Tensor Planet

Tensor Planet is revolutionizing commercial fleet maintenance with AI. We empower haulers to streamline operations, augment revenue, and slash costs.

[Tensor Planet @TensorPlanet]

Read the case study here: https://t.co/x3PLDP9PKO

[Tensor Planet @TensorPlanet]

𝐀 𝟓𝟓-𝐭𝐫𝐮𝐜𝐤 𝐟𝐥𝐞𝐞𝐭 𝐫𝐚𝐧 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭: ����𝐭𝐨𝐩 𝐭𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚𝐥𝐥 𝐭𝐫𝐮𝐜𝐤𝐬 𝐞𝐪𝐮𝐚𝐥𝐥𝐲.

They shifted from:
“What needs service this week?”

To:
“Which trucks are most likely to fail next?”

That shift was powered by...

[Tensor Planet @TensorPlanet]

𝗡𝗼 𝗺𝗮𝗷𝗼𝗿 𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺.

What changed? Better prioritization.

This is why most fleets do not need more data first. They need a better decision layer between the signals and the worklist.

#FleetUptime #FleetMaintenance #PredictiveMaintenance #AI

[Tensor Planet @TensorPlanet]

Would you drive a truck the AI gave a 90% chance of failing?

#PredictiveMaintenance #AI #Memes #Breakdown #FleetMaintenance #Trucks

[Tensor Planet @TensorPlanet]

Read the full blog: https://t.co/eWlqUlyms3

#PredictiveMaintenance #FleetMaintenance #FleetTechnology #Telematics #CommercialVehicles #TensorPlanet #AI

[Tensor Planet @TensorPlanet]

What Happens After a Vehicle Goes Down Mid-Route?

Oil pressure light. Driver is forty minutes from his next stop.

He pulls over immediately. That decision is also where the truck stops moving for the day.

Dispatch scrambles to cover the load. A tow gets called. And depending..

[Tensor Planet @TensorPlanet]

At TensorPlanet, we see fleets catch these risks weeks out, before any fault code fires.

The system flags the deviation, ranks severity, and pairs it with a specific repair recommendation the technician can act on at the next scheduled visit.

[Tensor Planet @TensorPlanet]

...overthinking all three long after work hours.

Her background in physical systems AI meets a problem that's very real for fleet operators: keeping vehicles running longer, with fewer surprises.

Welcome aboard, Nishi - really glad to have you here!

[Tensor Planet @TensorPlanet]

We're thrilled to welcome Dr. Nishi Parikh to the Tensor Planet Inc. team as our Senior Data Scientist! 🎉

A PhD in Machine Learning for Materials Science and a Swiss Government Excellence Fellow, here are a few things you should know about her:

[Tensor Planet @TensorPlanet]

...battery State of Health, degradation analysis, and anomaly detection in EV fleet data.
- Passionate about solving real-world problems with data, exploring new places one road trip at a time, and diving into conversations around AI, psychology, and human behaviour, often...

[Tensor Planet @TensorPlanet]

He's most excited about applying everything he's built across industries to a problem he genuinely believes in, keeping commercial fleets running smarter and longer.

Welcome aboard, Rushikesh - great to have you here!

[Tensor Planet @TensorPlanet]

We're thrilled to welcome Rushikesh Gite to the Tensor Planet Inc. team as our Lead Data Scientist! 🎉

An Indian Institute of Technology, Madras alum and a decade-deep practitioner in industrial AI, here are a few things you should know about him:

[Tensor Planet @TensorPlanet]

...manufacturing, which means he's seen more machine failure patterns than most data scientists.
- From LSTM-based remaining useful life prediction at TCS COE to RAG systems and GenAI agents at Capgemini and KPIT, he brings the full stack of modern industrial AI to the table

[Tensor Planet @TensorPlanet]

Here is where AI-powered Predictive Maintenance comes to the rescue!

#FleetMaintenance #MaintenancePlanning #FleetUptime #AI #TensorPlanet #PredictiveMaintenance #PreventiveMaintenance

[Tensor Planet @TensorPlanet]

Your PM schedule was built for last year's fleet.

It was not built for:

- the routes that changed
- the vehicles that aged out of the original interval assumptions
- the duty cycles that got heavier
- a schedule set in 2025 still running in 2026 does not know any of that.

[Tensor Planet @TensorPlanet]

So when a vehicle fails "between PM visits," it does not mean PM failed.

It means the schedule was never designed for variable conditions. The question is not "did we complete PM?"

The question is "did we inspect the right vehicles at the right time?"