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Über uns

Die Zukunft des Automobils durch Spitzenleistungen in Technologie und Innovation vorantreiben.
In der griechischen Sprache bedeutet Acsia " Wert ".
Acsia Technologies ist ein weltweit führender Anbieter von Automobilsoftware für Digital Cockpit & Display, e-Mobility und Telematik. Wir nutzen unser umfassendes Fachwissen, um die Entwicklung von Tools und Plattformen zu beschleunigen, die komplexe Probleme vereinfachen und ein sichereres, nachhaltigeres und überzeugenderes Fahrer- und Passagiererlebnis schaffen.
Mit Niederlassungen in den Vereinigten Staaten, Deutschland, Japan und Indien arbeiten wir mit führenden Automobilherstellern und Tier-I-Zulieferern zusammen, um die Mobilität von morgen mit hochwertigen und kosteneffizienten Softwarelösungen zu gestalten.

Die bisherige Reise

2013
Acsia Technologies wurde als Private Limited Company gegründet.
2014
Aufnahme des Geschäftsbetriebs im Technopark Thiruvananthapuram mit 3 Ingenieuren.
2016
Eröffnet ein Büro in Hyogo, Japan.
Gewinnt das erste Cluster-Projekt aus Japan.
2017
Gewinnt das Android-basierte Rear-Seat Entertainment (RSE)-Programm eines europäischen Automobilherstellers durch eine Partnerschaft mit einem deutschen Tier-I.
2018
Sichert die Zertifizierungen ISO9001:2015 und ISO27001:2013.
Liefert Projekte, die mit ASPICE CL1 bewertet wurden.
Betritt den ADAS-Bereich (Advanced Driver Assistance System).
Die Zahl der Mitarbeiter überschreitet die 100er Marke.
2019
Einstieg in die E-Mobilität.
Die Zahl der Mitarbeiter überschreitet die 200er Marke.
Liefert Projekte, die mit ASPICE CL2 bewertet wurden.
2020
Beginnt mit der vollständigen Unterstützung der Cluster-Entwicklung für alle Modelle der nordamerikanischen OEMs durch Tier-I.

Setzt ISO 26262 und ASPICE CL3 im QMS ein.
2021
Gewinnt das ASPICE-Konformitätsprojekt für das IVI-System.
Gewinnt das Projekt für Infotainment im Fahrzeug.
2022
Sichert die TISAX AL3-Zertifizierung.
Setzt ISO 21434 im QMS ein.
Eröffnet ein Büro in München, Deutschland.
Eröffnet ein Verkaufsbüro in Bengaluru, Indien.
2023
Eröffnet ein Büro in Detroit, USA.
Acsia erwirbt das deutsche Automobilsoftware-Startup Arctictern.
Sicherstellung der Great Place To Work (GPTW) Zertifizierung.
2024
Einweihung des globalen Hauptsitzes und des Forschungs- und Entwicklungszentrums in Thiruvananthapuram.
Sichert die GPTW-Rezertifizierung.
Wird 'Langfristiger Partner' für einen führenden globalen Luxus-OEM.
Einführung von LiLA, einer KI-gesteuerten Acsia Copilot Suite.

