Acsia Technologies Wins CII Industry–Academia Partnership Award 2025 for Second Consecutive Year

Dec 12, 2025
Jayachandran R, Vice President, People & Values, Acsia and Niya Francis, Lead, Acsia Academy receive the award from Bhuvnesh Pratap Singh, IAS, Deputy Secretary, Department for Promotion of Industry & Internal Trade (DPIIT), Government of India in the presence of Rajendra K. Srivastava, Professor & Former Dean, Indian School of Business, Dr. Ashish Mohan, Executive Director, CII, Vishvajit Sahay, Controller General of Defence Accounts (CGDA), and Vipin Sondhi, Chairman, CII Industry-Academia Partnership Award.
Jayachandran R, Vice President, People & Values, Acsia and Niya Francis, Lead, Acsia Academy receive the award from Bhuvnesh Pratap Singh, IAS, Deputy Secretary, Department for Promotion of Industry & Internal Trade (DPIIT), Government of India in the presence of Rajendra K. Srivastava, Professor & Former Dean, Indian School of Business, Dr. Ashish Mohan, Executive Director, CII, Vishvajit Sahay, Controller General of Defence Accounts (CGDA), and Vipin Sondhi, Chairman, CII Industry-Academia Partnership Award.

Thiruvananthapuram, December 12, 2025 — Acsia Technologies has once again been honored with the prestigious CII Industry–Academia Partnership Award, in the Diamond Category for 2025. This recognition underscores Acsia’s unwavering commitment to fostering strong collaboration between industry and academia and driving innovation, research, and skill development.

The CII Industry Academia Partnership Awards aim to recognize pioneers who strengthen the bridge between educational institutions and industry. In its second edition, the event held at the Stein Auditorium, India Habitat Centre, New Delhi on December 5 was inaugurated by Dr. Jitendra Singh, Hon’ble Minister of Science and Technology; Earth Sciences; Prime Minister’s Office; Personnel, Public Grievance and Pensions; Atomic Energy; Space, Government of India.  

The award was received by Jayachandran R, Vice President, People & Values, Acsia and Niya Francis, Lead, Acsia Academy from Bhuvnesh Pratap Singh, IAS, Deputy Secretary, Department for Promotion of Industry & Internal Trade (DPIIT), Government of India in the presence of Rajendra K. Srivastava, Professor & Former Dean, Indian School of Business, Dr. Ashish Mohan, Executive Director, CII, Vishvajit Sahay, Controller General of Defence Accounts (CGDA), and Vipin Sondhi, Chairman, CII Industry-Academia Partnership Award. 

Winning this award for the second consecutive year reflects Acsia’s strategic focus on nurturing talent pipelines, enabling cutting-edge research, and creating real-world impact through collaborative programs with leading academic institutions.

Jijimon Chandran, Founder and CEO of Acsia Technologies, said:
“Receiving this award for the second year in a row is a proud moment for all of us at Acsia. It reaffirms our belief that strong industry–academia partnerships are the cornerstone of innovation. At Acsia, we see this collaboration as a way to empower the next generation of engineers while accelerating advancements in automotive software. This recognition motivates us to continue building bridges between education and industry to shape the future of mobility.”

Over the past year, Acsia has collaborated with several esteemed institutions across Kerala, including Mar Baselios College of Engineering, Sree Chitra Thirunal College of Engineering, Trinity College of Engineering, Marian Engineering College, College of Engineering Trivandrum (CET), Rajagiri School of Engineering and Technology, SCMS School of Engineering and Technology, Central Polytechnic College, Vattiyoorkavu, Government Women’s Polytechnic College, Kaimanam, Amrita Vishwa Vidyapeetham, LBS Institute of Technology for Women, Poojappura, Government Engineering College, Barton Hill, and Government Engineering College, Palakkad.

Through these partnerships, Acsia has successfully executed initiatives such as Campus Lifecycle: Wheels of the Future (exclusive for B.Tech. students), expert talks and facility tours, industry-sponsored faculty development programs (FDP), and research projects, and training programs.

Of special significance is Acsia’s finishing school program for 50 students from two leading polytechnics in Trivandrum and the Acsia Hackathon that saw over 300+ registrations from 92 premier educational institutions and 24 finalists. The 24-hour national innovation challenge focused on AI-driven problem-solving and next-generation engineering was won by Adhithyan V P of Indian Institute of Technology, Patna.

For more information on Acsia Academy, visit https://www.acsiatech.com/acsia-academy/

For more information on Acsia Campus Connect initiatives, visit https://www.acsiatech.com/campus-connect/

For media enquiries, please contact athul.lal@acsiatech.com

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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.
  • Provide actionable insights that developers can use immediately.

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).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
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.