, the company says it’s currently the only predictive data tool in the “insurtech market.” With a database of over 1.3 trillion data markers, 5,000 behavior patterns and an abundance of factors that account for over 250 million people, Verikai gives insurance companies insights into individual and group risk like never before.
Hari Sundram, founder and CEO, said Verikai is a predictive data and risk tool for insurance underwriters and brokers. He said alternative data and ML are the core base of Verikai’s products, and they will always have a huge impact on the tools the company provides.
Calculating clinical outcomes and behavioral attributes using big data can now give insurance providers accurate, cost-effective forecasts. Real-time census risk reports from Verikai help professionals reduce losses, strategize and improve the complete underwriting process. The company is also providing its business customers with access to suitable insurance products to help HR and employees receive the insurance they need.
“As our ML models continue to mature and as we discover new data sources the ability to provide our customers with the best product models is always our number one priority,” said Sundram.
Founder(s): Franki Chamaki, Jason Hosking
Headquarters: Sydney, Australia
Total funding to date: $4.6 million
HIVERY hopes to fundamentally change the way consumer packaged goods (CPG) companies and retailers collaborate with regard to assortment and space decisions. HIVERY Curate uses proprietary ML and applied mathematics algorithms that have been developed and acquired from Australia’s national science agency — CSIRO’s Data61. With HIVERY Curate, a process that takes 6 months is reduced to around 6 minutes, all with the power of AI/ML and applied mathematics techniques.
Franki Chamaki, cofounder and CEO at HIVERY, said HIVERY’s customers are able to make rapid assortment scenario strategies simulations around SKU rationalization, SKU introduction and space while considering any category goal, merchandising rules and demand transference with HIVERY Curate. Once a strategy is determined, said Chamaki, HIVERY Curate can generate accompanying planograms for execution.
HIVERY’s proprietary ML models use recommender systems. These ML models can learn from clients’ datasets to make recommendations on assortment at store-level or at any cluster store count required. HIVERY combines ML with applied mathematics methods, often called “operations research” or “OR”. While HIVERY’s ML models recommend products, its OR algorithms factor in real world rules or constraints to ensure that any recommendations are practical, operational and product-space aware at store level.
Chamaki said retailers and CPGs currently require multiple solution providers to determine assortment or category strategy, optimize assortment, space, and generate store-level planograms. HIVERY, however, can run assortment strategy simulation and take into consideration any category goals and merchandising constraints into its recommendations — all in one solution — which Chamaki said no company does as of now.
The company earned a spot on Forbes Asia’s 100 companies to watch list last year and was more recently named by CB Insights in its 2022 Retail Tech 100 report — an annual ranking of the 100 most promising B2B retail tech companies in the world.
Founder(s): George Kailas, Adam Plante, and Niles Plante
Headquarters: New York, USA
Total funding to date: Undisclosed
Prospero.Ai says it’s committed to leveling the playing field in investing with AI and ML as the pillars of its solution. Prospero’s cofounders, George Kailas, Adam Plante and Niles Plante, created a platform that aims to make finance more fair and prosperous for all. Previously from the hedge fund world, CEO George Kailas is passionate about providing institutional quality investment research for free without conflict of interest.
Other fintech companies don’t offer their users the most valuable commodity — the predictions derived from their data — but Prospero is doing things differently, said Kailas. Prospero’s joint IP with NYU, a proprietary AI system, simplifies stock analysis into 10 key signals and educates on how to leverage their predictions to invest better.
“Prospero is the first platform that’s completely free while protecting users’ privacy completely. Currently in beta, it aims to reverse the deterioration of the middle class by providing financial tools and literacy for all,” he said.
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