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Acsia Technologies Wins the CII Industry Academia Partnership Award 2024

Dec 23, 2024
Acsia CII Award

Thiruvananthapuram, 17 December 2024: Acsia Technologies, a global leader in automotive software specialising in Digital Cockpits & Displays, e-Mobility, and Telematics, has been awarded the CII Industry Academia Partnership Award 2024 in the MSME sector (Diamond category). This recognition highlights Acsia’s exemplary efforts in fostering collaborations with academic institutions, bridging the gap between education and industry, and preparing students for real-world challenges in automotive software development.

The award ceremony, held in New Delhi as part of the CII Annual Summit on Technology, Intellectual Property, and Industry-Academia Partnerships, celebrated outstanding contributions by companies, academic, and research institutions fostering meaningful industry-academia synergies.

Reflecting on the recognition, Jijimon Chandran, Founder and CEO of Acsia Technologies, said: “This award validates our belief that collaboration is the cornerstone of innovation. Through programs like ‘Wheels of the Future’ and our mentorship initiatives, we strive to equip the next generation with the skills needed to lead tomorrow’s mobility revolution.”

Acsia congratulates all the winners across categories, including TVS Motors, Tata Motors, Hindustan Aeronautics Limited, HCL Tech, Serum Institute of India, TCS, Mahindra and Mahindra, Uno Minda, IIT Madras, Hindustan Unilever Ltd and Biocon Biologics. Their contributions continue to advance industry-academia partnerships in India.

Acsia’s Industry-Academia Connect Initiatives

Acsia has been at the forefront of creating structured engagements that nurture young talent and foster innovation.

The “Wheels of the Future” Campus Lifecycle Program guides students through progressive stages of learning and industry exposure. It begins with expert talks during induction weeks to familiarise students with industry best practices, followed by self-learning modules and assessments to identify top performers for summer internships. Based on their performance, selected students are offered a month-long internship, culminating in a six-month academic project at Acsia. This hands-on experience provides students with opportunities to work on real-world proof-of-concept projects and opens pathways for potential hiring.

Through collaborative research, Acsia mentored the development of “Vandy”, an electric car designed by students at the Government Engineering College, Barton Hill, Thiruvananthapuram, which gained international recognition at the Shell Eco-Marathon 2022 in Indonesia. The project received awards for its battery thermal management system and AI-driven drowsiness detection system, reflecting the depth of innovation enabled by Acsia’s mentorship.

Acsia also supports academic projects and labs, including the installation of an AC-DC charger at Trinity College of Engineering, Thiruvananthapuram, offering practical exposure to EV infrastructure. Its long-term internship programs provide six months of mentored, hands-on training in real-world automotive software development.

To strengthen technical expertise, Acsia delivers specialised training programs in AUTOSAR at Trinity College of Engineering and RUST/C++ at Mar Baselios College of Engineering and Technology, Thiruvananthapuram, ensuring students acquire industry-relevant skills.

The organisation has also facilitated mentorship programs with industry veterans such as Mr. Stefan Juraschek, Strategic Advisor at Acsia and a key figure behind BMW’s electric vehicle innovations. Stefan’s interactions with students at the College of Engineering, Thiruvananthapuram (CET) and Mar Baselios College of Engineering and Technology, Thiruvananthapuram, offered invaluable insights into electric vehicles, software-defined vehicles (SDV), and the future of mobility.

Acsia’s commitment to Diversity, Equity, and Inclusion (DEI) is evident through its exclusive placement drive for differently abled engineering students, conducted in collaboration with Kerala’s APJ Abdul Kalam Technological University (KTU) on 24th January 2023 at the Mar Baselios College of Engineering and Technology, Thiruvananthapuram. This initiative empowered final-year students across Kerala with career opportunities in engineering.

Through initiatives such as collaborative research, hands-on workshops, and structured training programs, Acsia enables students to gain practical experience, bridging the gap between academia and industry and creating a skilled, future-ready workforce.

About Acsia

Acsia is a leading provider of automotive software powering Digital Cockpits & Displays, e-Mobility and Telematics. We use our expertise across AUTOSAR, Android Automotive, Automotive Linux, QNX, HMI, Middleware and Platform Development, CI/CD/CT, AI/ML, Verification & Validation, Cybersecurity, Functional Safety, and Performance Optimisation, to develop solutions that simplify complex problems and create safer, sustainable, and more compelling driver and passenger experiences.

With a presence across the United States, Germany, Japan, and India, we collaborate with top automobile manufacturers and Tier-1 suppliers.

Press Contact

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.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

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

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
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Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
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AH2025/PS03 | AI/ML

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

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • 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:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • 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.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
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.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • 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).
  • Resource allocation map showing workload distribution across the team.
  • 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.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

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.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • 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:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

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.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.