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AI to control solar panels, and enable power sharing? How US Energy Sector startups are leading the AI race

As the global energy landscape evolves to meet the necessities of climate change and burgeoning demand, Artificial Intelligence (AI) and Machine Learning (ML) are rapidly emerging as linchpins for sustainable energy solutions. The nexus between these advanced technologies and energy sustainability is not merely theoretical but manifests in real-world applications, driving tangible benefits. The global AI market in the energy sector is predicted to reach a staggering $19.2 billion by 2028, and the potential of AI to unlock $1.6 trillion in savings by 2030, underpins the seismic shift underway. This transition is not just an indicator of optimized energy management and reduced greenhouse gas emissions but also a fertile ground for startups to innovate and contribute to a more sustainable, efficient, and resilient energy ecosystem.

The Energy Sector in the USA is Booming At a Rapid Pace

The energy sector stands at the cusp of a transformative phase, with AI and ML being the vanguards of this transformation. 

The global AI market in the energy sector is set to ascend at a CAGR of 25.1%, reaching a valuation of $19.2 billion by 2028, a testament to the growing affinity towards AI-driven solutions. The prowess of AI extends to a potential saving of $1.6 trillion for the global energy sector by 2030, embodying the financial prudence of embracing AI.

In terms of efficiency and sustainability:

What AI Can Do for the Energy Sector in the USA?

A few years back, no one knew how AI could be used in the US energy sector. Now, however, we have pretty splendid examples of companies using AI to enhance the customer experience and sustainability further. Let’s go through a few examples.

Customer Experience

AI is still in its infancy and there’s a lot to come. However, thanks to tech partners like Mantra Labs, energy companies in the US are able to leverage modern technology to enhance their customer experience exponentially. GreenBrilliance (a leading solar panel installer in the US) is one such example. The solution developed by GreenBrilliance helps customers know how many solar panels will be required to power a house, how much power does a solar panel produce, and more. Further, it would help them monitor, control, and troubleshoot their solar panels on their smartphones.

Customer experience is one such thing that has started getting attention in the last few years. B2C companies, irrespective of their industry, not only build products or render services but try to provide the best customer experience possible. Reports predict that Solar could fulfill 40-50% of U.S. electricity demand by 2050. Also, the cost of installing and servicing solar panels has also been reduced by 60% over the last decade. This has been possible only through implementing automation, simplifying operations, and bringing transparency to the customer, along with many other things. 

Efficiency and Sustainability

Efficiency and sustainability are the USPs of solar power and AI is helping to boost that further. 

For example, accurate demand forecasting is pivotal for energy efficiency and cost-effectiveness. Startups like GridX are leveraging AI to predict energy demand and optimize power flows, thereby reducing energy costs and enhancing system efficiency.

Companies like Power Ledger are using AI systems to modernize and decentralize grid systems, enabling efficient trade of solar power among neighbors. This fosters a balanced supply and demand, optimized power flows, and improved grid reliability. Moreover, AI-driven innovative energy storage solutions are instrumental in integrating renewable energy into the grid and enhancing energy security.

These diverse applications underscore the boundless potential of AI and ML to revolutionize the energy sector, making it more sustainable, efficient, and resilient.

What Future AI Promises to the US’s Energy Sector?

As AI technology continues to evolve, the horizon of possibilities in the energy sector broadens. Large corporations like Google, Amazon, and Microsoft, alongside agile startups, are exploring the AI energy landscape continuously, indicating a robust and growing ecosystem.

And it’s not only the private sector that is putting effort into harnessing the power of AI. The US Department of Energy (DOE), the National Renewable Energy Laboratory (NREL), and other governmental bodies are also leveraging AI to pioneer new generations of nuclear reactors, develop new solar and wind technologies, and create smart grid systems.

Integration of AI and ML with the energy sector is a narrative of innovation, sustainability, and vast potential. The journey of companies like Green Brilliance Predictum, Heliogen, GridX, Amperio, and Power Ledger illustrates the transformative power of AI and ML in forging a sustainable energy future. As the global AI market in the energy sector burges, the call for startups to innovate and contribute to this burgeoning ecosystem is loud and clear. The narrative of AI and ML in the energy sector is still being written, and startups have a golden opportunity to be the authors of many success stories in this narrative.

Further Readings: Bringing Solar Renewable Energy Closer to Consumers in the USA

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Smart Manufacturing Dashboards: A Real-Time Guide for Data-Driven Ops

Smart Manufacturing starts with real-time visibility.

