AI assistants and domain agents deployed on your own governed data — not a curated demo set. From MCP server configuration to supervisor agent routing to production-ready specialist agents, scoped and delivered.
Every engagement is structured around specific deliverables — not open-ended retainers. You know what you're getting and when before the work starts.
Design the agentic architecture for your environment — which agent categories, which MCP servers, which specialist agents, and how the Supervisor Agent routes between them.
Configure and deploy PIELake MCP servers — Well Master, Well-Logs, Seismic, GIS, Core Data, Visualization, QC — exposing governed data to any MCP-enabled AI assistant.
Deploy and configure specialist agents — Porosity, Rock Strength, Pseudo-Density, Geomechanics, Seismic — each scoped to one discipline, accurate within it.
Deploy governed enterprise search across engineering documents, operational data, and reference material — grounded in access-controlled, entitlement-checked results from PIELake.
Every AI deployment runs on the same entitlement-checked data your team already trusts.
Vendor-neutral from day one — swap the AI front-end without rebuilding the governed data layer.
The petrophysicist who knows what a curve means configures the agent — no engineering ticket required.
Scoped around a production deliverable — not an open-ended exploration that never ships.
Monitoring, drift detection, and governance are part of the initial scope.
Search & Discovery, Data Processing, Data Quality, Data Inference, AI-Assisted Interpretation — matched to your personas and use cases.
The full use case page — what the agent categories look like and how MCP servers expose governed data to any AI assistant.
See Use Case →The low-code agent framework and MCP server architecture underneath every Enterprise AI engagement.
Explore PIEScale →A 30-minute discovery call built around your specific agent categories, personas, and data — not a generic AI pitch.