PIEScale started in energy — decades of legacy well, seismic, and production data with nowhere governed to live. The same problem shows up anywhere data is complex, siloed, and supposed to feed AI it currently can't reach.
Different data, different regulations, the same core problem: legacy formats, siloed systems, and AI that can't reach any of it safely.
Upstream, midstream, and renewables data — decades of well logs, seismic surveys, and production records, plus the real-time drilling, reservoir, and grid data that's supposed to inform decisions today, not next quarter.
Complex geological and operational data — drill collars, assays, geology models, and plant operations that are often as siloed as energy's legacy archives, split across geology, engineering, and operations teams that rarely see the same picture.
Industrial automation and plant data — sensor streams, MES/OEE systems, and historian data collected constantly on the plant floor but rarely connected to enterprise AI or the teams making planning decisions upstream.
Grid, generation, and transmission data — the same real-time streaming and asset-monitoring challenges as energy, applied to a grid where compliance reporting isn't optional and downtime has immediate public consequences.
Process, safety, and plant operations data — where governance isn't a nice-to-have, because it's tied directly to safety incidents and regulatory compliance, not just reporting convenience.
Ground AI assistants and agents in governed operational data, not a curated demo set.
Migrate legacy well, log, and seismic archives into an OSDU-compliant foundation.
DTS/DAS, SCADA, and sensor streams processed at the edge, not batched overnight.
Unified well, production, and reservoir data engineers can actually query.
Audit trails, entitlements, and lineage built for compliance, not bolted on after.
One governed namespace across geoscience, engineering, and operations.
A 30-minute discovery call — grounded in your actual systems and data, not a generic pitch.