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
Lutz Nettig
Head of Sales and Business Development, Europe
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.
Latha Chembrakalam
  • Former VP and Head of Technical Centre India, Continental AG.
  • 17+ years with Bosch Engineering (VP of Powertrain and Electrification BU).
  • Has worked with global OEMs across Europe, Americas, China and Japan.

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.

Request a Meeting
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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