Workflow · energy · workflow
Halliburton reduces seismic workflow creation time by over 95% using Amazon Bedrock and generative AI
Halliburton's Seismic Engine required manual configuration of approximately 100 specialized tools to build workflows, a process that was time-consuming, error-prone, and required deep expertise, potentially limiting accessibility and efficiency.
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 · User submits natural language query
Users submit natural language queries through a FastAPI application's streaming interface.
Tools used
Amazon BedrockAmazon Bedrock Knowledge BasesAmazon NovaAmazon Nova LiteAmazon DynamoDBAmazon OpenSearch ServerlessAmazon Titan Text Embeddings V2Claude 3.5 Sonnet V2Claude 3.5 HaikuFastAPIAWS App RunnerLangChainS3Seismic Engine
Outcome
The AI-powered assistant achieved workflow generation success rates of 84–97%, surpassing both new and experienced users, and reduced workflow creation time by over 95% compared to manual processes, while making advanced geophysical tools more accessible.
Results
Time savedover 95%
Volumeup to 95%
Cost replaced84%
Grounding & classification
Source type: technical build writeup
40 fields verified against source quotes.
agentic workflowai agentconversational airagknowledge basemetric backednamed customersource backedtools describedvendor confirmedworkflow describedenergyaccuracy improvementcycle time reductionemployee productivitytechnical build writeupagentic task executionrag answering