01
Predictive maintenance triage
An agent reads vibration, temperature, and runtime data alongside technician notes and drafts a prioritized maintenance recommendation — with a reviewer confirming before a work order is issued.
Agentic AI in mining
Agents that work inside remote-site operations .
We build agents that read sensor and inspection data, draft maintenance and compliance recommendations, and coordinate supply chains across remote sites — with the audit trail a safety or operations team will actually sign off on.
Trusted by operators
-
+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
Read case study -
Supply chain intelligence agents reducing manual coordination overhead.
-
37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
-
AI agent implementation for a global automotive enterprise.
-
2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
Read case study
Why agentic AI
Mining operations run on judgment calls that never get systematized
Maintenance decisions, safety inspections and dispatch calls at remote sites depend on data scattered across historians, CMMS logs, technician notes and paper checklists. The sensors are usually fine, and so is the model. What no site safety or operations lead will trust with equipment and people on the line is a chatbot bolted onto that data with no evaluation harness, no sourced reasoning and no escalation path.
every recommendation traces to its source data
Use cases
Where agents earn trust in mining operations
Workflows that already produce a paper trail and already route through a human reviewer.
02
Safety and compliance reporting
Inspection checklists, near-miss reports, and environmental readings get consolidated into a structured, auditable report an agent drafts and a compliance officer reviews before filing.
03
Equipment failure diagnostics
When a fault is flagged, an agent correlates historian data across similar equipment and past incidents to draft a likely root cause — cutting the diagnostic search before a technician is dispatched.
04
Supply chain and spare-parts coordination
An agent tracks lead times, site inventory, and dispatch schedules across remote locations and drafts reorder or reallocation recommendations before a shortage stalls a maintenance window.
Delivery path
From workflow audit to a production mining agent
TriStorm keeps operational risk and engineering aligned — integration and safety questions surfaced before full build commitment.
Map systems and workflow
We audit the target process (maintenance backlog, shift reporting, or compliance pack) along with historian, EAM and sensor boundaries, ranking candidates by impact and integration risk.
- Systems & workflow audit
- Data quality review
- Prioritised use case
Build, test and validate
We implement against real operational data shapes, with an evaluation suite scored against your own thresholds before any output reaches an engineer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout with monitoring, full audit logging, and a structured handoff so your operations team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own maintenance and compliance workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
What the mechanism delivers
Multi-system coordination, measured in production
These numbers come from shipped agentic AI engagements outside mining — Synera (engineering software), Mixam (print on demand) and Schmitt-Thompson (healthcare). They are here because they measure what a mining workflow depends on: multi-step validation instead of a single pass, orchestration across many tools, and an extraction layer that stops rather than guesses. Every figure links to the case study behind it.
Agents do not adjust a plant setpoint or sign off a maintenance order. They gather, cross-check and draft what sits before an engineer's decision. Every step is logged for review.
2 hrs → 3 min
to generate a validated workflow
Not a mining deployment — Engineers moved from hours of tedious setup to minutes, through multi-step validation rather than a single generation pass.
95.4%
Success rate in workflow results
Not a mining deployment — A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
Not a mining deployment — Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production agentic deployments
We have not yet shipped a production agent inside a mining or heavy-industry operator. These are the closest available references — the same multi-step validation and multi-tool orchestration, shipped in engineering software, print on demand and healthcare.
FAQ
Agentic AI in mining and resources, answered
Where does agentic AI actually fit into a mining operation?
In workflows that already generate structured records and already require a human sign-off — maintenance work orders, inspection logs, permit and environmental reporting, procurement and dispatch coordination. We do not deploy agents to make unsupervised control decisions on physical equipment. We deploy them to read sensor and inspection data, draft the recommendation, and route it to the engineer or supervisor who signs off.
How is this different from the SCADA and fleet-management automation we already run?
SCADA and fleet-management systems execute fixed control logic against fixed thresholds. An agent reasons across sources a control system was never built to read (maintenance history, technician notes, procurement lead times, weather and permit constraints) and drafts a decision with its reasoning attached, then escalates when it is not confident. It sits alongside your control systems, not inside them.
Which mining workflows see agents first?
Ones with a clear paper trail already required and a human already reviewing the output: predictive maintenance triage, equipment failure diagnostics, safety and compliance inspection reporting, and supply chain or spare-parts coordination across remote sites. These are judgment-and-documentation workflows, not control-loop automation.
Can an agent actually help with equipment maintenance planning?
Yes — that is the closest-fit workflow. An agent reads sensor streams, work-order history, and technician notes, then drafts a prioritized maintenance recommendation with its reasoning shown. It is the same orchestration pattern behind Synera's engineering agent platform, where generating one validated workflow went from about two hours to about three minutes — reading structured and unstructured inputs and producing a reviewable output, not a black-box verdict.
How do you handle safety-critical decisions and regulatory audit requirements?
The agent never has final authority over a safety-relevant action. Every recommendation carries its source data and reasoning, is logged, and is routed to a qualified reviewer before anything happens on site. That audit trail is the same infrastructure a mine safety or environmental regulator would want to see in an inspection.
What happens when the agent is not confident in a recommendation?
It escalates rather than guesses. Confidence thresholds and data-coverage gaps route the case to a human reviewer with the full reasoning chain attached, the same escalation pattern we build into every regulated deployment.
Can this integrate with the equipment sensors and ERP systems we already run, across remote sites?
Yes, through existing APIs and data feeds — historian databases, CMMS, ERP, procurement systems. We do not ask you to replace SCADA or fleet-management infrastructure to add an agent layer on top of it, and we design for intermittent connectivity at remote or offshore sites from day one.
What is the typical path from a first conversation to a working system?
A scoped Proof of Value (one workflow, real operational data, a working agent) lands in about three weeks under our TriStorm methodology. Production rollout with monitoring and an operator handoff follows the same phased approach we use across every industry we serve.
Start with one workflow
Map one maintenance or compliance workflow worth automating
A 30-minute call identifies your site data sources, safety constraints, and a realistic path to a working agent your operations team will trust.