Success Story

Connected Cluster Development for India’s leading Electric Scooter Brand

Take a deep dive into how Acsia leveraged its expertise in Kanzi and Android-based HMI projects to deliver cutting-edge connected cluster experiences for the three upcoming models of a leading electric scooter brand in India. Acsia’s solution integrated system HMI, supported custom hardware specifications, and met the aggressive deadlines set by the client, setting a new standard in the Indian electric scooter market.

Business & Technology Landscape

In 2021, the electric scooter market in India experienced several notable trends:

Digital Instrument Clusters: Electric scooters increasingly featured digital instrument clusters providing essential information such as speed, battery levels, and navigation details. These clusters often included advanced features like turn-by-turn navigation and Bluetooth connectivity, enhancing the overall riding experience.

HMI Development: Human-Machine Interfaces (HMIs) became more intuitive and interactive, integrating smartphone connectivity and real-time vehicle diagnostics. This development made it easier for riders to access and control various functionalities of their scooters, contributing to safer and more convenient rides.

Navigation System Integration: Navigation systems in electric scooters offered real-time route guidance and traffic updates directly on the scooter’s display. This integration was particularly useful in urban settings, helping riders navigate efficiently through changing traffic conditions.

Connected Features: There was a growing emphasis on connected features, such as IoT integration, allowing scooters to communicate with smartphones and other devices. This trend facilitated features like remote diagnostics, over-the-air updates, and enhanced security measures.

Customer Problem Statement

The customer, an Indian OEM, wanted a feature-rich and cutting-edge connected cluster experience for their three upcoming scooter models planned for imminent launch. They needed a reliable partner, with specalised expertise and resources, who could meet the aggressive deadline and create a high-quality product. The customer also wanted a Kanzi-enabled HMI and an Android-based navigation system inbuilt in the scooter, which had to be created in the C++ tool of Kanzi before deploying it in the Android environment. This was the first-ever electric scooter in India with such robust HMI features, so there was no best practices template for any vendor or their in-house team to follow. The project complexity and tight deadline made it a challenging task.

Acsia Solution

Acsia was chosen for its deep domain expertise in Kanzi and Android-based HMI projects. Acsia designed and developed the HMI of the scooter based on the screen designs provided by the OEM. They integrated the navigation system, made on Android 1.0 OS, with the Kanzi (3.6.15 version) based system HMI. Key features implemented by Acsia included:

Navigation Integration: Integration of the navigation application into the vehicle cluster HMI proved challenging due to display mirroring and the need for seamless user interaction injection into the Android application.

Audio Ducking: Implemented audio ducking support to manage Bluetooth audio, ensuring vehicle notification chimes (indicator, reverse, etc.) are audible despite Bluetooth streaming.

Keyboard Integration to Kanzi: Integrated the Android keyboard into the Kanzi UI, ensuring consistency between the Kanzi-based cluster and various Android applications. This included support for dark and light modes in the default AOSP keyboard.

Custom Kanzi Plugin Integration: Developed and integrated custom Kanzi plugins to support system requirements not met by the default Kanzi engine. These included:

Custom Click Listener: Redirects all UI interactions in Kanzi directly to the Android layer event listener, facilitating clean architecture and generic event handling.

XML Data Source: Allows Kanzi UI developers to test data source integration without actual backend data feeding, supporting parallel development.

Slideshow Plugin: Implements complicated animations and GIF support as a sequence of images rendered into the UI.

Business Outcome & Impact

  • The customer met their deadline and rolled out their vehicle to the market.
  • The feature-rich cluster was the first of its kind in the Indian market for a scooter, providing an excellent user experience for drivers.
  • The integrated navigation system delivered significant improvement in route calculation times compared to other models, enhancing user experience in urban environments.
  • The integration of Kanzi and Android systems resulted in significant reduction in overall system response time, making the interface more responsive.
  • The project established the brand as a leader in innovation in the electric scooter market in India, being the first to introduce such advanced connected cluster features.

Key Learning

Deep expertise in Kanzi and Android-based cluster projects, with the skillset to handle end-to-end development and provide various customizations as needed.

Expert Speak

Gloria Joseph
Delivery Leader
Integrating the advanced features of both Kanzi and Android HMI systems required a thorough understanding of the technologies and meticulous planning. Our team's collaborative efforts ensured a successful implementation, providing an exceptional user experience.
Vasantharaj G
VP Technology & Innovation
Acsia's deep expertise in Kanzi and Android-based HMI projects was crucial in meeting our aggressive deadlines and delivering a high-quality product. Their ability to integrate complex systems seamlessly was impressive.
Request a Meeting
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.
This site is registered on wpml.org as a development site. Switch to a production site key to remove this banner.