Welcome to

Innovate | Code | Transform

Acsia Hackathon 2025 is a 24-hour non-stop offline coding marathon hosted at the
Acsia Global Headquarters and R&D Centre, Thiruvananthapuram on 31st October and 1st November.
This is your chance to dive deep into real-world challenges, collaborate with top minds, and build solutions that could transform the future. Participation is absolutely free, and you stand a chance to win cash prizes, internships and pre-placement interviews.

Problem Statements

AH2025/PS01 | AI/ML

Star Team Builder to create optimal project teams, balancing skills, cost, and availability while offering recommendations and alternative scenarios.

AH2025/PS02 | AI/ML

Project Manager for Automotive Software Development that integrates tools, predicts risks, balances workloads, and automates dashboards for proactive management.

AH2025/PS03 | AI/ML

Agentic AI to personalize infotainment, comfort, and navigation in real time using multiple streams of data to enhance safety, inclusivity, and passenger satisfaction.

AH2025/PS04 | AI/ML

Log Analytics Assistant to ingest unstructured logs to detect anomalies, extract root causes, and deliver insights — improving system reliability.

AH2025/PS05 | AI/ML

Learning Demand Forecaster to predict employee skill gaps to deliver personalized learning, and provide insights into workforce readiness and training ROI.

Why Participate?

Solve Real Challenges
Work on problems in generative AI, data science, edge intelligence and software defined vehicles (SDV) using real-world scenarios.

Showcase Your Skills
Compete with top engineering talent and demonstrate your expertise in AI/ML, embedded software, and more.

Career Acceleration
Top performers will be fast-tracked for internship and pre-placement interview (PPI) at Acsia.

Mentorship & Networking
Interact with Acsia’s engineering leaders and domain experts in person.

Hackathon Format

Mode

Offline

Duration

24-hour (non-stop) from 10 am on 31st October to 10 am on 1st November.

Participation Type

Individual or Team (up to 2 members)

Track

AI / ML

Entry

Participation is free

Venue

Acsia Global Headquarters & R&D Centre, 7th Floor, Niagara, Embassy Taurus TechZone, Technopark Phase III, Thiruvananthapuram 695581

Dates to Remember

  • Registrations: 23rd September – 2nd October
  • Email Communication for Idea Submission: 3rd October
  • Online Idea Submissions: 6th – 10th October
  • Evaluation: 13th – 21st October
  • Results: 22nd October (Shortlist of 20)
  • Mentoring: 23rd – 30th October
  • Hackathon: 31st October
  • Winner Announcement Ceremony: 1st November

Prizes & Opportunities

First Prize ₹50,000 +
Internship + PPI

Second Prize
₹25,000 +
Internship + PPI

Third Prize
₹15,000 +
Internship + PPI

All Participants Receive

  • Digital Certificate of Participation
  • LinkedIn Badge
  • Acsia Goodie Bag

Rules & Eligibility

Eligibility:

  • Open to BTech/MTech/MCA/MSc (2026, 2025, 2024) batches of institutions in India.
  • Participants can register individually or as a team (up to 2 members).
  • Participants must have a HackerRank profile.
  • Each team must have at least one member with programming experience.

Rules:

  • All code must be written during the hackathon.
  • Participants should bring their own devices, including laptop.
  • Use of open-source tools/libraries is allowed with proper attribution.
  • Participants must submit a working prototype and a short pitch.
  • Plagiarism or pre-developed solutions will lead to disqualification.
  • Judges’ decision shall be final.

Application Process

  • To register for the Acsia Hackathon 2025, all participants should have a HackerRank community account. Sign up for the HackerRank community account if you haven’t already. If you already have an account, update it and share your profile URL in the form below.
  • Fill the application form completely and Submit your application.
  • You will receive a confirmation email and further intimation about the next steps.
  • Please note: Participating in the Acsia Hackathon 2025 is free and does not involve a fee.

Panel of Distinguished Judges

Dr. Rajasree M S

Dr. Rajasree M S
Chief Executive Officer,
TrEST Research Park

Prof. Asharaf S

Prof. Asharaf S
Dean (Academic) and Professor,
Digital University Kerala

Mr. Vasantharaj G
Vice President Technology and Innovation,
Acsia

Mr. Nibil P M
AVP & Head, Advanced Technology Group,
Acsia

SPOC

Niya Francis
Sr. Executive, Acsia Academy
Mobile: +91 81388 17096
Email: niya.francis@acsiatech.com

Disclaimer

Acsia Hackathon 2025 is a competitive event intended for educational and recruitment purposes. Participation does not guarantee employment or internship offers. All intellectual property developed during the hackathon remains with the participants unless otherwise agreed. By registering, participants agree to abide by the rules and consent to the use of their submissions and images for promotional purposes by Acsia.

Applications with incomplete or inaccurate information will not be eligible for the competition. Kindly refer to the detailed FAQ document and Competition Rules for guidance.

Partners

Sorry. Registrations are Closed.

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.
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