Success Story

Breaking New Ground in High-End Digital Instrument Cluster Market

Here is how Acsia entered the high-end digital instrument cluster market, leveraging advanced boot time optimization and human-machine interface development to significantly enhance functionality and user experience for a global automotive supplier.

Business & Technology Landscape

The quest for offering a top-notch driving experience to customers has always been the driving force behind continuous innovations in the automotive industry. A critical question posed was: “Is it possible to develop a Linux-based IC/IVI that boots up in just one second?” This challenge was paramount as OEMs sought solutions to significantly enhance digital instrument clusters, offering improved functionality and interactive experiences that boost driving safety and comfort.

Key Trends:

Boot Time Optimization: Automotive manufacturers are striving to achieve faster boot times for IC/IVI systems, aiming to reduce industry norms from 10-30 seconds to less than 5 seconds.

Advanced Human-Machine Interfaces (HMI): The adoption of sophisticated HMI technologies, such as Rightware Kanzi, is crucial for creating seamless and intuitive user interfaces in digital clusters, infotainment systems, and heads-up displays (HUDs).

Real-Time Graphics and Performance: There is a growing need for real-time graphics rendering on constrained hardware platforms, driven by the desire to deliver high-quality animations and visual effects without compromising performance.

Customer Problem Statement

The leading Tier-I automotive supplier faced multiple challenges:

FastBoot Development: Required a Linux-based IC/IVI system with a boot time of just 1 second, a significant reduction from industry norms.

Kanzi Expertise: Needed deep expertise in Kanzi for developing advanced digital instrument clusters.

Animation and Boot Time Issues: Struggled with animation glitches and poor boot-up performance in a Kanzi-based instrument cluster for a sports car.

Acsia Solution

Acsia was selected due to their proven expertise in Boot Time Optimization and HMI development using Rightware Kanzi.

FastBoot Solution:

  • Reduced boot time from 20 seconds to 6 seconds by optimizing Bootloader and Kernel configurations.
  • Further reduced to 4.6 seconds by customizing the boot loader and optimizing the Kernel.
  • Achieved a 1.2-second boot time using innovative, proprietary techniques.

Kanzi-Based HMI Development:

  • Acsia’s team underwent advanced training in Kanzi at Rightware, Finland, earning the Rightware Partner title.
  • Built a strong partnership with Rightware, enhancing their expertise in Kanzi for IC development.

Sports Car Project:

  • Resolved a long-standing animation glitch within a week.
  • Reduced boot-up time from 7.5 seconds to 4.5 seconds in three months.

Cluster Program:

  • Developed the full instrument cluster software stack and HMI layer in Kanzi for five variants.
  • Implemented a “Problem-based Training” methodology for efficient team ramp-up.
  • Took on complete development responsibility, including sprint planning, handling change requests, and bug fixing.

Business Outcome & Impact

    • Achieved Faster Boot Times: Delivered a boot time of 1.2 seconds, setting a new industry standard.

 

  • Enhanced Customer Experience: Solved critical animation and boot performance issues, meeting OEM targets and improving user satisfaction.
  • Successful Project Completion: Helped the Tier-I supplier meet critical production milestones, ensuring timely vehicle rollouts.
  • Strengthened Partnerships: Built a strong relationship with Rightware and established Acsia as a trusted problem solver in the automotive software domain.

 

Key Learning

  • Deep Expertise in Boot Time Optimization: Demonstrated ability to significantly reduce boot times through innovative techniques.
  • Mastery of Kanzi for HMI Development: Advanced training and partnership with Rightware enhanced Acsia’s capabilities in developing sophisticated digital clusters.
  • Effective Problem-Solving: Successfully resolved complex technical issues quickly, establishing a reputation for reliability and expertise.
  • Efficient Team Ramp-Up: Implemented effective training methodologies to quickly scale up and meet project demands.
  • Complete Development Responsibility: Proven capability to handle comprehensive development tasks, from planning to execution, ensuring project success.

Expert Speak

Vasatharaj G
VP Technology & Innovation
Our venture into Kanzi-based HMI development was driven by a clear vision to excel in the global automotive market. The advanced training our team underwent in Finland and the strong partnership we built with Rightware enabled us to master this cutting-edge technology. Our ability to swiftly resolve critical issues and enhance the performance of digital instrument clusters demonstrates our technological prowess and dedication to delivering superior solutions.
Anil S
VP, Delivery
Our success in resolving complex challenges for the Tier-I supplier, from fixing animation glitches to optimizing boot-up performance, is a testament to Acsia's capability in managing high-stakes projects. By efficiently ramping up our team and maintaining seamless project execution, we not only met but exceeded our client's expectations. This reinforces our position as a reliable and innovative partner in the automotive industry.
Nibil P M
AVP Advanced Technology Group
Achieving a one-second boot time for a Linux-based instrument cluster was an ambitious challenge. Through rigorous optimization of bootloader and kernel configurations and innovative techniques, we surpassed industry standards and set a new benchmark. This accomplishment underscores our deep expertise in boot time optimization and our commitment to pioneering advancements in automotive software development.
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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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