LiLA Agentic AI Platform

Simplify and Witness 60-85% Productivity Gain by Automating Workflows in:
  • Requirements Engineering
  • Standards and Compliance
  • Code Verification and Documentation
  • Defect Management
  • Integration and Test Engineering
  • Document Analytics

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 LiLA Can Help?

Transform document complexity into clarity. Convert multi-lingual engineering specifications into structured, risk-aware requirements. Accelerate delivery, cut interpretation time by 40%, and ensure compliance across global programs.

Simplify compliance by auto-mapping engineering artifacts to MISRA, ASPICE, and ISO 26262 standards. Detect gaps early, reduce audit preparation by 50%, and speed up certification cycles for distributed teams.

Boost verification and documentation with automated generation of safety-critical tests, coverage metrics, and design-linked documentation. Shrink test creation time by 60%, lower compliance overhead, and enhance release confidence for regulated systems.

Accelerate QA cycles through automated CI/CD pipelines, test cases, scripts, and audit-ready reports. Cut execution time by 70%, reduce reporting effort by 50%, and elevate release quality across embedded and enterprise environments.

Resolve defects faster by converting logs into actionable insights. Automate clustering, root cause analysis, and duplicate detection to trim triage effort by 40%, reduce support costs by 25%, and improve product reliability in complex systems.

Turn intricate documents, diagrams, and RFQs into actionable intelligence. Increase proposal validation by 50%, enable global collaboration with multi-lingual support, and shorten onboarding through semantic retrieval and feature modeling.

Empower HR, IT, Procurement, and other enabler functions with intelligent, proactive insights — shifting operations from reactive support to strategic value creation and driving greater organizational agility and efficiency.

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 test case 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?
60-85% productivity gains​
HITL (human in the loop) Agentic AI platform
Full-stack coverage across SDLC
Multi-domain-specific intelligence​
Scalable, customizable and accurate outputs, unlike copilots
Agentic workflows (not just chatbots)​
Secure and offline
60-85% productivity gains​
HITL (human in the loop) Agentic AI platform
Full-stack coverage across SDLC
Multi-domain-specific intelligence
Customizable and accurate outputs, unlike copilots
Agentic workflows (not just chatbots)​
Secure and offline
What’s In It For You

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

LiLA simplifies and saves time on requirements engineering, complying with MISRA C/C++ guidelines, unit testing, and test case generation.

LiLA integrates Human-in-the-loop Agentic workflows for requirements, testing, and defect management to minimize defects and ensure robust quality. It automates compliance checks, delivering first-time accuracy without rejections or rebuilds.

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 requirements engineering, code verification & documentation, integration & testing, defect management, and document analysis, 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, norms and requirements documentation. This eliminates the need for manual searches, significantly reducing turnaround time (TAT) and ensuring accuracy in requirement interpretation.

Apply

LiLA can automate various workflows, including:​ 

  • Requirements management 
  • Standards and Compliance 
  • Code verification 
  • Code documentation 
  • Integration and Testing 
  • Defect management 
  • Document analysis 
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 leading OEMs and Tier-1s. Many POCs are also underway. 

Apply
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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.