About Us

Driving the ​automotive future through excellence in ​technology and ​innovation.​
In the Greek language, Acsia means “value”
Acsia Technologies is a global leader in automotive software powering Digital Cockpit & Display, e-Mobility, and Telematics. We use our deep expertise to accelerate the development of tools and platforms 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 OEMs, and Tier-I suppliers to transform tomorrow’s mobility with high-quality and cost-effective software solutions.

The Journey So Far

2013
Acsia Technologies incorporated as a Private Limited Company.
2014
Commences business operations at Technopark Thiruvananthapuram with 3 engineers.
2016
Establishes office in Hyogo, Japan.
Wins the first cluster project from Japan.
2017
Wins Android-based Rear-Seat Entertainment (RSE) program of European auto major by partnering with German Tier-I.
2018
Secures ISO9001:2015 and ISO27001:2013 certifications.
Delivers projects assessed at ASPICE CL1.
Enters ADAS (Advanced Driver Assistance System) domain.
Headcount crosses the 100 mark.
2019
Enters the e-mobility space.
Headcount breaches the 200 mark.
Delivers projects assessed at ASPICE CL2.
2020
Commences full cluster development support for all models of North American OEM through Tier-I.

Deploys ISO 26262 and ASPICE CL3 in QMS.
2021
Wins ASPICE compliance project for IVI system.
Wins in-vehicle infotainment project.
2022
Secures TISAX AL3 certification.
Deploys ISO 21434 in QMS.
Establishes office in Munich, Germany.
Opens sales office in Bengaluru, India.
2023
Establishes office in Detroit, USA.
Acsia acquires German automotive software start-up Arctictern.
Secures Great Place To Work (GPTW) certification.
2024
Inaugurates Global HQ and R&D Centre in Thiruvananthapuram.
Secures GPTW re-certification.
Becomes ‘Long-Term Partner’ for a leading global luxury OEM.
Launches LiLA, an Agentic AI Platform.

Meet The Leadership Team

Jijimon Chandran
Founder & CEO
Rejeesh R Pillai
VP New Initiatives
Anil Shahul Hameed
VP Engineering
Jayachandran R
VP People & Values
Vasanthraj G Pillai
VP Technology & Innovation
Datta Hegde
MD, Germany
Atsushi Masuyama
Head of Sales & Marketing, Japan
Nibil P M
AVP & Head, Advanced Technology Group
Sojan James
Principal Architect
Diljith Muthuvana
Head of Telematics & Connectivity Solutions
Gloria Joseph
AVP & Head, Resource Management Group
Rajan Thomas
Global Delivery Leader
Valliaappan Chidambaram
Head of Verification & Validation
Jose Kunnappally
Head of Marketing & Communications
Ratish Bhat
Director Sales – India
Strategic Advisors
Stefan Juraschek
  • Former VP R&D, Electric & Electronics Department of BMW Group.
  • 36+ years with BMW Group in various roles.
Dr. Arun Surendran
  • Strategic Director and Principal, Trinity College of Engineering, Thiruvananthapuram.
  • 20+ years in strategy, engineering education, cybersecurity, and policy development.

Purpose

To drive the automotive future through excellence in technology and innovation.
Mission
We develop technologies that simplify complex challenges and expand what’s possible inside a vehicle.
Vision
To transform mobility with big ideas in automotive technology.
Acsia Values
Integrity
Walk the talk, even if no one is watching.
Passion
Keep the fire in the belly burning bright.
Unity
Together, we are always smarter and stronger.
Respect
Show regard for each other and our work.
Excellence
Have an eye for detail in everything we do.
Commitment to the Society

Acsia Foundation Trust spearheads the corporate social responsibilities (CSR) of the organization. For over a decade since our inception, Acsia has engaged with leading NGOs (non-governmental organizations) to support various initiatives in the fields of primary education and sustainability.

In 2023, Acsia Foundation Trust was incorporated to give a formal structure to our interventions, ensure greater focus and accountability for outcomes. Acsia Technologies has undertaken four CSR projects during FY 2023-24.

Solarization of CIMR (Central Institute on Mental Retardation), Thiruvananthapuram dedicated to providing education, training, development, and rehabilitation of the mentally challenged. The project will reduce operational expenses as well as carbon footprint.

