INDUSTRY · MINING & HEAVY INDUSTRY

What AI agents can do inside a mining operation

Cross-experiment knowledge synthesis, maintenance support, a plant-operations copilot over your process data. This is what an AI agent can do inside a mining operation, air-gap capable, with your technical archive never leaving the site.

Inside your own systems Air-gap capable Technical archive stays on site Every action recorded
These are workflows an AI agent can run. The exact scope is defined with your team
WHAT AI AGENTS CAN DO

Three workflows, from quick win to company-wide

Start with one workflow that pays for itself, then expand on the same private, governed platform. No new vendor cycle each time.

01 · START · KNOWLEDGE ● live today

Knowledge synthesis across experiments

Connect engineers to the archive of past experiments and studies, surfacing prior findings, parameters and outcomes so teams reuse relevant work instead of repeating expensive studies.

search archivesummarizecompare runscite sourcerecommend
02 · EXPAND · MAINTENANCE

Maintenance and reliability assistant

Over failure history, equipment manuals and CMMS work orders: on a failure, retrieve similar cases, the probable root cause and the procedure, and prepare the work order.

detect failurefind similarroot causedraft work orderapprove
03 · SCALE · OPERATIONS

Plant-operations copilot over process data

Connected to the historian, LIMS and ERP, answer operational questions in natural language, detect deviations, and prepare shift and environmental-compliance reports. Air-gapped.

ask process datadetect deviationshift reportenvironmental report
LIVE

The knowledge-synthesis workflow above runs in production today inside a Chilean industrial-mining technology company. The other two are illustrative of the same capability. The exact scope is defined with your team.

HOW FOGOARAI DOES IT

The same engine behind every workflow

In mining, expensive knowledge sits in past studies and process data. Surfacing it (and querying it) is where an agent pays for itself.

RAG · KNOWLEDGE · LEADS HERE

RAG · knowledge synthesis

Agents read your internal archive (documents, manuals, regulations, prior cases) and answer with citations to the source. Private embeddings, served locally inside your network.

DATABASES · NL→SQL

Database reads · NL→SQL

Ask your databases in plain language. A fine-tuned local model turns the question into validated SQL. No BI ticket, and no data leaves the network.

SMALL MODELS

Agents trained on small models

Small specialized models handle the repetitive work (classify, extract, validate, summarize) at a fraction of frontier cost, escalating to frontier models only when the task needs it.

GOVERNANCE

Governance and compliance

Every action is tied to a real user, gated by policy, approved where it matters, and recorded in an immutable audit trail your security and compliance teams can review.

See how the engine works in depth
GOVERNANCE

Built to pass your security review

Built for air-gapped operations, with full traceability for safety (Sernageomin) and environmental (SMA) review.

G.01Role-based access controlACTIVE
G.02Tool allowlists and action boundariesACTIVE
G.03Human-in-the-loop approval flowsACTIVE
G.04Full audit logs for model + tool callsACTIVE
G.05Structured output validationACTIVE
G.06Sensitive data redactionACTIVE
G.07Versioned prompts, models, policies, workflowsACTIVE
G.08Evaluation gates before production deploymentACTIVE
G.09Continuous monitoring after deploymentACTIVE
See the full governance model
GET STARTED

Put an AI agent to work in your operation

Start with knowledge synthesis over your technical archive, then expand into maintenance and a process-data copilot. All air-gapped, all inside the site.

Goes straight to the founder. No sales team
PILOT WORKFLOWone workflow · scoped together
DURATION90 days
DEPLOYMENTyour VPC or on-prem

DATAstays inside your network
APPROVALSyour people, your rules
SUCCESS METRICcost saved · quality held

STATUS● READY TO SCOPE