Intangles} digital twin technology for real-time predictive insights for the mobility industry

This startup uses proprietary digital twin technology to provide real-time and predictive insights into the mobile industry.

The mobility ecosystem is facing several challenges due to rising fuel prices, rising labor costs for both skilled and unskilled labor, and labor shortages. As regulations tighten and competition heats up, the entire system is becoming more complex. As these elements accumulate on multiple fronts, fleet operators need to have cost control metrics with the right technology to boost their bottom line while optimizing operations.

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There are various solutions in the market that have tried to meet these pressing needs of fleet managers, but none of them are sufficient to provide the necessary insights. The basic concerns that need to be addressed are the health of the property and the operator.

This is where Entangles comes in, a company that uses proprietary DigitalTune technology to provide real-time predictive insights to the mobile industry. The solution includes the use of proprietary ML algorithms to generate predictive insights about component failure, automated reports using accurate statistics on vehicle performance (fuel consumption, mileage, operating hours), driving behavior and geospatial intelligence.

As of today, the Entangles platform monitors 40K+ assets in 10 countries, onboards nearly 500 fleet operators every month, and crunches an astounding two billion sensory data points every day.

Tech Panda Entangles CEO Anup Patil spoke to them about what they do and how they got there.

Anup Patil

We provide comprehensive predictive insights that enable an operator to receive alerts well in advance of a failure, along with appropriate diagnostic guidelines.

“We provide comprehensive predictive insights and appropriate diagnostic guidelines that allow an operator to receive alerts well before a failure occurs. Such accurate diagnostic guidelines combined with predictive alerts are changing the entire ecosystem where responsibility for vehicle health is a shared responsibility rather than a blame game. This is what Entangles is trying to address.” It is,” he said.

“We recognize that the current suite of analytical tools is inadequate and incapable of addressing these issues. That’s why we’ve taken a completely new approach by creating digital twins of every vehicle component, including the engine, battery, intercooler, turbocharger, and air and fuel intake systems. These virtual twins are from multiple perspectives. We analyze, perform peer-to-peer comparisons and make appropriate recommendations based on the analysis.

What do they do?

The Entangles system uses proprietary hardware that can interface with the latest vehicle diagnostic ports.

“This technology can collect data from various commercial vehicle platforms and power generation technologies. The captured data is sent to the cloud via the cellular network and consumed entirely by a dedicated server backbone,” explained Patil.

The data is fed into a variety of ML algorithms, which simulate the behavior of various systems and components, such as engine cooling and fuel tanks. Using geospatial knowledge, the algorithms generate predictive insights about component breakdowns, detailed statistics on vehicle performance (fuel consumption, mileage, mileage), driving behavior and automated reporting.

Motivation and origin

The Entangles team’s passion for data science and automobile technologies has led them to explore on-board diagnostic data on passenger vehicles.

“It was clearly seen that there was limited scope for predictive health checks on passenger vehicles for good/conventional maintenance of privately owned and operated vehicles. This led us to investigate data flow on commercial vehicles, including trucks and buses, which opened the door to a wide range of opportunities,” recalls Patil.

It was rightly observed that the scope of health inspection on passenger vehicles was limited to ensure good condition/conventional maintenance of privately owned and operated vehicles. This led us to explore the data stream on commercial vehicles, including trucks and buses, which opened the doors to a wide range of possibilities.

“With a clear use case, we have built a hardware interface that can collect data on OEMs, fuel injection and emissions technologies from CV (commercial vehicle) platforms,” ​​he added.

This is complemented by a modern edge-to-cloud communication backbone and proprietary algorithms targeting predictive health alerts, driver behavior profiling, fuel theft and geo-spatial intelligence.

Entangles USP

Entangles’ main point of differentiation is extracting easily visualized, actionable insights from complex telemetry data streams with a view to meeting the KPIs of the average fleet manager.

“This includes very accurate performance data (fuel consumption, distance, running hours), very accurate predictive alerts on failure, diagnostic alerts with detailed metadata (causes, maintenance solutions) and detailed reports on schedules and detailed reports,” Patil explains.

With on-the-go visibility and application-specific performance aggregation, they can better plan their operations with a focus on improved profitability.

Their solutions can predict key engine faults, leading to lower downtime and lower maintenance costs.

“With on-route visibility and application-specific performance aggregates, they can better plan their operations with a focus on improved profitability. Platform-issued fuel spill notifications can quickly reduce trip costs and automated reports on fleet performance reduce the need for human intervention,” he added.

Development history

Entangles is regularly generating around 20,000 predictive alerts per month, which has enabled it to reduce crash events by up to 75%. Patil announced that more than 200,000 liters of fuel had been monitored for corrosion by equipment installed in Indian ships.

The company has achieved a 20-30% improvement by tracking driver behavior over 20-30%. In addition, vehicle breakdowns have decreased, resulting in a 10-30% increase in property availability. It has also helped reduce vehicle maintenance costs by 5-10 percent.

When it comes to EVs in the commercial vehicle market, we aim to be the face of telematics in India from last mile delivery to long haul.

“In the Indian market, we have been well received from every level of industry, from small and medium businesses to large OEMs. The rapid adoption and recognition of our solutions by our largest customers speaks for itself,” he says.

Entangles currently has 7 OEMs onboard in 11 countries where it operates. It also has more than 8,000 flight operators on the platform.

“As of today, the Entangles platform tracks 60K+ assets across 11 countries, onboarding nearly 500 fleet operators every month and crunching an astounding three billion sensory data every day. With some of the biggest brands in mobility signed up as customers, we expect 5x growth in FY’23,” he proudly predicts.

Future plans

As Entangles consolidates its position in the Indian mobility ecosystem, the prospect of new opportunities in North America, Europe, Australia and APAC is very promising, says Patil.

“Our impressive history of growth and expansion demonstrates the game-changing potential of predictive analytics enabled by digital twin technology. We continue our efforts to redefine performance parameters in mobility and transportation.

A digital twin is a system that creates a virtual copy of a real-world asset and is capable of simulation, reasoning and machine learning.

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“When it comes to EVs in the commercial vehicle market, we aim to be the face of telematics in India from last mile delivery to long distance. In terms of technology, we intend to strengthen our leadership position in using digital twin technology to serve issues like engine health and battery health.

The company is strengthening its presence in the medium to heavy commercial vehicle category with IC Engines and expanding its analytics package to include vehicle health monitoring, driver status and fleet operations automation.

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