How Infosys built a generative AI solution to process oil and gas drilling data with Amazon Bedrock
Oil and gas operations generate vast amounts of complex multimodal technical documents — well completion reports, drilling logs, and lithology diagrams — that conventional non-AI processing methods fail to handle due to specialized terminology, interconnected data relationships, and mixed text and image formats, resulting in inefficient data extraction and time-consuming manual processing.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Technical document ingestion
Well completion reports, drilling logs, and lithology diagrams are ingested into the system for processing.
Tools used
Amazon BedrockAmazon Bedrock Nova ProAmazon Bedrock Knowledge BasesAmazon OpenSearch ServerlessAmazon Titan Text EmbeddingsCohere Embed English modelBGE RerankerAmazon Q DeveloperPyMuPDFOpenCVInfosys TopazColBERT
Outcome
The final hybrid RAG solution achieved 92% retrieval accuracy against a human expert baseline, under 2-second average query response time, a 4.7/5 user satisfaction rating from field engineers and geologists, a 40–50% decrease in manual document processing costs, and field engineers spending 60% less time searching for technical information.
What failed first
Three iterative RAG approaches were tried before the final design: the initial image-analysis approach worked for text but failed on image-related queries; ColBERT multi-vector embeddings proved difficult to store and manage; and fixed-size chunking improved keyword retrieval but produced fragmented long-form answers by splitting related information across chunks.