LiLA Agentic AI Platform

Significantly enhance software development efficiency and productivity.

Artificial intelligence (AI) and machine learning (ML) are revolutionizing software development, particularly in the evolving automobile industry, where software-defined vehicles (SDVs) rely heavily on code for features and performance. These technologies promise to enhance productivity, streamline workflows, and reduce costs—benefits that are crucial for the cost-sensitive automotive sector.
However, challenges remain, including integration difficulties and concerns regarding privacy, functional safety, and compliance. By leveraging AI and ML, software engineers can automate tasks like code generation and debugging, allowing them to focus on more complex, creative aspects. Success requires expertise in the automotive-specific development environment and ensuring compatibility with existing standards and processes.

How Acsia Can Help?

LiLA Agentic AI Platform forms the bedrock of Acsia’s AI and ML capabilities in automotive SW development.
  • Requirements Engineering*
    • SYS and SWE
  • Standards Compliance
    • MISRA C/C++
    • ASPICE*
    • ISO 26262*
  • Defect Management
    • Log Analyzer
    • Defect Analyser and Duplicate Detection
  • Feature Modelling*
  • RAG (Retrieval Augmented Generation)
  • Diagram interpreted generation using vision models
  • AUTOSAR Chatbot
  • Norms Analyzer
  • Code Development
    • Generate design (SWE.2) from SWE.1 as input​
    • Develop code using prompts generated from SWE.2
    • Create Unit Tests using AI (SWE.4)​
    • Generate code coverage​
  • Code Optimization
    • Code Refactoring
    • Dead Code Elimination
    • Loop Optimization
    • Function Inlining
    • Reduce Code Complexity
*In pipeline

Acsia is committed to working closely with OEMs and their Tier-1 suppliers to fully harness the power of LiLA Agentic AI Platform and enable significant gains in efficiency, productivity, and cost savings across production programs.

Project Highlights

1. Validation of automotive component suppliers is time consuming. Too many suppliers. Complexity of data analysis – AI can streamline and analytics to generate recommendation report based on criteria provided. Relevant to OEM and T1.

2. Log analysis and anomaly detection, duplicate defects detection – both are time consuming. Ten thousands of tickets. Logs will be GB size files. Relevant to OEM and T1.

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If automotive HMI in English has to be translated to another language like Arabic or Mandarin or Spanish: manual translation of thousands of words and strings is time consuming and expensive.

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Hear From Experts
Vasantharaj G Pillai
VP Technology & Innovation
With Acsia since 2014
“AI and ML are revolutionizing automotive software development by streamlining processes, automating tasks like defect management and code generation, and ensuring compliance with industry standards. Acsia’s LiLA Agentic AI Platform accelerates this transformation, enhancing productivity, reducing development time, and ensuring high-quality, compliant software — helping OEMs stay ahead of competition.”
OEM Production Program Experience:
Tier-I Experience:
Why Acsia?
A Decade of Excellence
10 years of experience in automotive software development for the world’s leading automakers.
LiLA Agentic AI Platform
Acsia’s LiLA Agentic AI Platform is exclusively tuned for automotive SW development use cases.
Experience across IVI, e-Mobility, and Telematics
Acsia engineers have deep domain insights gathered from delivering numerous projects in in-vehicle infotainment, e-mobility and telematics to train the AI models.
AUTOSAR, Android Automotive, Automotive Linux, Verification & Validation, HMI capabilities
Acsia engineers have proven expertise in automotive industry-relevant capabilities to train the AI models.
Isolated, Offline/Local and Secure
LiLA is deployed locally and operated offline ensuring information security by preventing the sharing of vital data online.
2-months Consulting
Framework and criteria to jointly identify use cases that can offer the greatest value.
2 Deployment Models
Custom UI (chat mode) and Microsoft Visual Studio Code IDE integration.
10 years of experience in automotive software development for the world’s leading automakers.
Acsia’s AI-powered developer suite LiLA is exclusively tuned for automotive SW development use cases.
Acsia engineers have deep domain insights gathered from delivering numerous projects in in-vehicle infotainment, e-mobility and telematics to train the AI models.
Acsia engineers have proven expertise in automotive industry-relevant capabilities to train the AI models.
LiLA is deployed locally and operated offline ensuring information security by preventing the sharing of vital data online.
Framework and criteria to jointly identify use cases that can offer the greatest value.
Custom UI (chat mode) and Microsoft Visual Studio Code IDE integration.
What’s In It For You

LiLA helps development teams save considerable effort & time spent on the analysis of DLT logs and reporting anomalies, detection of duplicate bug tickets, unit testing, and code optimization.

LiLA simplifies and saves time on requirements engineering, generating software architecture and design, developing code with engineered prompts, complying with MISRA C/C++ guidelines, unit testing, and code refactoring, dead code elimination, and performance optimization.

LiLA simplifies the management of large code volumes in maintenance projects through refactoring and optimization. It minimizes errors, enhancing software reliability and consistency.

LiLA automates compliance checks against standards, ensuring the software meets all requirements without rejections or rebuilds, allowing for first-time accuracy.

Frequently Asked Questions

Acsia’s LiLA Agentic AI Platform is an AI-powered developer suite tailored for the automotive industry. It automates tasks such as defect management, document analysis and code generation, thereby increasing productivity, streamlining workflows, and reducing costs in the automotive software development life cycle (SDLC).

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Retrieval Augmented Generation (RAG) in Acsia’s LiLA Agentic AI Platform streamlines the process of managing automotive requirements by quickly retrieving relevant information from extensive standards and documentation. This eliminates the need for manual searches, significantly reducing turnaround time (TAT) and ensuring accuracy in requirement interpretation.

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LiLA can automate various tasks, including:​

  • Defect management
  • Document analysis
  • Code generation
Apply

The suite is deployed locally and operates entirely offline, ensuring that sensitive automotive data remains secure and never gets shared online. This approach prevents unauthorized access and safeguards vital information from potential breaches.

Additionally, LiLA automates compliance checks against industry standards, ensuring that the software meets all regulatory and safety requirements on the first attempt. This eliminates the need for rework or rejections, enhancing both security and development efficiency.

Apply

Yes, LiLA has been successfully deployed by a leading German OEM. Many POCs are underway.

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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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