Halliburton Landmark has successfully integrated generative AI capabilities into its Seismic Engine workflows using Amazon Bedrock. This transformation enables geoscientists and data scientists to convert natural language commands into executable seismic processing workflows, dramatically improving speed, accessibility, and accuracy within cloud-native infrastructure.
- Up to 95% reduction in seismic workflow configuration time
- Natural language interface powered by Amazon Bedrock and Anthropic Claude
- Cloud-native architecture ensures scalable, low-latency interaction
Infrastructure signal
The solution leverages a fully cloud-native architecture built on AWS services to support high availability and scalability. Central infrastructure components include Amazon Bedrock for large language model processing, FastAPI deployed on AWS App Runner for API management, and DynamoDB for chat state persistence. Additionally, Amazon OpenSearch Serverless is utilized for indexing and querying Seismic Engine documentation to enable on-demand question answering.
This infrastructure supports streaming interactive sessions that deliver low-latency conversational experiences. Intent routing powered by Amazon Nova Lite within Amazon Bedrock dynamically directs queries to the appropriate LLM functionality. The use of fully managed AWS services helps reduce operational overhead, providing a dependable foundation to handle complex geophysical data workflows at scale.
Developer impact
Developers now focus on creating robust multimodal AI agents that translate natural language into structured YAML workflows. This reduces reliance on deep domain expertise for manual workflow scripting by enabling conversational interaction with the Seismic Engine’s 82 specialized tools. The generative AI system also handles natural language Q&A, offering seamless access to extensive technical documentation without interrupting workflow creation.
The integration of chat state tracking in DynamoDB facilitates multi-turn dialogue, allowing users to iteratively refine workflows through conversation. Streaming response support further improves user experience by providing immediate feedback during processing. These enhancements streamline developer workflows by automating complex transformation logic and improving integration testing capabilities within a managed cloud environment.
What teams should watch
Teams working on technical workflow automation or domain-specific AI assistants should monitor the evolving integration patterns between large language models and cloud-native infra components like managed databases and serverless search. Halliburton’s use case illustrates how to orchestrate proprietary domain knowledge with generalized generative AI to enhance both productivity and accuracy at scale.
Attention should also be given to the operational implications of deploying multi-intent AI systems via API gateways with intent classification and streaming capabilities. Efficient logging and state management using managed NoSQL stores like DynamoDB can enable responsive, context-aware interactions necessary for real-time engineering workflows without compromising on reliability or cost efficiency.