Neuron7 employs open source AI tools for field service across devices

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Neuron7.ai emerged from stealth this week to reveal its platform that combines various open source AI technologies to automate field service across many types of devices. The product’s promise earned the company $4.2 million in seed funding from Nexus Venture Partners and Battery Ventures.

Naturally, there’s already a fair number of organizations attempting to apply AI to a wide range of field service issues, from optimizing traffic routes to encouraging customers to engage bots rather than humans to resolve an issue.

It’s not likely AI platforms are going to replace the need for field technicians anytime soon, given all the issues that might be encountered once a device is deployed. However, AI will clearly play a significant role in enabling a limited number of field service technicians to support a much wider range of devices deployed anywhere in the world.

Neuron7.ai is building a platform that consumes the recommendations created by open source AI engines and models. The aim is to make AI technologies accessible to organizations that typically don’t have the resources required to build AI models that specifically address the unique needs of a field service team, said Neuron7.ai CEO Niken Patel.

Open source AI tools

The Neuron7 platform ingests structured and unstructured data from a wide range of sources, including product and service manuals, knowledge bases, technician notes, customer relationship management (CRM) systems, and messaging systems such as Slack. It then applies various open source AI engines based on frameworks such as TensorFlow to determine how to best remediate a performance issue or an outright device failure, said Patel.

Designed as a software-as-a-service (SaaS) application, Neuron7’s goal is to make AI accessible to organizations that need to optimize field service across an increasing array of devices that require remote support by technicians, Patel said. Technicians can’t be expected to be experts on every potential issue or parameter for all those different devices — “No one can be an expert on every device,” he said.

In addition to aggregating all the data that technicians require to resolve an issue as soon as possible, Patel said, Neuron7 captures the unique knowledge and expertise of the technicians that service the devices to ultimately make the AI platform more accurate. That capability mitigates turnover issues that occur when experienced technicians leave an organization and new ones are onboarded.

Investing in service

Pricing for the Neuron7 platform is based on a subscription model, with tiers that depend on the number of data sets that need to be trained. However, Patel said the company is hoping to shift to a pricing model that is based more on the outcomes enabled by the platform.

Angel investors, early backers, and advisors of the company include Akash Palkhiwala, CFO at Qualcomm; Ashish Agarwal, CEO of Neudesic Global Services; Kintan Brahmbhatt, general manager for Amazon Podcasts; and Anand Chandrasekaran, executive vice president for Five9.

In the age of COVID-19, organizations are looking for ways to automate service management as much as possible to reduce the number of technicians they need to dispatch. Achieving that goal requires organizations to provide customer support technicians with as much relevant data as possible so they can resolve any issues remotely. The challenge is that the devices being deployed in B2C and B2B environments are becoming more complex, Patel said. As more complex devices are connected within an internet of things (IoT) application environment, the need to augment technicians with an AI platform becomes more pressing, he added.