Two projects in association with Kanal Innovations Charitable Trust working towards the empowerment of children.

  • A Finishing School project at Government Women’s Polytechnic College, Kaimanam and Central Polytechnic College, Nettayam imparting essential skills such as communication, leadership, cultural orientation, resume building, and basic business etiquette such as email drafting, to equip students with the necessary tools and competencies to excel in today’s competitive job market.
  • Infrastructure upgrade for the Government Upper Primary School in Valiyathura that was damaged when used as a relief camp during Cyclone Ockhi.

Beach Wall Art project at Shanghumukhom in association with the Sustera Foundation working towards building climate-responsible communities. The project aims to raise awareness about ocean litter and plastic pollution and create a sustainable environment.

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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 

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/PS04 | 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/PS03 | AI/ML

Context

In a highly competitive automotive market, consumer purchase decisions are influenced by a mix of vehicle features, price, and brand perception. Automakers invest heavily in design and innovation, but it is often unclear which specific features (e.g., mileage, horsepower, safety, infotainment, connectivity) actually drive sales in different regions and demographics.

 

Pain Point

  • Automakers often rely on intuition, surveys, or fragmented market studies, which may not reflect actual consumer behaviour.
  • Without clear insights, companies risk overinvesting in features that don’t influence buying decisions while underestimating the importance of others.
  • This leads to misaligned product strategies, higher costs, and lost opportunities in competitive segments.

 

Challenge

Develop a data-driven AI solution to quantify the importance of car features in consumer purchasing decisions. The system should analyze:

  • Sales data (model, features, trim levels, price).
  • Customer demographics (age, income, region).
  • Market variations (urban vs rural, luxury vs budget segments).

 

Goal

Identify and rank which features most strongly influence purchasing decisions, enabling automakers to:

  • Focus R&D investments on features consumers truly value.
  • Tailor marketing strategies to highlight high-impact features.
  • Customize offerings by region, demographic, or price segment.

 

Outputs

  • Ranked feature importance list (e.g., mileage, price, infotainment, safety).
  • Feature impact segmentation (importance by region, age group, or price tier).
  • Visualization of trade-offs (e.g., mileage vs horsepower vs price sensitivity).

 

Impact

  • Better product design decisions → aligning cars with what customers actually want.
  • Efficient R&D and marketing spend → reduced waste, higher ROI.
  • Stronger competitive positioning → faster response to shifting consumer trends.
  • Scalable model → applicable across new launches, regions, and evolving customer preferences.
AH2025/PS02 | AI/ML

Context

Electric Vehicle (EV) adoption is accelerating globally, driven by sustainability goals and government incentives. However, charging infrastructure development lags behind, and demand at charging stations is often highly variable, influenced by factors such as time of day, location, and weather. This creates challenges for both EV users (availability, waiting times) and city planners (under/over-utilization of infrastructure).

 

Pain Point

  • Charging stations experience unpredictable surges or idle periods, leading to long wait times or wasted infrastructure.
  • City planners and operators struggle to decide how many charging points to allocate at different locations.
  • Poor demand forecasting results in inefficient investment and reduced adoption of EVs due to unreliable charging availability.

 

Challenge

Develop an AI solution that forecasts charging demand at individual stations. The system should take into account:

  • Historical station usage (transactions per hour/day).
  • Temporal patterns (time of day, weekdays vs weekends, seasonality).
  • Geographic location (urban, suburban, highway).
  • External factors such as weather conditions, holidays, or special events.

 

Goal

Provide accurate time-series demand forecasts (hourly/daily) per charging station, enabling operators and planners to:

  • Allocate charging points efficiently.
  • Reduce wait times for EV users.
  • Optimize investment in EV infrastructure.

 

Outputs

  • Predicted demand curves (number of EVs per time unit, per station).
  • Station-level insights (peak usage windows, underutilized stations).
  • Scenario forecasts (e.g., rainy day vs sunny day, weekday vs weekend).

 

Impact

  • Smarter infrastructure planning → efficient use of budget and resources.
  • Improved EV user experience → reduced charging wait times.
  • Accelerated EV adoption → supporting sustainability and emissions reduction.
  • Scalable solution that can be adapted by municipalities, private charging operators, or energy utilities.
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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