Aquant was founded in 2016 by Shahar Chen and Assaf Melochna to tackle a costly problem in service organizations: the widening skills gap as experienced technicians retire and complex machinery becomes harder to troubleshoot. Their platform applies AI to the messy, domain-specific data inside service operations, work orders, service notes, manuals and the tacit knowledge of top experts, to deliver accurate diagnoses and step-by-step resolution guidance for technical support and field service.
The heart of Aquant is its Service Co-Pilot, which acts as an AI expert that any agent or technician can consult. By ingesting historical service records and product documentation, the system understands how specific symptoms map to root causes and recommended fixes for particular equipment. This means less experienced reps can resolve issues that previously required a senior specialist, reducing escalations, repeat visits and downtime. Aquant also provides triage that determines whether an issue can be solved remotely or requires a truck roll, optimizing costly field dispatch.
Beyond frontline assistance, Aquant offers analytics and insights that help service leaders understand performance, identify recurring failure patterns and quantify the cost of service. Its focus on complex, equipment-centric support distinguishes it from general CX chatbots: Aquant is designed for industries like medical devices, industrial equipment, manufacturing and consumer durables where accurate technical resolution carries high stakes.
Aquant has raised more than $130 million across multiple rounds, including a $30 million Series B led by Insight Partners and a $70 million Series C led by Qumra Capital, with additional backing from Lightspeed Venture Partners and Pitango. With its service-specific AI and proven enterprise traction, Aquant targets service and support organizations that need to close the expertise gap and resolve complex technical issues faster.
