Typical Opaque AI Platform

Geoplat Artificial Intelligence

Synthetic data often narrower, with weaker control of imaging realism

Requires spectral decomposition + attribute pre-computation

Aggressive filtering blurs high-frequency components

Requires full retraining for new basins (weeks to months)

Limited visibility into training data provenance and failure modes

SaaS / cloud-only — proprietary seismic data must be uploaded externally

Heavy infrastructure requirements, often impractical for secure corporate environments

12 years physics generator R&D — 1M+ paired training samples since 2019

Optimised runtime: ≤32 GB RAM at peak, 16 GB stable — no GPU cluster required

Transparent training pipeline with explicit geological and physical assumptions

Transfer Learning — any basin adapted in ~20 min

Full kinematic + dynamic signal preservation (AVO-safe)

Works directly on raw amplitude — no attribute pre-computation

On-premises deployment — seismic data never leaves your infrastructure

The differences that matter to a working geophysicist: training transparency, physics control, data provenance — and data sovereignty.