Transforming Unstructured Engineering Data into a Structured Intelligence Pipeline
We developed a scalable AI-driven pipeline to accurately process and classify health data from diverse sources, ensuring secure, cost-efficient, and structured insights for personalized health analytics.
Faster Data Ingestion
Automated ETL processes enabled rapid extraction from engineering documents, reducing manual processing time.
Improved Extraction Accuracy
Chunked document processing and targeted inference significantly reduced errors and improved data reliability.
Cost-Efficient AI Processing
Optimized use of local GPU-VM models minimized inference costs while maintaining high processing speed.
Higher Data Integrity
A secondary validation pass ensured structured data met quality standards, improving downstream usability
Overview
Our client needed a robust ETL pipeline to extract and organize data from engineering specifications buried in dense PDFs, complex tables, and intricate diagrams. Traditional methods struggled to handle the combination of text, structured data, and visual elements, making it difficult to retrieve actionable insights. The project required a retrieval-augmented approach to ensure structured, accessible data for downstream tasks.
Objective
The primary goal was to streamline document ingestion and data retrieval while ensuring that extracted content was structured and accurate enough for immediate use. The pipeline needed to accommodate varied document formats, handle complex cross-references, and maintain cost efficiency without sacrificing performance.
Challenges
Processing large, unstructured documents posed multiple challenges, from extracting engineering details hidden in intricate diagrams to ensuring consistency across diverse document formats. Standard OCR techniques lacked the precision needed for structured retrieval, and relying on large-scale inference models risked ballooning costs. Additionally, avoiding hallucinations and inaccuracies in extracted data was critical to maintaining reliability.
Solution
We implemented LlamaIndex to build and maintain a vector database, allowing for efficient retrieval-augmented processing of document content. While considering alternatives like LangChain and Haystack, we chose LlamaIndex due to its lightweight dependencies, strong developer community, and flexible plugin support, enabling rapid iteration and experimentation. To balance performance and cost, we deployed a GPU-VM-hosted 14B parameter model for most classification and extraction tasks, ensuring fast and cost-effective processing. For more advanced needs, such as parsing intricate diagrams or cross-referencing large sections of text, we selectively used GPT-4, taking advantage of its extended context window and vision capabilities without excessive reliance on high-cost inference. A critical breakthrough was chunking documents into smaller, context-specific sections rather than processing entire files at once. By breaking down PDFs and OCR-extracted text into segments tied to specific engineering components, we significantly improved extraction accuracy and reliability while reducing the risk of hallucinations. Additionally, we introduced a secondary LLM validation pass to detect and correct inconsistencies before loading data into the structured store, ensuring high data integrity. By transforming disparate, unstructured engineering documents into a cohesive, structured intelligence pipeline, we provided the client with a faster, more reliable way to access and analyze engineering insights while optimizing for cost and efficiency.
