Success Story

Achieving ASPICE Level 2 Compliance for Infotainment Systems for a Global OEM

In a high-stakes environment where compliance with international standards is crucial, Acsia successfully led a project to help a leading Tier-I supplier meet ASPICE Level 2 compliance. This initiative was critical for launching an advanced infotainment system slated for inclusion in future models of the leading luxury OEM.

Business & Technology Landscape

In 2022, the European automotive industry witnessed significant technological advancements, particularly in the realm of automotive software development. As vehicles became increasingly software-driven, the importance of adhering to rigorous standards such as Automotive SPICE (ASPICE) and cybersecurity measures like UN R155 became paramount. These standards are critical for ensuring the safety, reliability, and security of automotive systems, which are increasingly interconnected and exposed to cyber threats.

ASPICE Compliance

Automotive Software Process Improvement and Capability Determination (ASPICE) is a framework used widely by automotive manufacturers and suppliers to assess and enhance their software development processes. ASPICE Level 2 compliance indicates a mature process management capability that systematically manages, controls, and monitors software development. This level of compliance is often required by European OEMs and is crucial for suppliers aiming to demonstrate their capability in delivering high-quality software consistently.

Cybersecurity Regulations

The integration of UN R155, a regulation developed by the United Nations Economic Commission for Europe (UNECE), into automotive standards, highlights the growing focus on cybersecurity. This regulation mandates that all vehicles be equipped with a comprehensive cybersecurity management system to protect against, monitor, and respond to cyber-attacks effectively. Compliance with UN R155 is not just about enhancing security but also about building consumer trust in new automotive technologies.

Infotainment Systems

The infotainment systems market has seen substantial growth, driven by consumer demand for vehicles equipped with advanced connectivity, multimedia capabilities, and intuitive user interfaces. Major automotive players like Continental, Bosch, Volkswagen, Daimler, and BMW have been at the forefront of integrating cutting-edge infotainment technologies. These systems are central to the digital cockpit of modern vehicles, combining entertainment, navigation, and connectivity functions with vehicle diagnostics and user settings into a single system interface.

Market Dynamics

The push towards more connected and autonomous vehicles has led to an increased emphasis on software capabilities within the automotive sector. This shift necessitates a deeper integration of software development standards and cybersecurity measures as fundamental components of the vehicle design and manufacturing process. Suppliers and OEMs must continuously evolve their processes to meet these stringent standards, which are becoming as critical as the mechanical components of a vehicle.

Customer Problem Statement

To address the stringent requirements of ASPICE 3.1 Level 2 and prepare for imminent OEM audits, the customer needed to ensure their infotainment systems met the necessary standards, particularly with the production rollout deadline approaching. The challenge was further intensified by the need to comply with UN R155 Cybersecurity and Cybersecurity Management System regulations. Recognizing the complexity and urgency of these requirements, the customer sought expert assistance to ensure timely and successful compliance.

Acsia Solution

Acsia deployed a robust approach involving 100+ engineers who were ramped up in just seven months. The team tackled over 100+ features across more than 10 domains and addressed over 50+ change requests.

Acsia’s intervention included:

  • Detailed process mapping and gap analysis.
  • Integration of best practices in compliance and documentation.
  • Strategic planning to align with ASPICE Level 2 processes while ensuring the system met ISO 26262 ASIL-D standards for functional safety.

Business Outcome & Impact

  • Compliance Achievement: Successfully enabled the Tier-I supplier to achieve ASPICE Level 2 compliance.
  • Audit Success: Facilitated a successful audit process, clearing the way for meeting the OEM’s production rollout deadline.
  • Safety and Security Enhancement: Ensured the infotainment system met the highest standards of functional safety and security, crucial for the OEM’s competitive edge in the market.

Key Learning

Acsia’s experience highlights the importance of agility and expertise in navigating complex compliance landscapes. The project underscored the need for:

  • Effective team scalability within short timelines.
  • Strong technical leadership to manage extensive feature integration and regulatory compliance.

