Acsia Technologies Inaugurates Global Headquarters and Research & Development Centre and Launches a Generative AI-based Developer Assistant for Automotive Software
Sep 14, 2024

Thiruvananthapuram, August 22nd, 2024: – Acsia Technologies, a global leader in automotive software specializing in Digital Cockpits & Displays, e-Mobility, and Telematics, marked a significant milestone with the inauguration of its new Global Headquarters and Research & Development Centre at the Embassy Taurus TechZone in Technopark Phase III, Thiruvananthapuram. The inauguration was graced by P. Rajeeve, Minister for Industries, Law, and Coir, Government of Kerala.

“Over the past decade, Kerala has seen significant growth across various sectors. Minister P. Rajeeve stated that the government is now focused on making Thiruvananthapuram a global automotive technology hub. He noted that Kerala has achieved the required level of digital literacy and emphasized the prominent role of Acsia Technologies in this development. The Minister also assured that young people will receive the technical training needed to fully leverage these opportunities. He added that the global automotive conclave, scheduled for November in Thiruvananthapuram, will be a key milestone in advancing these efforts.

The Presidential Address was delivered by Kadakampally Surendran, Hon’ble MLA and Former Minister.

During the event, Minister P. Rajeeve also launched Acsia Copilot, named ‘Lila,’ a generative AI-based developer assistant for automotive software. By generating code for a speedometer, Minister Rajeeve officiated the launch of this revolutionary tool, which is set to transform the software development process in the automotive industry.

Lila is an innovative solution that automates routine coding tasks, enabling developers to focus on more complex and creative aspects of software development. The process begins with developers inputting a prompt, which is then expanded using a Large Language Model (LLM) to generate accurate and efficient code snippets. These snippets are displayed in the Copilot interface, where developers can review, refine, and submit the code for compilation, followed by unit testing to ensure it meets the required quality standards.

Acsia is committed to the ethical implementation of AI and Machine Learning (ML), ensuring that these technologies enhance productivity, efficiency, security, and cost savings across the development cycle. The integration of AI also ensures consistent quality in work products, reducing the likelihood of errors and enhancing the reliability of the software.

“The world’s leading automakers trust Acsia to keep their production programs on track. From the very beginning, our mission has been to simplify the complexities of automotive technology by creating software solutions for digital cockpits & displays, e-mobility, and telematics that set new standards in quality and security, ultimately transforming driver and passenger experiences,” said Jijimon Chandran, Founder and CEO of Acsia. ”

Thiruvananthapuram is on the threshold of becoming a global hub for automotive technology, and at Acsia, we are proud to lead this movement. Collaborating with the government and partners like CII Kerala, we are determined to position our capital city as a world leader in automotive innovation. Establishing a global automotive technology hub here is crucial for providing high-quality jobs and driving economic growth across the State.” He added.

The newly inaugurated 1000-seater facility represents a significant milestone for Acsia Technologies, a company that has grown exponentially over the past decade. The facility not only underscores Acsia’s commitment to innovation and excellence but also provides ample room for further growth as the automotive technology industry continues to evolve. With the rise of e-mobility, software-defined vehicles, and connected cars, Acsia is well-positioned to partner with original equipment manufacturers (OEMs) to develop solutions that enrich, engage, and secure driver and passenger experiences.

“The synergy between skilled human resources and industrial expertise positions Kerala, particularly Thiruvananthapuram, as a significant player in the global automotive sector. It is a remarkable achievement that Acsia has made considerable progress in just ten years, said APM Mohammed Hanish IAS, Principal Secretary, Dept. of Industries, Government of Kerala.

“Kerala has a highly skilled and well- trained workforce. This expertise equips Kerala to leverage advancements in automotive technology,” said Stefan Juraschek, Former VP R&D, BMW Group, and Strategic Advisor, Acsia.

“Kerala has great potential, and the journey has been fulfilling. We look forward to the future”, said Christina Hein and German Ferreira from BMW Group congratulating the team on the new inauguration.

The inauguration ceremony was attended by a host of distinguished guests, including APM Mohammed Hanish IAS, Principal Secretary, Dept. of Industries, Government of Kerala, Stefan Juraschek, Former VP R&D, BMW Group, and Strategic Advisor, Acsia, Christina Hein & German Ferreira from BMW Group, Col. Sanjeev Nair, CEO, Technopark, Anoop P Ambika, CEO, Kerala Startup Mission, and Ajay Prasad, MD & CEO India, Taurus Investment Holdings.

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.
  • 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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