Spherecast vs GeologicAI

Feature sets don't overlap at all. GeologicAI centers on physical data capture and geological interpretation: RGB, XRF, hyperspectral and LiDAR sensors fired in a single integrated pass over drill core, plus LIBS-based analysis for rare-earth and light elements following the Lumo Analytics acquisition. Outputs are AI-assisted logs on a cloud-connected Digital Core Table, Resource Knowledge Systems for multi-sensor analysis, geostatistics and uncertainty quantification, and a Drill Hole Optimizer for planning drill programs, with RMSP and mining-software integration. It reports 4x faster logging, sub-48-hour core-to-data turnaround, and +400% project acceleration — all measured in a mining cycle, not a sprint. Spherecast operates in a completely different layer: demand forecasting, consensus S&OP with human override, multi-echelon inventory optimization, PO and transfer-order recommendations, supplier-email parsing, AI agents that action supply exceptions, plain-English what-if scenarios, real-time visibility by SKU/location/raw material, an ERP and warehouse connector set, a sandbox for real-data testing, and one-click approval that writes back into the ERP. GeologicAI's hardest dependency is a scanning rig and senior geology staff to QA logs; Spherecast's hardest dependency is an existing ERP and structured data. One turns rock into defensible models; the other turns forecasts into executed orders.

Both list pricing as contact — and that's where the similarity ends. GeologicAI is priced and scoped like a capital project: a hardware-and-services rollout (scanning rig, Digital Core Table deployment, Resource Knowledge Systems, plus modeling support) that a mine site absorbs as a program with a pilot and a rollout plan, not a software signup. Its own positioning excludes junior explorers with too few holes to justify an integrated scanning program, which tells you the deal size sits well above typical SaaS. Spherecast is a software subscription with a defined onboarding path: connect your ERP or data warehouse, run the sandbox environment with real data, then go live in two to three weeks. That timeline only makes sense on subscription economics — no hardware, no site visits, no multi-month pilot capex. The practical takeaway: GeologicAI's cost has to be justified against a multi-million-dollar drill program where a misread intercept is worse than the fee; Spherecast's cost has to be justified against planner headcount, stockouts, and excess inventory across warehouses and co-manufacturers. Because neither publishes a number, the only real evaluation each supports is its own pilot — a scanning program versus a sandbox trial. They don't share a budget category, so no price comparison is meaningful.