Acsia Technologies Powers Central Institute on Mental Retardation (CIMR) with Solar Energy
Sep 14, 2024

Thiruvananthapuram, September 10, 2024: Acsia Technologies, a global leader in automotive software solutions for Digital Cockpits & Displays, e-Mobility, and Telematics, has announced the solarization of the Central Institute on Mental Retardation (CIMR) in Thiruvananthapuram as part of its Corporate Social Responsibility (CSR) initiative. The project was inaugurated by K. N. Balagopal, Hon’ble Minister for Finance, Government of Kerala, who lit the traditional lamp to mark the commencement of the 10-kilowatt on-grid solar system at CIMR, symbolizing a significant step towards sustainability and energy efficiency.

The new solar installation is expected to generate an average of 40 units of energy per day, contributing to the Institute’s energy needs and enabling CIMR to sell any surplus energy back to the grid.

“All great philanthropic ideas and institutions like CIMR face many difficulties. The government will always support the running of such institutions; however, cooperation from both the public and private sectors is also needed,” said K. N. Balagopal, Hon’ble Minister for Finance, Government of Kerala. The minister also commended Acsia Technologies for its exemplary activities.

“As part of our CSR initiative, Acsia is committed to supporting education and sustainability projects. This solarization project at CIMR not only promotes clean energy but also supports the Institute in its mission to provide essential services,” said Jijimon Chandran, Founder and CEO of Acsia. “I am also pleased to collaborate with the esteemed organization CIMR, which has become a beacon of hope and opportunity for thousands of children both in Kerala and beyond.”

“This project will provide significant strength and energy to the Central Institute on Mental Retardation’s efforts to support individuals with mental challenges,” said Fr. Thomas Chenganariparampil, President of CIMR. He also expressed his heartfelt gratitude to Acsia’s team for their commendable contribution.

Jijimon Chandran, Founder and CEO of Acsia, Fr. Thomas Chenganariparampil, President of CIMR, and Rajesh Panicker, Chief Financial Officer of Acsia, also spoke at the event.

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Athul Lal A G
Director of PR
Email: athul.lal@acsiatech.com
Mob: +91 81290 07793

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
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Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

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Build an AI-powered log analytics assistant that can:

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Goal

Deliver a working prototype that:

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  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

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  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
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Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

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  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

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  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
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  • Improved accessibility and inclusivity for diverse user needs.
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AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
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  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
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  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
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Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
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  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

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Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
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  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
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