PIEScale solves the same core problems regardless of industry — getting complex, siloed data into a form that AI can use, and deploying agents on top of it. These are the six use cases where it shows up most clearly.
Different industries, different data types, same underlying challenge: fragmented, ungoverned data that AI can't reach and engineers can't trust.
Ground AI assistants and agents in governed operational data — not a curated demo set. PIEScale exposes trusted data via MCP servers so any AI assistant queries live, governed information, not stale exports or hallucinated answers.
Migrate legacy well, log, and seismic archives into an OSDU-compliant foundation — with custom schema support for the proprietary formats OSDU alone doesn't cover. No big-bang cutover, no custom scripts written from scratch.
DTS/DAS, SCADA, and sensor streams processed at the edge via RTSS — Petrabytes' extension of RTDIP, contributed back to the open OSDU standard. Data moves from sensor to governed foundation without overnight batching.
Unified well, production, and reservoir data engineers can actually query — geomechanics, flow assurance, and reservoir monitoring on one governed foundation instead of reconciled from three separate systems.
Audit trails, entitlements, and lineage built into the platform's five-layer security model — so compliance reporting is a governed byproduct of daily operations, not a manual reconstruction exercise after the fact.
One governed namespace across geoscience, engineering, and operations — so a geology model update or production anomaly is visible to every team working from the same data, not just the team that owns the source system.
A 30-minute discovery call grounded in your actual data and the specific decision or workflow you're trying to improve.