Treffen Sie das Führungsteam

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Jijimon Chandran
Jijimon Chandran
Founder & CEO
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Rejeesh R Pillai
Rejeesh R Pillai
VP and Head Defence & Aerospace BU
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Anil Shahul Hameed
Anil Shahul Hameed
VP and Head Mobility BU
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Priya balan
Priya Balan
Vice President – People & Values
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Vasanthraj G Pillai
Vasanthraj G Pillai
VP Technology & Innovation
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Atsushi Masuyama
Atsushi Masuyama
Head of Sales & Marketing, Japan
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Nibil P M
Nibil P M
AVP & Head, Advanced Technology Group
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Gloria Joseph
Gloria Joseph
AVP and Head, Pre-sales and Solutioning
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Ratish Bhat
Ratish Bhat
Director Sales – India
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Rajan Thomas
Rajan Thomas
Global Delivery Leader
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Valliaappan Chidambaram
Valliaappan Chidambaram
Head of Verification & Validation
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Lutz Nettig
Lutz Nettig
Head of Sales and Business Development, Europe
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Latha Chembrakalam
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Ajith kumar
Dr. K Ajith Kumar
Exp: 30+ Years
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Jijimon Chandran
Jijimon Chandran
Founder & CEO
LinkedIn icon.svg
Rejeesh R Pillai
Rejeesh R Pillai
VP and Head Defence & Aerospace BU
LinkedIn icon.svg
Anil Shahul Hameed
Anil Shahul Hameed
VP and Head Mobility BU
LinkedIn icon.svg
Priya balan
Priya Balan
Vice President – People & Values
LinkedIn icon.svg
Vasanthraj G Pillai
Vasanthraj G Pillai
VP Technology & Innovation
LinkedIn icon.svg
Atsushi Masuyama
Atsushi Masuyama
Head of Sales & Marketing, Japan
LinkedIn icon.svg
Nibil P M
Nibil P M
AVP & Head, Advanced Technology Group
LinkedIn icon.svg
Gloria Joseph
Gloria Joseph
AVP and Head, Pre-sales and Solutioning
LinkedIn icon.svg
Ratish Bhat
Ratish Bhat
Director Sales – India
LinkedIn icon.svg
Rajan Thomas
Rajan Thomas
Global Delivery Leader
Strategische Berater
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Image Profile
Latha Chembrakalam
  • Former VP and Head of Technical Centre India, Continental AG.
  • 17+ years with Bosch Engineering (VP of Powertrain and Electrification BU).
  • Has worked with global OEMs across Europe, Americas, China and Japan.
Teamdplinkedin img
Ajith kumar
Dr. K Ajith Kumar
  • Outstanding Scientist (Retd) and Former Director, Naval Physical and Oceanographic Laboratory (NPOL), DRDO
  • Former Project Director of Maareech (Advanced Torpedo Defence System for Ships of Indian Navy)

Zweck

Die automobile Zukunft durch herausragende Technologie und Innovation vorantreiben.
Mission
Wir entwickeln Technologien, die komplexe Herausforderungen vereinfachen und die Möglichkeiten im Inneren eines Fahrzeugs erweitern.
Vision
Die Mobilität mit großen Ideen in der Automobiltechnologie verändern.
Acsia Werte
Integrität
Gehen Sie Ihren Worten Taten folgen, auch wenn niemand zuschaut.
Leidenschaft
Lassen Sie das Feuer im Bauch hell brennen.
Einigkeit
Gemeinsam sind wir immer klüger und stärker.
Respekt
Zeigen Sie Respekt für einander und unsere Arbeit.
Exzellenz
Sie haben bei allem, was wir tun, ein Auge fürs Detail.
Engagement für die Gesellschaft

Acsia Foundation Trust steht an der Spitze der sozialen Verantwortung von Unternehmen (CSR) der Organisation. Seit über einem Jahrzehnt arbeitet Acsia mit führenden Nichtregierungsorganisationen (NGOs) zusammen, um verschiedene Initiativen in den Bereichen Grundschulbildung und Nachhaltigkeit zu unterstützen.

Im Jahr 2023 wurde der Acsia Foundation Trust gegründet, um unseren Interventionen eine formale Struktur zu geben und eine stärkere Fokussierung und Verantwortlichkeit für die Ergebnisse zu gewährleisten. Acsia Technologies hat im GJ 2023-24 vier CSR-Projekte durchgeführt.

Solarisierung des CIMR (Central Institute on Mental Retardation), Thiruvananthapuram, das sich der Erziehung, Ausbildung, Entwicklung und Rehabilitation geistig behinderter Menschen widmet. Das Projekt wird sowohl die Betriebskosten als auch den ökologischen Fußabdruck reduzieren.

Zwei Projekte in Zusammenarbeit mit Kanal Innovations Charitable Trust, die sich für die Stärkung von Kindern einsetzen.

  • Ein Abschlussschulprojekt am Government Women’s Polytechnic College, Kaimanam, und am Central Polytechnic College, Nettayam, das grundlegende Fähigkeiten wie Kommunikation, Führungsqualitäten, kulturelle Orientierung, Erstellen von Lebensläufen und grundlegende Geschäftsetikette wie das Verfassen von E-Mails vermittelt, um die Schüler mit den notwendigen Werkzeugen und Kompetenzen auszustatten, damit sie sich auf dem heutigen wettbewerbsintensiven Arbeitsmarkt behaupten können.
  • Verbesserung der Infrastruktur der Government Upper Primary School in Valiyathura, die während des Zyklons Ockhi beschädigt wurde, als sie als Hilfslager diente.

Beach Wall Art Projekt in Shanghumukhom in Zusammenarbeit mit der Sustera Foundation, die sich für den Aufbau von klimaverantwortlichen Gemeinden einsetzt. Das Projekt zielt darauf ab, das Bewusstsein für Meeresmüll und Plastikverschmutzung zu schärfen und eine nachhaltige Umwelt zu schaffen.

KAnal
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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.