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About the Client

The client is a technology-driven enterprise operating in the automotive or fleet management domain, focused on enhancing operational efficiency through smart data systems.

Their platform caters to fleet operators, vehicle manufacturers, and asset managers, aiming to deliver better control, visibility, and automation around vehicle categorization, classification, and reporting.

The goal: use advanced search, classification, and data visualization tools to simplify the decision-making process around automotive asset management.

Challenges

Search & Classification Efficiency

Traditional search mechanisms were too slow and inaccurate for large-scale vehicle datasets.
The system lacked flexible querying for multi-attribute vehicle classification.

Data Flow & Result Accuracy

The challenge was to generate accurate classification results dynamically based on changing mapping rules.
Required robust handling of large volumes of semi-structured data.

Scalability & Performance Bottleneck

As asset categories grew, the system struggled with performance and maintainability.
Data representation and manipulation needed to evolve with a modern, scalable design.

UI/UX Limitations in Visualization

Insights were hard to interpret due to poor graphical representation and outdated reporting.
The client wanted dynamic report generation with visual insights.

Scale Without Sacrificing Performance

We engineer modular architectures that grow effortlessly with your data and users.

Solution: Challenge → Solution → Result Mapping

Challenge	Solution	Result
Poor Search Speed and Relevance	Implemented ElasticSearch with customized indexing and query configurations to support rapid filtering by asset attributes (e.g., make, model, class, usage).	Achieved real-time, attribute-level filtering with significant speed improvement (up to 80% faster search queries).
Inflexible Mapping Logic	Introduced a Mapping V2 module—a flexible rule engine where administrators can define classification rules for assets.	Enabled dynamic result generation based on custom rule sets; improved rule accuracy and reduced classification errors.
Incomplete Asset Categorization	Developed a modular Asset Classification engine with sub-modules for granular categorization based on user-defined rules and ElasticSearch filters.	Delivered a highly configurable classification engine, adaptable to multiple business use cases.
Poor Reporting & Visualization	Integrated graphical report generation, providing real-time dashboards based on classified and mapped data.	Improved decision-making through visual insights; enhanced user experience and system usability.

Tech Stack & Engineering Highlights

Frontend: Angular (or similar SPA framework)
Backend: Node.js or Python (for orchestration & rule management)
Database: ElasticSearch (document-oriented search & classification)
Visualization: Chart.js / D3.js / Highcharts for graphical reporting
Data Format: JSON-centric storage for flexible querying
Engineering Practices:
○ Modular rule engine design
○ Document-oriented data manipulation
○ Incremental rollout with feedback loops

Architecture Overview

Chrysalis Solmotive Limited

Measurable Results / ROI

80% faster search results using ElasticSearch indexing
Dynamic asset classification with 95%+ accuracy
Enhanced UX with visual dashboards and reporting
Increased system maintainability and rule flexibility
Shortened release cycles due to modular development

Why ValueCoders?

Expertise in ElasticSearch & document-oriented systems
Agile sprints with sprint-wise value delivery
UX-first development with business-friendly dashboards
Modular design enabling future extensibility
Proactive collaboration with brainstorming before each sprint

Client Quote

“The new classification and mapping system has dramatically improved our ability to manage and visualize asset data. The integration of ElasticSearch and the rule-based Mapping V2 module has taken our data clarity and operational agility to the next level.”

– Client Representative, Chrysalis Solmotive Limited

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