Acsia Technologies Earns ISO 9001:2015 and ISO 27001:2013 Certifications
Jul 12, 2019
Acsia Technologies has achieved ISO certification, recognizing their outstanding Quality and Information Security Management Systems.

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Recognized for Exemplary Quality and Information Security Management Systems

 

Thiruvananthapuram, July 12, 2019: Acsia Technologies, a global leader in automotive software powering Connected Vehicles, Infotainment Systems, and e-Mobility, today announced that it has been awarded the esteemed ISO 9001:2015 quality management system certificate and ISO 27001:2013 information security management system accreditation. Both certificates adhere to the latest standards introduced by the International Organization for Standardization (ISO).

Acsia underwent a comprehensive independent appraisal and assessment, scrutinizing various aspects of its internal and customer-facing operations. The 2015 standard upgrade offers businesses a more comprehensive approach to managing business processes compared to the ISO9001:2008 version.

Notably, the new standard eliminates the need for a documented procedure for internal audit, instead requiring the establishment of an internal audit program. ISO 9001:2015 places a stronger emphasis on risk management, customer satisfaction, leadership, and commitment compared to the 2008 version.

The ISO 27001:2013 accreditation underscores Acsia’s ongoing dedication to data security, particularly regarding client data, systems, and processes. Acsia prioritizes ensuring the integrity, availability, and security of all data as part of its commitment to clients.

As one of the world leaders in automotive software, Acsia uses its expertise to develop software tools and platforms to simplify complex problems. They work with top automobile brands, OEMs and Tier 1 companies across India, Japan, Sweden, Germany, and United States. Their products deploy the latest advances in the field and enable their clients to provide delightful experiences to their customers.

Since 2014, Acsia has built a strong reputation for delivering on-time and high-quality projects. Obtaining ISO certification aligns with Acsia’s strategic plan to achieve and maintain the highest levels of process maturity in security and quality management practices. Acsia remains committed to providing customers with high-quality, reliable, and innovative services and solutions.

 

Press Contact

Athul Lal A G
Director of PR
Email: athul.lal@acsiatech.com
Mob: +91 81290 07793

 

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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.
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  • 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:

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  • 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

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Challenge

Build an AI-powered log analytics assistant that can:

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Goal

Deliver a working prototype that:

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Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
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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

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Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

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  • Accessibility needs (visual/hearing impairments, elderly passengers).

 

Goal

Deliver real-time, adaptive personalization of:

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  • 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.
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  • Improved accessibility and inclusivity for diverse user needs.
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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.
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Challenge

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

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  • Customer demographics (age, income, region).
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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.
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Context

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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).
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  • 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:

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  • 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.
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  • 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)
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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

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  • 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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