Manufacturing companies today generate data by the second through sensors, machines, ERP systems, and MES platforms. But without real-time insights, even the most advanced production lines are essentially flying blind.

Manufacturers are implementing real-time dashboards that serve as control towers for their daily operations, enabling them to shift from reactive to proactive decision-making. These tools are essential to the evolution of Smart Manufacturing, where connected systems, automation, and intelligent analytics come together to drive measurable impact.

Data is available, but what’s missing is timely action.

For many plant leaders and COOs, one challenge persists: operational data is dispersed throughout systems, delayed, or hidden in spreadsheets. And this delay turns into a liability.

Real-time dashboards help uncover critical answers:

  • What caused downtime during last night’s shift?
  • Was there a delay in maintenance response?
  • Did a specific inventory threshold trigger a quality issue?

By converting raw inputs into real-time manufacturing analytics, dashboards make operational intelligence accessible to operators, supervisors, and leadership alike, enabling teams to anticipate problems rather than react to them.

1. Why Static Reports Fall Short

  • Reports often arrive late—after downtime, delays, or defects have occurred.
  • Disconnected data across ERP, MES, and sensors limits cross-functional insights.
  • Static formats lack embedded logic for proactive decision support.

2. What Real-Time Dashboards Enable

Line performance and downtime trends
Track OEE in real time and identify underperforming lines.

Predictive maintenance alerts
Utilize historical and sensor data to identify potential part failures in advance.

Inventory heat maps & reorder thresholds
Anticipate stockouts or overstocks based on dynamic reorder points.

Quality metrics linked to operator actions
Isolate shifts or procedures correlated with spikes in defects or rework.

These insights allow production teams to drive day-to-day operations in line with Smart Manufacturing principles.

3. Dashboards That Drive Action

Role-based dashboards
Dashboards can be configured for machine operators, shift supervisors, and plant managers, each with a tailored view of KPIs.

Embedded alerts and nudges
Real-time prompts, like “Line 4 below efficiency threshold for 15+ minutes,” reduce response times and minimize disruptions.

Cross-functional drill-downs
Teams can identify root causes more quickly because users can move from plant-wide overviews to detailed machine-level data in seconds.

4. What Powers These Dashboards

Data lakehouse integration
Unified access to ERP, MES, IoT sensor, and QA systems—ensuring reliable and timely manufacturing analytics.

ETL pipelines
Real-time data ingestion from high-frequency sources with minimal latency.

Visualization tools
Custom builds using Power BI, or customized solutions designed for frontline usability and operational impact.

Smart Manufacturing in Action: Reducing Market Response Time from 48 Hours to 30 Minutes

Mantra Labs partnered with a North American die-casting manufacturer to unify its operational data into a real-time dashboard. Fragmented data, manual reporting, delayed pricing decisions, and inconsistent data quality hindered operational efficiency and strategic decision-making.

Tech Enablement:

  • Centralized Data Hub with real-time access to critical business insights.
  • Automated report generation with data ingestion and processing.
  • Accurate price modeling with real-time visibility into metal price trends, cost impacts, and customer-specific pricing scenarios. 
  • Proactive market analysis with intuitive Power BI dashboards and reports.

Business Outcomes:

  • Faster response to machine alerts
  • Quality incidents traced to specific operator workflows
  • 4X faster access to insights led to improved inventory optimization.

As this case shows, real-time dashboards are not just operational tools—they’re strategic enablers. 

(Learn More: Powering the Future of Metal Manufacturing with Data Engineering)

Key Takeaways: Smart Manufacturing Dashboards at a Glance

AspectWhat You Should Know
1. Why Static Reports Fall ShortDelayed insights after issues occur
Disconnected systems (ERP, MES, sensors)
No real-time alerts or embedded decision logic
2. What Real-Time Dashboards EnableTrack OEE and downtime in real-time
Predictive maintenance using sensor data
Dynamic inventory heat maps
Quality linked to operators
3. Dashboards That Drive ActionRole-based views (operator to CEO)
Embedded alerts like “Line 4 down for 15+ mins”
Drilldowns from plant-level to machine-level
4. What Powers These DashboardsUnified Data Lakehouse (ERP + IoT + MES)
Real-time ETL pipelines
Power BI or custom dashboards built for frontline usability

Conclusion

Smart Manufacturing dashboards aren’t just analytics tools—they’re productivity engines. Dashboards that deliver real-time insight empower frontline teams to make faster, better decisions—whether it’s adjusting production schedules, triggering preventive maintenance, or responding to inventory fluctuations.

Explore how Mantra Labs can help you unlock operations intelligence that’s actually usable.

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