Our Agentic Twin is built as a knowledge system grounded in physics & verified against data
Trusted decisions come from more than AI alone: every model is grounded in engineer-certified knowledge, and every agent response passes through deterministic verification to reduce hallucinations before it reaches the user.
- P&IDs, PFDs & architecture
- OEM manuals, RBD / FMEA
- HAZOP & Cause & Effect
- Historian tags & time series
- Work orders & inspection logs
- Maintenance & failure events
- Inventory logs
- Textbooks & industry documents
- Expert knowledge
- Energy, mass & momentum laws
- Sensor dynamics
- Vibration analysis
- Physics models
- Form
- Function
- Failure
- Behavioral Models
- Structural Models
- Anomaly Detection
- Remaining Useful Life
- Performance Optimization
- Process Simulation
- Asset Library
- Context
- SME Knowledge
- Reasoning Models
- Physics & Process Models
- SME CERTIFIED
- DETERMINISTIC CHECKS
- FULL PROVENANCE
- NO HALLUCINATION
Every source, one library.
P&IDs, OEM manuals, historian tags, work orders and inspection logs, plus the asset-class material around them.
Everything is normalized, deduplicated, linked to the asset it describes and kept with its provenance. Physics comes in alongside it, so the workflows that follow have something to reason against.
The substrate every agent runs on.
One knowledge system, one skill library, one harness. Agents inherit retrieval, tools and guardrails instead of each being built from scratch.
Retrieval carries provenance. Skills are reusable reasoning and calculation routines per asset class. The harness orchestrates them and applies deterministic verification, so an unverifiable claim never reaches an engineer.
Agents build the twin, step by step.
Four workflows in sequence: the foundational asset model from documents and SME input, then static and dynamic enrichment against real operating behavior.
Static enrichment extends the asset model into FMEA++, subsystem boundaries and base physics. Dynamic enrichment learns from real operations, turning trips, alarms and repairs into failure signatures. SMEs certify each step before it enters the twin.
Agents build, backtest and version the models.
Feature generation, training against certified labels and per-asset backtests. Nothing is released without SME evaluation.
Reliability models cover anomaly detection, remaining life and health scoring; performance models cover expected versus actual, loss attribution and what-if. Precision, recall and lead time are reported per asset.
The certified output of the twin.
Three model stores that everything downstream reasons against: what the asset is, how it behaves numerically, and what the agents know.
Asset models describe form, function and failure. ML models cover anomaly detection, RUL, performance optimization and simulation. The agent substrate holds the asset library, context, SME knowledge and physics models. Every entry is certified, versioned and traceable.
Agents that act on the twin.
Diagnostics, performance, root cause, false-positive filtering and operations. Each one reasons against the same certified twin, so every answer carries evidence.
Alerts arrive with causes ranked by confidence, ruled-out candidates shown and evidence attached. Each case ends with an action and a drafted work order; the SME verdict is written back into the twin.
Nothing reaches an engineer unverified.
Trust is a layer of the platform, not a claim about it. Every model is certified by engineers, and every agent output is checked against the twin before it is shown.
SME certification sits at the end of each workflow. The harness then applies deterministic verification: a claim that cannot be checked against the twin is discarded rather than surfaced. What does reach the user carries provenance, and the SME verdict is written back.