Methodology
How results are produced and governed
DrillQC keeps a clear line between deterministic engineering checks and advisory AI, and keeps the evidence that makes a result reviewable.
Deterministic QC versus advisory AI
Engineering results come from deterministic checks: the same inputs produce the same output every time. AI is used only to explain and summarize results and to help navigate them. AI never produces or changes an engineering result.
Versioned inputs and outputs
Inputs and outputs are versioned so that a result is tied to the exact data that produced it. A result can be located, reviewed, and compared to earlier runs.
Reproducibility
A check is reproducible: re-running a versioned dataset reproduces the same result. Reproducibility is what makes a result reviewable rather than a one-off.
Dataset governance
Public demonstrations use controlled synthetic datasets only. Datasets move forward through explicit review rather than being changed silently to match an expected answer.
Human approval gates
Qualified people approve datasets and decisions. Approval is a human act; software surfaces the evidence, but it does not grant approval on its own.
Evidence retention
The evidence behind a result — its inputs, checks, and warnings — is retained so the result can be reviewed later, not just at the moment it was produced.
Why unresolved calculations are withheld
When a calculation is still under independent engineering review, its output is not presented as a production result. Withholding an unresolved calculation is deliberate: it prevents an unreviewed number from being treated as engineering truth.