80% Less Manual Data Gathering: How a Global Mining Advisory Firm Freed Its Senior Analysts

A global mining advisory firm was spending its senior analysts' hours on manual data entry instead of analysis, because the only path from mining documents to the firm's data system ran through human extraction.

The firm serves mining companies worldwide with analytics, benchmarking, and strategic guidance. Its platform processes production data across hundreds of mines and commodities. The analysts running this operation are experienced professionals with deep domain knowledge.

They were spending most of their time on data entry.

Every day, analysts read NI 43-101 reports, annual production filings, capital market feeds, and regulatory updates. They extracted numbers by hand, normalized units across different reporting standards, and keyed everything into the firm's data system. Then they did it again the next day.

Accurate work. Necessary work. But not the work you hire senior mining analysts to do. As the business grew, more documents needed processing, and more analyst time went to extraction instead of analysis. The team had become a bottleneck, not because they were slow, but because manual extraction consumed their hours.

A production AI extraction system at this document depth is not a model problem. It is an engineering problem with a model embedded in it.

Tecknoworks built that production layer. The agent reads mining documents (NI 43-101 reports, annual production filings, capital market feeds, regulatory updates), extracts values with source citations down to the page and section, and standardizes units across 500+ mapping rules. It pre-fills the firm's data entry interface with confidence scores, so analysts focus only on exceptions.

Every extracted field carries a full audit trail: source document, page, section, extraction date. A three-tier classification routes analyst attention to where human judgment matters: auto-approve when confidence is high and the value is unambiguous, review when the agent is uncertain or the source citation needs human eyes, reject when the document does not support a confident extraction. The senior consultants gained direct read access to the data without going through the analyst team for every query.

Two weeks from first line of code to live in the client's production environment. Not a pilot, not a demo, not a sandbox. The agent ran against real mining documents from day one of production deployment, and the analyst team used it for daily work the same week.

The production layer is where the work actually sat. The model handled extraction and unit standardization. The platform handled everything that decides whether a model in production holds: confidence scoring on every field, source citation back to the page and section, unit-mapping enforcement across reporting standards, the three-tier review queue, the analyst workflow integration, the audit trail. The system around the model carries more weight than the model itself.

Before this engagement, the senior analysts were the pipeline. They read documents, extracted numbers, normalized units, and keyed everything into the platform.

After it, they became the quality layer. The agent handles extraction and standardization. The analysts handle judgment calls, exceptions, and the domain expertise they were hired for. The team that used to be the bottleneck became the part of the workflow where human expertise creates value.