Predikto raises $4M from TechOperators, ATA to predict machine failure

By Urvaksh Karkaria
Staff Writer-Atlanta Business Chronicle

Predikto Raises $4m to predict machine failures

Atlanta software firm Predikto has raised nearly $4 million to help manufacturers predict product failures earlier.

Predikto’s software engine — dubbed Max — allows manufacturers, railroad companies and other asset-intensive industries to predict equipment failure and warranty claims.

By detecting failures before they happen, companies can increase productivity, reduce downtime and tweak the manufacturing process to increase production volume, CEO Mario Montag said.

Max — an artificial intelligence and machine learning software robot built to design custom algorithms — uses real-time sensor data, historical maintenance records and past failure data to predict equipment breakdowns.

“Clients give us their data and (Max) spits out algorithms that can predict when a piece of equipment is going to fail,” Montag said

While predictive analytics software is widely used in business, manufacturing has been late to leverage Big Data to squeeze efficiencies.

Predikto is riding the wave of the industrial Internet of Things. Pumps, motors and other parts are being manufactured with on-board sensors that deliver tons of data on the health and performance of the devices.

“The industrial Internet of Things is creating a drive to do more with data,” Montag said. “The big data revolution of being able to do more and chew more information is sweeping through industrial manufacturing.”

Predikto raised the $3.6 million in a Series A round led by TechOperators, an Atlanta early stage venture firm managed by a quartet of serial entrepreneurs with billion-dollar exits under their belts.
Super angel groups Atlanta Technology Angels (ATA) and Angel Investor Management Group (AIM) also participated in the Predikto raise.

Predikto’s technology targeted to specific equipment and industry sectors, and its managed-service approach of delivering insight rather than tools, differentiates the startup, said Said Mohammadioun, partner at TechOperators.

“The market expects solutions rather than tools,” Mohammadioun said. “They don’t want to be in the business of figuring out how to use tools.”

The capital will be used for product development and sales and marketing — critical challenges for the startup.

Predikto must continue to innovate to stay ahead of the competition, while taking as much marketshare as possible, said Stephen Walden, a board member at Atlanta Technology Angels.

“That’s why this raise was so important for them to get into the market quickly,” Walden said. “Right now they’ve got, I won’t say a lock on the market, but a proprietary algorithm that nobody else can yet match.”

Launched in late 2012, Predikto is targeting a large addressable market. The industrial predictive analytics market in North America is estimated at more than $10 billion in annual sales, Montag said.

“Our sweet spots are continuous manufacturing facilities, such as steel plants, food & beverage plants and transportation companies with distributed assets — airplanes, trains and truck fleets,” Montag said. Siemens, for example, uses Predikto software to detect failures in train doors and diesel engines.

While the application of predictive analytics has so far been limited to the financial services and retail sector, the next market is industrial systems. Until recently, engineers typically followed a standardized maintenance schedule for industrial equipment, similar to annual maintenance schedules for automobiles.

Predikto relies on real-time data from sensors in the machinery, such as vibrations, electrical usage, and ambient temperature, to give engineers a more efficient predictive maintenance process. Rising temperatures, for instance, when combined with other things, can be a sign of inadequate lubrication, or an incorrectly fitting part that’s causing friction.

“The secret is in being able to run all these variables at once through a sophisticated algorithm to tell what is really going on,” Walden said.

Keeping maintenance downtime for critical equipment to the minimum required can save operators millions of dollars.

“If an engine that powers a steel mill costs $1 million for every hour it is shut down for maintenance, you’d rather not do (maintenance) every thousand hours, if you can do it every five thousand hours,” Walden said.

In the future, Predikto plans to target the booming oil and gas industry.

“The pain of asset downtime in that industry is significant,” Montag noted, adding it costs $500,000 for every day an oil rig is shutdown.

For Predikto, future success will depend on getting customers to fix things proactively, which requires a change in way of doing business, Mohammadioun said

“Companies know how to fix things that break — now, we are able to tell them when things are going to break,” he said. “That requires a change in culture.”



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