Businesses and their customers rely on equipment now more than ever before. And when that equipment – whether it powers a network, is involved in chemical or industrial processes, assists in health care, or delivers fun and games – fails, organizations and their stakeholders can feel the pain.
But what if you could avoid that pain?
That’s what Palo Alto-based eKryp works to help businesses do.
The company offers a software-as-a-service-based Prescriptive Service Intelligence solution that enables organizations reduce their operational costs and downtime.
It employs applied machine learning to predict equipment failures and demand for parts, and to understand when service may be required. That way, businesses in the chemical equipment, gaming equipment, high-tech equipment, industrial equipment, internal assets, and medical equipment fields can get ahead of such problems.
For example, companies that make slot machines (and the casinos that house them) lose big money if those devices malfunction or fail completely. So eKryp uses data from gaming equipment sensors, usage information, and historical data to understand behavioral and time-based patterns. And the eKryp dashboard enables gaming companies to identify potential problems and develop field service schedules to address them proactively. Karpagam Narayanan, founder and president of eKryp, says the platform could even be used to measure a service provider or technician’s performance.
Narayanan established eKryp last March. She says the company’s solution falls into the AI service management category. And she says there’s now a shift in industry from using systems of record like CRM and doing analytics on top of that to using intelligence as a platform and the prescription as the action. In this new world, she adds, prescription and data drive the workflow, instead of taking the workflow and driving data from that.
There’s a lot of interest in AI right now, she notes, but many organizations are unsure where to start with it. Narayanan suggests a great way to get a toe in the water is to begin using AI with just one account, one customer. That customer’s AI pilot can then serve as a proof point, and the supplier can offer to deduct the cost of the pilot from the customer’s later fees. That helps everyone involved be more confident in moving their AI efforts forward, advises Narayanan, who was a speaker at MSP Expo in May.
The next MSP Expo – to take place in January/February in Fort Lauderdale, Fla., is now accepting pitches for speakers and sessions. For more information on that event and how to submit, click here.
Edited by
Maurice Nagle
