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Jenseits des Lenkrads: Wie Lösungen außerhalb des Fahrzeugs das Fahrerlebnis revolutionieren
Man interacting with a smartphone to control his car remotely, showcasing the capabilities of out-of-car solutions for a connected automotive experience.
Man using a smartphone to manage his car remotely, illustrating innovative out-of-car solutions for enhanced connectivity and convenience.

In Kürze

  • Das moderne Auto ist mehr als nur ein Transportmittel – es ist ein Zentrum der Konnektivität und Intelligenz.
  • Out-of-Car-Lösungen, die auf der Cloud und fortschrittlichen Datenanalysen basieren, bieten unvergleichlichen Komfort, Sicherheit und Effizienz.
  • Acsia leistet Pionierarbeit bei innovativen Funktionen wie der nahtlosen Fernverwaltung von Fahrzeugen, der proaktiven Wartung mit Hilfe von KI und hyperpersonalisierten Empfehlungen, die sich an Ihren Fahrstil anpassen.

Die Automobilwelt befindet sich inmitten eines tiefgreifenden Wandels. Out-of-Car-Lösungen lösen die traditionellen Grenzen des Autobesitzes auf und verweben Ihr Fahrzeug mit Ihrem digitalen Leben. Diese Technologie ist nicht mehr nur auf das Fahren selbst beschränkt, sondern ermöglicht es Ihnen, Ihr Auto von überall aus zu verwalten, zu verstehen und mit ihm zu interagieren.

Die Eckpfeiler der Out-of-Car-Innovation

Mächtige Kräfte kommen zusammen, um diese Revolution möglich zu machen:

  • Cloud Computing: Das Rückgrat von Out-of-Car-Lösungen liegt in skalierbaren Cloud-Plattformen (wie AWS, Azure, GCP). Diese bieten Rechenleistung auf Abruf, enormen Speicherplatz und die Möglichkeit, Ihr Auto nahtlos mit einer Welt digitaler Dienste zu verbinden.
  • Telematik: Kleine Geräte, die in Fahrzeuge eingebaut werden, sammeln eine Fülle von Echtzeitdaten, von der Motorleistung und dem Kraftstoffverbrauch bis hin zum Standort und Fahrverhalten. Diese Daten sind das Lebenselixier der Out-of-Car-Intelligence.
  • Künstliche Intelligenz: Algorithmen des maschinellen Lernens durchforsten riesige Datensätze, erkennen Muster, prognostizieren potenzielle Probleme und passen die Erkenntnisse an Ihre speziellen Bedürfnisse und Vorlieben an.
  • Benutzerzentrierte Anwendungen: Die Vorteile dieser technologischen Revolution werden Ihnen über intuitive Smartphone-Apps und Webportale zur Verfügung gestellt, über die Sie Ihr Auto aus der Ferne verwalten können.

Ermächtigung an Ihren Fingerspitzen

Lassen Sie uns einen Blick auf die verschiedenen Möglichkeiten werfen, wie Out-of-Car-Lösungen das Leben einfacher machen:

  • Mühelose Bequemlichkeit: Sie haben vergessen, Ihr Auto abzuschließen? Sie brauchen eine Abkühlung an einem schwülen Tag? Mit ein paar Fingertipps auf Ihrem Smartphone können Sie die Türen ver- und entriegeln, die Klimaanlage einstellen und sogar die Lichthupe betätigen, damit Sie Ihr Auto auf einem überfüllten Parkplatz wiederfinden.
  • Seelenfrieden: Systeme zur Wiederbeschaffung gestohlener Fahrzeuge lokalisieren den Standort Ihres Fahrzeugs, wenn das Undenkbare passiert. Geofencing warnt Sie, wenn Ihr Fahrzeug ein bestimmtes Gebiet verlässt. Das ist ideal, um jugendliche Fahrer zu überwachen oder ein geliehenes Auto im Auge zu behalten. Die Notruffunktionen können im Falle eines Unfalls automatisch Hilfe herbeirufen.
  • Kosteneffiziente Proaktivität: Die vorausschauende Wartung analysiert die Flut von Telematikdaten, um die frühesten Anzeichen von Verschleiß zu erkennen. Anstatt auf Pannen zu reagieren, können Sie den Service im Voraus planen und so kostspielige Reparaturen und Zeitverluste am Straßenrand vermeiden.
  • Nachhaltiges und effizientes Fahren: KI-gestütztes Feedback zu Ihren Fahrgewohnheiten bietet maßgeschneiderte Vorschläge zur Optimierung des Kraftstoffverbrauchs und zur Verringerung Ihres CO2-Ausstoßes. Bei einigen Systemen wird das Umweltbewusstsein sogar spielerisch gestärkt, indem effizientes Fahren zu einer spannenden Herausforderung wird.
  • Ihr Auto, Ihre Art: Out-of-Car-Lösungen lernen mit der Zeit Ihre Vorlieben. Ihre Sitz- und Spiegeleinstellungen könnten sich automatisch anpassen, Ihre Lieblingsmusik könnte abgespielt werden und Ihr Navigationssystem könnte Ihnen proaktiv den Weg von der Arbeit nach Hause vorschlagen.

Acsia: Die vernetzte Autolandschaft gestalten

Wir bei Acsia wissen, dass wahre Innovation nicht nur in der Technologie, sondern auch in ihrer durchdachten Anwendung liegt. Unser Angebot an Out-of-Car-Lösungen nutzt die neuesten Entwicklungen, um Ihre Beziehung zu Ihrem Fahrzeug neu zu definieren. Wir sind führend in Bereichen wie:

  • Fahrzeug-Fernsteuerung: Stellen Sie sich vor, Sie könnten die Klimaanlage Ihres Fahrzeugs steuern, seinen Status überprüfen und in Notfällen sogar Hilfe anfordern – und das alles, ohne in der Nähe Ihres Fahrzeugs sein zu müssen.
  • Vorausschauende Wartung: Unsere KI-gesteuerten Systeme analysieren große Mengen an Telematikdaten, um frühzeitige Anzeichen von Verschleiß zu erkennen und Ihr Fahrzeug langfristig in optimalem Zustand zu halten.
  • Personalisierte Fahr-Insights: Erhalten Sie maßgeschneiderte Empfehlungen, wie Sie Ihre Fahrweise verbessern, den Kraftstoffverbrauch senken und eine vernetzte Fahrt hinter dem Steuer genießen können.

Der Weg nach vorn

Wir kratzen nur an der Oberfläche dessen, was möglich ist. Stellen Sie sich vor, dass Ihr Auto nahtlos mit der Infrastruktur einer intelligenten Stadt kommuniziert, um den Verkehrsfluss zu optimieren oder Gefahrenwarnungen in Echtzeit weit über die Reichweite Ihrer eigenen Sensoren hinaus zu erhalten. Out-of-Car-Lösungen könnten sogar den Weg für abonnementbasierte Funktionen ebnen, mit denen Sie Ihr Auto auf Wunsch mit zusätzlichen Funktionen ausstatten können. Wir bei Acsia setzen uns für eine Zukunft ein, in der das Autofahren sicherer, angenehmer und stärker in unser vernetztes Leben integriert ist als je zuvor.

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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.