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XAVI : Your new smart home

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In this world of technologies you can imagine almost anything. Let’s take your home for example. After coming from your hectic day at work, home is the place where you want to spend your time in front of TV, watching your favourite channel peacefully without any distraction. On this very moment what if you need to move to switch on/off the light? Turn on the AC or check the time. What would you possibly think? Oh I wish i had a genie. Yes!! here is the genie to take away your pain and to make your life easy and simple.

Xavi is Mantra Labs’s proprietary software and hardware smart-home platform, letting you control many of your smart-home devices, including lights, TV, Air-conditioner and thermostats. It can be controlled not only via an app but also with your voice.

It is a device which makes for a much smarter TV viewing experience with the consumer querying the mobile app for things to watch or switch to a certain serial.

Currently, you can simply talk to Xavi via your app and ask it “Show me a list of English Action movies starting in next 15 minutes” and it will automatically list it down. Be it switching to your favorite show or watching yet another Friends rerun – just talk to the App and it will do the rest.

XAVI brings smart remote, smart bulb and smart clock for an automated home.

Xavi Remote: Xavi Remote can control TV’s, Air Conditioners and Bulbs for now. It gives you the three way pleasure that you might have only dreamt of, with a single gesture you can adjust your home’s temperature and switch off the lights to enjoy a peaceful movie. Yes! That’s how simple it becomes, when you have a Smart Remote.

Xavi Bulb: Xavi Bulb offers, dimming the light through buttons/sliders built within the application that controls it. Changing colors of the lights according to your mood is another thing a Smart Bulb is usually capable of.

Xavi Clock: Xavi clock could read your notification alerts and check other daily tasks. For example at evening, if you are coming from office, as soon as you are near your home- around 200-400 metres away, the clock would notify that your are about to reach home.

XAVI is in Prototype state however it won’t be long when it will be unleashed for consumers with an overall improved functionality.

Apart from these there are many other smart devices available in the market. See these 10 smart devices to make your home more smarter.

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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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