Evidence infrastructure for physical systems
Overview
MIS builds long-term machine memory through continuous vibration and acoustic observation. Every deployed device generates proprietary evidence that no one else has: insert-only records that accumulate value with every operational cycle, every maintenance event, every environmental change. This dataset cannot be replicated after the fact.
Baselines earned from each machine's own past. Assessments traceable to specific evidence. Knowledge that progresses from physics-based assumptions, to patterns observed in the field, to outcomes confirmed through real interventions. As MIS expands to cover broader material systems, including infrastructure, energy, and built environment, it will evolve to Material Intelligence System.
Core Capabilities
Continuous Observation
Vibration and acoustic sensing via dedicated hardware. Insert-only evidence accumulation across the full operational lifetime of every machine.
Long-Term Memory
Complete operational history. Baselines built from each system's own history. Evidence that compounds across maintenance cycles, ownership changes, and team turnover.
Deviation Detection
Identifies meaningful changes against earned baselines. Distinguishes signal from noise using the machine's own history, not universal thresholds.
Traceable Assessment
Every claim maps to specific evidence IDs, feature computations, and time windows. Documented analysis with confidence levels based on how the knowledge was earned.
Current Domains
Commercial Real Estate
Building systems, HVAC equipment, and mechanical infrastructure where assets operate across decades and ownership transitions.
Manufacturing
Rotating equipment, production machinery, and industrial systems where operational history determines maintenance decisions and failure prevention.
Field Operations
Distributed infrastructure where inspection continuity is routinely lost, crews change, and historical context disappears with every team transition.
Utilities & Energy
Grid infrastructure, transformers, pipelines, and transmission systems that outlive every operating team and where failure costs are measured in millions.
First 30 Days
Initial deployment focuses on establishing the evidence foundation:
- 1Assessment of existing infrastructure and available signal sources
- 2Hardware deployment and integration with operational systems
- 3Historical data migration and establishment of temporal continuity
- 4Baseline characterization from the system's own operating patterns
- 5Operator training on evidence interpretation and system operation
- 6System enters continuous observation mode with earned baseline established
Capability Checklist
- Insert-only evidence accumulation from vibration and acoustic sensors
- Baselines earned from each machine's own history
- Long-term memory preserved across team and ownership changes
- Deviation detection against earned baselines, not universal thresholds
- Traceable claims with documented evidence chains
- Complete audit trail of every observation and assessment
- Knowledge maturity tracking: from physics-based assumptions, to field observations, to confirmed outcomes
- Device-system identity separation (devices move, system history persists)
- Structured documentation with documented analysis
- Operator access to full evidence history and contextual assessments
Deployment Options
MIS deploys as managed infrastructure within customer environments or as a hosted service, depending on data governance requirements. Implementation includes hardware deployment, system integration, historical data migration, and operator training.
Capabilities Earned Through Evidence
MIS evolves as evidence accumulates. Advanced capabilities unlock only when sufficient evidence, validation, and operational history justify them. Nothing is assumed. Everything is earned.
Predictive Assessment
Failure prediction only where sufficient historical evidence exists, degradation trajectories are observable from earned baselines, and operating context is stable enough to model with documented confidence.
Maintenance Guidance
Intervention recommendations within documented confidence bounds, based on prior outcomes linked to evidence, comparable histories, and interpretable documented analysis. Not prescriptive automation.
Safe Envelope Enforcement
Operational constraint enforcement only after long-term validation, stable decision boundaries, and human-reviewed outcomes. MIS prioritizes evidence-based decision support before any automation.
Execution Domains
The same evidence architecture applies to any physical system with measurable signals and clear economic value. We expand sequentially. Each domain builds on the evidence foundation established before it.
Industrial & Infrastructure
Factories, mining operations, power plants, water systems, logistics infrastructure. MIS core, where the evidence architecture is being proven and refined.
Built Environment
Building health monitoring, HVAC intelligence, structural monitoring. Natural extension of MIS. Buildings are machines that operate on longer timescales.
Energy & Resources
Grid stability, renewable system monitoring, consumption optimization, water resource systems. High-value domain where evidence integrity has regulatory and safety implications.
Environmental Systems
Agricultural optimization, soil monitoring, irrigation intelligence, micro-climate sensing. Strong sensor-based domain with clear economic value and scalable deployment.
Logistics & Supply Chain
Cold chain monitoring, warehouse dynamics, transport system intelligence, asset flow tracking. Requires cross-system integration that depends on earlier infrastructure evidence.
Interested in evidence infrastructure?
We work with infrastructure operators and organizations where physical evidence determines outcomes. Contact us to discuss whether Aletheon's approach fits your domain.