Oil & Gas Agentic Chat
Asset teams make better decisions when every answer traces back to the source.
Oil & gas companies already own decades of asset knowledge. Most of it sits in unstructured documents, reports, and legacy maps that expert staff spend hours finding, reading, and transcribing. For years, the industry’s answer has been to build rigid, pre-curated “golden databases” that are costly to maintain and quickly go out of date.
Oil & Gas Agentic Chat functionality takes a different approach. Instead of relying on manual transcription, specialized AI agents read messy historic files on demand and extract the structured data your asset team needs, when it needs it. Every extracted value is anchored to its precise document citation, so engineers and geoscientists can validate results against the original source before acting on them.
Built on the i2k AI Platform and its deep oil and gas domain knowledge, Oil & Gas Agentic Chat workflows act as an intelligent assistant that can carry out multistep geotechnical tasks across millions of documents.
“By anchoring every extracted value to its precise document citation, the architecture enables asset teams to improve decision-making with source data validation. The i2k AI Platform technology allows operators to treat their untouched historical archives as a master record of the asset knowledge they already own.” —John Boden, CEO, i2k Connect
Oil & Gas Agentic Chat deliverables
Just-in-time extraction of structured data from unstructured documents, reports, and legacy maps, without first building a “golden database”
Specialized AI agents that execute multistep geotechnical tasks across millions of files
Recognition of basins, fields, wells, formations, and customer-supplied taxonomies
The ability to read well files, tabular data in PDFs, and log data files (LAS, LIS, DLIS)
Geological, geophysical, and engineering analyses built directly from original source documentation, including seismic imaging, identifying new drilling opportunities, complex 3D well trajectories, stratigraphic cross sections, production decline curves and production enhancement
A precise document citation for every extracted value, so results can be checked and trusted
Fewer human hours spent manually searching and transcribing corporate archives
Reduced operational risk and faster technical workflows for asset and executive teams
Why it matters
Your historical archive becomes a working master record of asset knowledge, not a storage cost. Teams spend less time hunting for data and more time interpreting it, and they can trace every conclusion back to its source document. With MCP (Model Context Protocol), an open standard, AI assistants and agents can discover, connect to, and work with external data sources, enterprise systems, local files, and software tools, without a custom integration for each one. The i2k map/search/chat workflow builds on this to save significant time, with typical LLM token savings of 70% to 80%.
