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Aquant, a platform leveraging AI to support customer service workers, today announced that it closed a $70 million series C funding round led by Qumra Capital, Insight Partners, and Pitango Growth with participation from Lightspeed Venture Partners and Angular Ventures. The capital brings the company’s total raised to date to $110 million, and CEO Shahar Chen says it’ll be used to bolster product development and expand Aquant’s engineering, client services, and go-to-market teams with positions in the U.S., Europe, and Israel.
Customer service teams are increasingly embracing AI and automation as the pandemic continues to put a strain on day-to-day operations. According to Gartner, 37% of service leaders are either piloting or using AI bots and virtual customer assistants, while 67% believe they’re high-value tools in the contact center. Early adopters of AI solutions report a 25% improvement in customer experience and accelerated rates of innovation, as well as higher competitiveness, higher margins, and better employee experiences, IDC reports.
New York-based Aquant, which was founded in 2016 by Chen and Assaf Melochna, aims to give service leaders, reps, and teams information they need proactively and on-demand. Aquant transforms unstructured data into structured data and then predicts the solutions for service challenges.
Above: Aquant’s dashboard.
“By providing deep analysis of a company’s service data, Aquant is able to present a full-body health report that shows strengths, weaknesses, and opportunity for improvement,” Chen told VentureBeat via email. “The platform provides a clear map of a service organization. The same service data also helps to provide specific service recommendations, helping field engineers fix the root cause of the problem correctly the first time. From an executive standpoint, Aquant helps direct service leaders where to look, show them what needs attention, help them prioritize, help them develop training plans — all which ultimately lead to better, more magical service.”
Making sense of data
There’s a long list of common challenges in the customer service industry, particularly in field service. In a 2018 survey, the biggest concern for managers is ensuring that their field workforce is operating at optimal efficiency and productivity. An estimated 66% of the workforce uses knowledge bases — e.g., online FAQs — compared with 82% of customers. That’s why 70% of service organizations believe they’ll be burdened by the knowledge loss of a retiring workforce in the next 5 to 10 years, the Service Council reports.
Aquant seeks to address the challenge by mining data from various sources to learn manufacturing, utilities, and telecom companies’ unique service languages. At a high level, the platform captures the knowledge of subject-matter experts by extracting insights from data silos like customer relationship management platforms and enterprise resource planning software. According to Chen, Aquant takes only days to learn a service language from millions of customer tickets, parts catalogs, inventory, supply chains, internet of things alerts, and more.
Aquant’s AI algorithms identify patterns and make decisions as they interpret the differences in the way that service issues are described. The platform then extrapolates context and intent and maps problems to solutions as it prioritizes technicians’ job schedules based on business goals. For example, Aquant can predict when customer complaints are the result of error or environmental factors versus product failure. Moreover, the system can automatically prompt team members to respond, recommending solutions based on cost-effectiveness while searching for anomalies in warranty claims.
“[Our] platform … maps out everything from employee performance to part and asset performance and can create real-time customer risk alerts. Managers, directors, and VPs can now see very clear connections between their team and service performance levels and how those factors contribute to service outcomes,” Chen explained. “[We also provide] intelligent triage, which transforms tribal knowledge into prescriptive intelligence, enabling call center agents and field techs to troubleshoot problems. A customer explains the problem and answers a few short additional questions, and our AI then recommends the most likely or most cost-effective solution for the problem.”
Aquant recently launched Service Insights, a tool that provides a window into factors that impact customer experiences as well as recommendations, industry benchmarks, trends, workforce performance stats, customer risk scores, and training strategies. And in August, the company rolled out Intelligent Warranty Audit, which categorizes data to help warranty managers process or reject “high-risk” claims.
“Our customers — service executives — face a major problem: a shortage of skilled labor. This problem is the result of an aging workforce, a lack of interest in the skilled trades by younger generations, higher turnover of Millennial and Gen Z employees, and the acceleration of the labor shortage caused by the pandemic,” Chen continued. “Service leaders struggle to find and retain qualified service technicians, and, as a result, the quality of service and customer experiences suffer. Aquant offers a solution that captures and disseminates the knowledge of our clients’ most skilled employees — so that new and underperforming employees can have the knowledge to provide great service experiences every time.”
Aquant’s rivals in the global customer service automation market include Zendesk-backed Cleverly.ai, Kustomer, Directly, Zinier, and to a lesser extent TechSee and CareAR. But the company has managed to carve out a niche for itself, with over 30,000 users across customers including The Home Depot and Siemens Healthineers in industries including medical device, food equipment, capital equipment, and industrial automation and appliance manufacturing.
“The most frequent competitive decision we face is that many service organizations are attempting to build their own AI platforms in-house, but that can often take several years and require an entire team of data scientists, analysts, and IT integrators,” Chen said. “Plus, they lack the historical knowledge from other Aquant customers that is built into our data algorithms. Aquant’s AI factors in industry knowledge and data and combines that with a service company’s data, which means that our results are able to provide solutions based on a larger dataset and industry context.”
Aquant currently has 90 employees and expects to have over 120 by the end of the year.
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