[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

GoGo AI Network Portfolio Company Algo8 Deploys AI Decision Support System to Optimize Energy Use at Major Indian Steel & Infrastructure Group

GoGo Al.jpg

GoGo AI Network announced that its portfolio company, Algo8 Industrial AI (“Algo8”), has successfully deployed an AI-based decision support system for a major publicly listed Indian steel and infrastructure group (customer name withheld for confidentiality).

Key highlights

  • Optimization trials indicated average export power savings of approximately 4.39 megawatts (MW), improved export efficiency, and a significant reduction in infirm/unscheduled power, with no adverse impact on production throughput.
  • Digitalized operational logbooks improved workforce productivity by approximately 10.3 labor hours per day (more than 3,700 hours annually).
  • The deployment includes a Power Schedule Mode that automates forward-looking power distribution plans to improve coordination across production, load distribution, and power management teams.

Deployment scope: integrated, energy-intensive steel operations

The customer operates complex steelmaking facilities incorporating Electric Arc Furnaces (EAFs), Ladle Refining Furnaces (LRFs), auxiliary steelmaking units, and a dedicated captive power plant. Balancing real-time power demand across these assets is critical under fluctuating load conditions, where inefficient energy allocation can create material operating costs.

Also Read: AiThority Interview With Arun Subramaniyan, Founder & CEO, Articul8 AI

Solution overview: Algo8’s PlantBrain platform

Algo8’s PlantBrain platform uses machine-learning models to analyze real-time power consumption and generate actionable recommendations to help operators:

Related Posts
1 of 42,567
  • Optimize power utilization across operational units
  • Recommend start/stop strategies for refining furnaces
  • Predict optimal ramp-up and ramp-down levels for captive power generation
  • Improve utilization of import limits and reduce unnecessary power wastage

Power scheduling and operational resilience

The Power Schedule Mode enables automated generation of forward-looking power distribution plans, improving visibility and cross-functional alignment, particularly during unplanned events or system constraints.

Strategic importance for GoGo AI Network

“This deployment reflects the growing adoption of industrial AI solutions across large-scale manufacturing environments,” said Brandon Kou, President of GoGo AI Network Inc. “Algo8’s ability to deliver measurable efficiency gains for major industrial enterprises underscores the strength of its technology platform and reinforces our investment thesis focused on scalable, revenue-generating AI companies.”

“This implementation highlights the role AI can play in optimizing complex energy systems,” said Nandan Mishra, CEO and Co-Founder of Algo8. “We are excited to support our customer in advancing intelligent and sustainable industrial operations.”

GoGo AI Network Inc. is an investment issuer focused on identifying, investing in, and supporting early-stage and growth-stage companies developing artificial intelligence, automation, and next-generation software technologies. The Company targets opportunities across multiple sectors and geographies and seeks to create long-term shareholder value through disciplined capital allocation, active portfolio support, and the strategic monetization of its investments over time.

Also Read: Cheap and Fast: The Strategy of LLM Cascading (Frugal GPT)

[To share your insights with us, please write to psen@itechseries.com]

Comments are closed.