AI-Powered Document Processing for a German Automotive Company
Client Overview
A leading German automotive compliance and documentation company that provides due diligence, policy management, and regulatory alignment services for major global brands including Ford, Mercedes-Benz, and BMW. The organization handles extremely large and complex IATF policy documents that must be compared, analyzed, and searched with high accuracy.
The Challenge
The client worked with thousands of pages of technical policies, each containing clauses labeled in numerical formats such as 4.9.1.2. However, a major challenge existed:
– Each manufacturer (Ford, BMW, Mercedes) used the same clause numbering structure.
– Clause “4.9” in Ford’s document was completely different from clause “4.9” in BMW’s document.
– This made cross-document semantic search nearly impossible.
– Manual review required hours of effort and still resulted in errors.
Solution Overview
We designed a complete AI-driven document processing and semantic search platform. The solution combined NLP, RAG principles, vector embeddings, and graph databases to deliver context-aware document intelligence.
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The client needed:
– A system that could extract and parse documents automatically.
– A method to separate clauses while retaining the correct brand context.
– Smart semantic search that understands differences between identical clause numbers.
– A scalable cloud-based solution accessible to compliance teams.
Detailed Solution
We built automated crawlers to gather all relevant IATF and brand-specific policy documents from controlled sources. The pipeline supported:
- PDF extraction
- Multi-file batch downloading
- Automated version tracking to avoid duplicates
Each PDF was cleaned and standardized:
- Formatting inconsistencies were normalized
- Headings and subheadings were structurally identified
- Clause hierarchies were extracted
- Tables and multi-column layouts were parsed into machine-readable form
The most critical component of the system:
- Every clause was split into independent chunks (e.g., 4.9 → 4.9.1 → 4.9.1.2) -
- Each chunk received the following metadata:
- Manufacturer name (Ford, Mercedes, BMW)
- Document ID
- Clause number
- Parent clause reference
This guaranteed that even if “4.9” appeared in three documents, each chunk was uniquely identifiable.
Using advanced transformer models:
- Each chunk was converted into a high-dimensional vector embedding
- Embeddings captured meaning, context, and technical intent
- Similar clauses across brands could now be compared accurately
To improve retrieval quality:
- All chunks and embeddings were stored in Neo4j
- Relationships such as “belongs_to,” “same_clause_number,” and “similar_to” were created.
- This allowed multi-hop reasoning such as:
Show me all clauses related to section 4.9 for BMW that are similar to Ford’s requirements
The system was deployed on Azure with:
- Azure Functions for serverless processing
- Azure Blob Storage for document archiving
- Secure API endpoints for internal teams
- Role-based access controls (RBAC)
- Auto-scaling for heavy processing tasks
Impact & Results Of The Project
- Context-aware search became possible for the first time
- Identical clause numbers across companies no longer caused confusion
- Compliance teams could instantly compare Ford vs. BMW vs. Mercedes clauses
- Document review time decreased by more than 60%.
- Due diligence accuracy increased significantly because every chunk carried source-specific metadata
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Conclusion
This project demonstrates the power of combining NLP, document intelligence, vector embeddings, and graph databases. By preserving brand context within every document chunk, the platform solved a long-standing ambiguity problem in the automotive compliance industry. The resulting system is scalable, precise, and future-proof for expanding document libraries.