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CASE STUDIES

Content and Repair Procedure Aggregator

Implementation:

2024-2025

Industry:

B2B Automotive Information Services / InsurTech

Client's Business Profile:

A provider of expert systems that aggregate technical and market data from car manufacturers. The institute collects, processes, and structures raw technical, design, and repair data directly from global car manufacturers and makes it available to dealers, appraisers, and insurers.

Implementation:
Industry:
Client's Business Profile:

2024 – 2025

B2B Automotive Information Services / InsurTech

A provider of expert systems that aggregate technical and market data from car manufacturers. The institute collects, processes, and structures raw technical, design, and repair data directly from global car manufacturers and makes it available to dealers, appraisers, and insurers.

Documentation
Chaos
Lack of Conceptual Consistency
Low Team Efficiency

Challenges

The rapid delivery of precise technical, market, and repair data to dealers, appraisers, and insurers encountered a significant technological and structural barrier. As data volume increased, the lack of data consistency and extreme knowledge dispersion became a challenge.

  • Technical data and repair procedures from global car manufacturers have their own unique documentation standards and unstructured files in various formats.
  • The same concepts (e.g., repair procedure type, model, or brand) are often described differently depending on the manufacturer, which prevents automatic data merging without prior manual verification.
  • Dispersed knowledge led to high costs for employees to access necessary data. Employees spent an average of 20-30% of their working time searching for information required to complete tasks.

Solution

  • Designing a dedicated architectural layer that acts as an intelligent integrator between raw, dispersed engineering data and the end-user. This layer automatically downloads, maps, and processes files with diverse structures.
  • Thanks to advanced data processing and normalization algorithms, content is standardized, categorized according to uniform metadata (e.g., brand, model, year, repair procedure type), and merged into a consistent data model.
  • Optimizing the knowledge base for instant searching using advanced indexing mechanisms.
  • Users were provided with an intuitive, central search interface operating in self-service mode. The system allows for instant searching of all documentation, regardless of the original source file format, and generates precise, contextual results in a fraction of a second.

Client Gains

Reclaiming Team Work Time

This allowed experts to focus on key analytical and operational tasks.

Unification of Organizational Knowledge

Elimination of information silos and protection of knowledge capital.

Accelerating Business Partner Service Time

Data organization allowed for instant verification of inquiries from dealers, appraisers, and insurers.

Operational Scalability

Readiness for advanced systems. Creating a stable intermediate layer separating raw files from the user provided the organization with technological flexibility and readiness to seamlessly connect AI systems in the future.