Expert Speak

Gloria Joseph
Delivery Leader
Meeting the ASPICE Level 2 compliance within the set timeline was a formidable challenge, but our team's dedication and adaptability were key to our success. The coordination of 105 skilled engineers and the integration of over 70 new features while managing 80 change requests demonstrated our capability to handle complex projects under pressure. This achievement not only reflects our technical proficiency but also our commitment to delivering excellence in every aspect of our work.
Vishnudas C. H.
SME
ASPICE Level 2 is not just a benchmark; it's a comprehensive standard that ensures rigorous quality and process control in software development. Achieving 100% compliance was crucial for our client's strategic objectives and required a deep dive into every layer of the development process. Our success in this project underscores our expertise in automotive software engineering and sets a new standard for future projects. It’s a testament to our team’s ability to translate complex standards into effective, practical solutions that enhance product quality and safety.
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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 

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/PS04 | 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/PS03 | AI/ML

Context

In a highly competitive automotive market, consumer purchase decisions are influenced by a mix of vehicle features, price, and brand perception. Automakers invest heavily in design and innovation, but it is often unclear which specific features (e.g., mileage, horsepower, safety, infotainment, connectivity) actually drive sales in different regions and demographics.

 

Pain Point

  • Automakers often rely on intuition, surveys, or fragmented market studies, which may not reflect actual consumer behaviour.
  • Without clear insights, companies risk overinvesting in features that don’t influence buying decisions while underestimating the importance of others.
  • This leads to misaligned product strategies, higher costs, and lost opportunities in competitive segments.

 

Challenge

Develop a data-driven AI solution to quantify the importance of car features in consumer purchasing decisions. The system should analyze:

  • Sales data (model, features, trim levels, price).
  • Customer demographics (age, income, region).
  • Market variations (urban vs rural, luxury vs budget segments).

 

Goal

Identify and rank which features most strongly influence purchasing decisions, enabling automakers to:

  • Focus R&D investments on features consumers truly value.
  • Tailor marketing strategies to highlight high-impact features.
  • Customize offerings by region, demographic, or price segment.

 

Outputs

  • Ranked feature importance list (e.g., mileage, price, infotainment, safety).
  • Feature impact segmentation (importance by region, age group, or price tier).
  • Visualization of trade-offs (e.g., mileage vs horsepower vs price sensitivity).

 

Impact

  • Better product design decisions → aligning cars with what customers actually want.
  • Efficient R&D and marketing spend → reduced waste, higher ROI.
  • Stronger competitive positioning → faster response to shifting consumer trends.
  • Scalable model → applicable across new launches, regions, and evolving customer preferences.
AH2025/PS02 | AI/ML

Context

Electric Vehicle (EV) adoption is accelerating globally, driven by sustainability goals and government incentives. However, charging infrastructure development lags behind, and demand at charging stations is often highly variable, influenced by factors such as time of day, location, and weather. This creates challenges for both EV users (availability, waiting times) and city planners (under/over-utilization of infrastructure).

 

Pain Point

  • Charging stations experience unpredictable surges or idle periods, leading to long wait times or wasted infrastructure.
  • City planners and operators struggle to decide how many charging points to allocate at different locations.
  • Poor demand forecasting results in inefficient investment and reduced adoption of EVs due to unreliable charging availability.

 

Challenge

Develop an AI solution that forecasts charging demand at individual stations. The system should take into account:

  • Historical station usage (transactions per hour/day).
  • Temporal patterns (time of day, weekdays vs weekends, seasonality).
  • Geographic location (urban, suburban, highway).
  • External factors such as weather conditions, holidays, or special events.

 

Goal

Provide accurate time-series demand forecasts (hourly/daily) per charging station, enabling operators and planners to:

  • Allocate charging points efficiently.
  • Reduce wait times for EV users.
  • Optimize investment in EV infrastructure.

 

Outputs

  • Predicted demand curves (number of EVs per time unit, per station).
  • Station-level insights (peak usage windows, underutilized stations).
  • Scenario forecasts (e.g., rainy day vs sunny day, weekday vs weekend).

 

Impact

  • Smarter infrastructure planning → efficient use of budget and resources.
  • Improved EV user experience → reduced charging wait times.
  • Accelerated EV adoption → supporting sustainability and emissions reduction.
  • Scalable solution that can be adapted by municipalities, private charging operators, or energy utilities.
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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