Success Story

Takeover and Enhancement of Next-Gen Digital Cockpit for a Global OEM

In 2021, Acsia successfully took over the development of a next-generation digital cockpit for a leading luxury OEM, overcoming significant challenges in integration and continuity to ensure a sophisticated, connected driving experience.

Business & Technology Landscape

During the year 2021, the automotive software domain saw significant advancements, particularly in Software-Defined Vehicles (SDVs) and Digital Cockpits, driven by the demand for sophisticated, connected, and intelligent vehicles.

Key Trends in SDV

Over-the-Air (OTA) Updates: Prevalence of OTA updates for deploying new features, security patches, and performance improvements remotely.

Enhanced Vehicle Connectivity: Reliance on robust communication networks like 5G for real-time data exchange, telematics, and V2X communication, enabling advanced driver assistance systems (ADAS) and autonomous driving capabilities.

Centralized Computing Architectures: Transition from multiple electronic control units (ECUs) to centralized computing platforms, offering greater processing power and improved system integration.

Digital Cockpit Trends

Unified User Interfaces: Integration of instrument clusters, infotainment systems, and head-up displays into a single, unified user interface for seamless interaction.

Advanced Infotainment Systems: Featuring voice recognition, gesture control, and personalized content powered by advanced processors and GPUs.

High-Resolution Displays and Graphics: Adoption of high-resolution displays, OLED panels, and curved screens for clearer and more vibrant visuals.

Connectivity and Smart Services: Support for connected services and third-party applications, enabling functionalities like real-time navigation and streaming media.

Personalization and User Profiles: Extensive personalization options for users to customize their driving experience.

Customer Problem Statement

The client, a Tier-I supplier, for a leading luxury OEM, was in the lookout for a new software partner to support the development of next-gen cockpit module.

Maintenance Takeover: This was a maintenance takeover project with Acsia taking over the entire Start of Production (SOP) in March 2023.

Initial Scope: Identifying defects/bugs reported during field trials by the OEM or other vendors and providing fixes.

Acsia Solution

The client decided to work with Acsia due to their strong track record in delivering successful projects in the instrument cluster and infotainment domains. Acsia’s demos and facility audits showcased their proven expertise, making them the ideal partner for this project.

Project Responsibilities

Pre-SOP Support and Takeover: Acsia took over the project before the Start of Production (SOP) was established, handling the entire software responsibility, including bug fixes and change requests.

Post-SOP Software Maintenance: After SOP, Acsia continued to provide software maintenance, ensuring the system remained stable, functional and all performance parameters are met.

Software Integration: Acsia was responsible for integrating software across three product lines.

Media Gateway Unit (MGU): Managed the head unit without instrument cluster functionality.

Integrated Digital Cockpit (IDC): Included all MGU features plus additional driver information.

Rear Seat Entertainment (RSE): Provided entertainment options for rear seat passengers.

Challenges Overcome

Reverse Engineering: Acsia had to reverse engineer the system due to the lack of documentation from the previous vendor, which they successfully accomplished.

High First-Time Fix Rate: Achieved a 96% first-time fix rate during the maintenance phase, minimizing the frequency of recurring issues.

Business Outcome & Impact

  • Production Timelines: The OEM successfully met their production timelines, ensuring vehicle rollout as per the fixed date in 2023.
  • Improved Customer Experience: Enhanced overall architecture led to a better user experience.
  • System Health Improvement: Performance improvements and system health measures optimized the system as per OEM expectations.

Key Learning

  • Root Cause Analysis (RCA): Improved RCA processes resulted in higher process efficiency and a higher first-time fix rate.
  • APSICE V Model: Acsia demonstrated the capability to handle the entire APSICE V model (both system and software) except for SYS 1 (elicitation stage) for instrument cluster projects for any OEM/Tier-I.
  • AUTOSAR Configuration: Optimization of system inputs and outputs.
  • Infotainment Lifecycle: Knowledge of the vehicle and infotainment lifecycle on IDC.
  • Wake-Up Reset and STR: Understanding of wake-up reset and Suspend to RAM (STR) in vehicle systems.
  • Display Issues: Addressing screen blackout, flickering, and link lost issues.
  • Diagnostic Commands: Improvement in diagnostic commands and responses in actual vehicles and test properties.
  • Software Flashing: Enhancements in software flashing time.
  • Secure Communication: Improvement of secure communication channels between master-slave connections in infotainment systems.

Expert Speak

Hitha S P
Delivery Head
Our commitment to excellence and proven track record were key factors in being chosen for this project. By owning the software integration and maintenance across the three product lines, we ensured seamless continuity and stability. The successful rollout of vehicles in 2023, meeting all production timelines, is a testament to our team's dedication and capability in managing complex automotive software projects.
Ajeesh Sahadevan
SME
Taking over the project from where the previous vendor left off was a challenging task, especially with minimal documentation. However, our team's expertise in the digital cockpit and infotainment domain allowed us to reverse engineer the system effectively. We not only met but exceeded expectations, achieving a 96% first-time fix rate, which significantly reduced recurring issues and enhanced the overall user experience.
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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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