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Success Story

Wiederbelebung des Android Embedded Device Projekts für einen führenden europäischen OEM

Inmitten der transformativen Trends der Elektrifizierung und Konnektivität in der Automobilindustrie übernahm Acsia im Jahr 2021 erfolgreich das Projekt der eingebetteten Android-Geräte für den globalen OEM und stellte damit sein technisches Können und sein Engagement für hervorragende Leistungen unter Beweis.

Business & Technology Landscape

Im Jahr 2021 hat sich die Automobilindustrie durch zwei große Trends stark verändert: Elektrifizierung und Konnektivität. Die Elektrifizierung gewann aufgrund strengerer Emissionsvorschriften, niedrigerer Batteriekosten und einer erweiterten Ladeinfrastruktur an Dynamik, wodurch Elektrofahrzeuge (EVs) attraktiver und praktischer wurden. Gleichzeitig hat die Integration von Android Automotive OS die Konnektivität von Fahrzeugen revolutioniert, indem es die Benutzererfahrung verbessert und eine nahtlose Integration mit anderen intelligenten Geräten ermöglicht hat. Diese Trends brachten die Fahrzeuge in Einklang mit den Erwartungen der modernen Verbraucher und machten das Autofahren vernetzter und angenehmer.

Customer Problem Statement

Ein Tier-I-Zulieferer, der an einem Android-Embedded-Device-Projekt für einen führenden OEM mitarbeitete, stand vor komplexen Herausforderungen. Das Projekt erforderte eine zusätzliche Konzentration auf die OEM-Konformität (ASPICE L2).

Acsia Solution

Als Antwort auf diese Herausforderungen definierte Acsia die bestehende Architektur in ein strukturierteres Format um und dokumentierte sie gemäß den ASPICE-Standards (SYS 2, SYS 3, SWE 1 und SWE 2). Acsia leistete auch wichtige Unterstützung für interne und externe Teams, indem es Fragen zur bestehenden Architektur beantwortete und eingehende Schwachstellenanalysen durchführte, um potenzielle Cybersicherheitsprobleme zu identifizieren.

Aufgrund der gezeigten Leistung und Expertise wurde Acsia mit der Arbeit an der zweiten Version des eingebetteten Android-Geräts beauftragt. Dies beinhaltete die Verwaltung der Hardware- und Softwareanforderungen von den frühen Phasen der Clusterentwicklung (SYS 2 bis SYS 5 und SWE 1 bis SWE 6). Darüber hinaus spielte das Acsia-Team aus architektonischer Sicht eine Schlüsselrolle bei der Entwicklung eines umfassenden Cybersicherheitskonzepts zur Verbesserung der Sicherheit des Geräts. Das Team spielte auch eine entscheidende Rolle bei der Angebotserstellung, der Anforderungsbewertung, den Designdiskussionen, den Testaktivitäten und den Validierungsprozessen und sorgte so für die Anpassung an die Bedürfnisse des Kunden und die Einhaltung der ASPICE-Prozesse.

Business Outcome & Impact

Die Beteiligung von Acsia stellte sicher, dass der OEM den Zeitplan für die Produktionseinführung einhalten konnte. Durch die Implementierung von ASPICE- und Cybersicherheitsstandards wurden die Prozesseffizienz und die Geräteleistung erheblich verbessert. Die Projektergebnisse zeigten den Wert strukturierter Prozesse und der Einhaltung von Industriestandards, was zu einer erfolgreichen und pünktlichen Produkteinführung führte.

Key Learning

Das Projekt stattete das Team mit den notwendigen Fähigkeiten aus, um die Cybersecurity-Anforderungen von Multi-Vendor-Projekten zu bewältigen und das gesamte APSICE-Modell mit Ausnahme von SYS 1 zu verwalten. Diese Fähigkeit stellt sicher, dass Acsia Projekte im Zusammenhang mit eingebetteten Android-Geräten, die sowohl Hardware- als auch Software-Anforderungen beinhalten, effektiv bearbeiten kann.

Expert Speak

Ambika Thiruvappallil Karunakaran
Ambika Thiruvappallil Karunakaran
KMU
Vector
Dank unseres innovativen Ansatzes und unserer umfassenden Erfahrung im Bereich der Automobiltechnologie konnten wir eine schwierige Situation in eine Erfolgsgeschichte verwandeln. Durch die Neudefinition der Architektur und die Implementierung strenger Standards lieferten wir eine robuste, sichere und hochfunktionale Lösung, die den Anforderungen des Kunden entsprach und seine Erwartungen übertraf.
Gloria Joseph
Gloria Joseph
Leiter der Auslieferung
Vector
Dank unseres Engagements für eine exzellente Lieferung und die Pflege starker Kundenbeziehungen konnten wir bedeutende Projektherausforderungen meistern. Durch unsere Zusammenarbeit und unser Engagement für die Einhaltung von Industriestandards konnten wir eine pünktliche Lieferung und überragende Leistung sicherstellen und unseren Ruf als zuverlässiger Partner in der Automobilindustrie festigen.
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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.