Drill-hole & assay analysis
Analysis across drill holes, assays and geological intervals.
- Assay and interval review
- Anomaly and outlier detection
- Cross-hole consistency checks
Confidence in the ground.
Analysis of exploration and geological data — drill holes, assays and models — to raise confidence in resource decisions.
Exploration decisions rest on data that is expensive, sparse and easy to misread.
Drill programmes are costly and irreversible. Every assay, collar and interval carries weight far beyond its size, and a quiet QA/QC failure can propagate into a resource estimate that is hard to unwind.
The industry runs on standards — JORC, NI 43-101 — because confidence has to be defensible to investors and regulators, not just internally plausible.
Our view: geological confidence is a data problem before it is a modelling problem. If every assay can be traced through QA/QC into the model, the resource decision stands on firmer ground.
What we do
CapabilitiesGeological data integration
Bring disparate exploration data into one analysable, traceable record.
- Collar, survey and assay integration
- Geological model inputs
- Data quality and provenance
Exploration confidence
Raise confidence in exploration and resource decisions.
- QA/QC traceability
- Evidence for resource estimation
- Decision support for technical teams
Provenance & audit trail
Every result traced to its source sample and the model decision behind it.
- Sample-to-result lineage
- Model rationale
- Exportable evidence
How we deliver
01
Understand the ground
We learn the deposit, the programme, the standards and the data you hold.
02
Integrate & analyse
We bring the data together, analyse it and wire the model into your workflow.
03
Prove & support
We document QA/QC and provenance, and support the decision.
What confidence looks like
Outcomes- Assays traceable through QA/QC
- Exploration decisions backed by evidence
- Data quality issues surfaced early
- Records defensible to investors and regulators
Bring us a drill programme.
We will analyse a sample of your exploration data and show the provenance behind each result — before you commit to anything.
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