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Platform

The platform behind auditable predictive intelligence

Integrity converts raw industrial telemetry into structured evidence, confidence-scored decisions, and operationally defensible outputs — in a single stack, with provenance preserved end to end.

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Three-layer architecture
Instrumentation, fusion, and evidence-based AI in one stack
Live telemetry scroll
Raw signal to defensible action, with provenance at every hop
Defensible by design
Replayable decision records, confidence scores, audit trails
Why architecture matters

Better AI alone cannot fix bad inputs.

The fundamental problem in legacy predictive systems is incomplete and fragmented data — not insufficient model complexity.

  1. 01

    Incomplete data

    Legacy systems poll at 5–15 minute intervals and miss the transient events that precede real failures. The signal is in the gaps.

  2. 02

    Fragmented context

    Power, cooling, vibration, and operational data live in disconnected silos. No single system maps the interdependencies between them.

  3. 03

    False alarm fatigue

    Without sufficient context or confidence scoring, systems generate 40–60% false-positive rates. Operators learn to ignore alerts entirely.

Three-layer architecture

Each layer addresses a specific failure mode in legacy predictive monitoring systems.

Layer 01

Complete Instrumentation

Why it matters

Better models cannot compensate for missing data. The sensing foundation determines the ceiling of every downstream analysis.

What legacy systems miss

Built for 10–20kW racks with 5–15 minute polling. Misses the transient events that precede real failures.

What Integrity adds

1kHz power monitoring, advanced mechanical sensing, vibration analysis, and environmental gradient detection.

Layer 01

Deploy custom sensors, augment existing infrastructure with higher-resolution capabilities, or ingest from what the customer already has.

Layer 02

Intelligent Data Fusion

Why it matters

Isolated data streams cannot reveal cross-system failure modes. Cascade failures propagate through relationships disconnected systems cannot see.

What legacy systems miss

Power, cooling, vibration, and operational data live in disconnected silos with no cross-correlation.

What Integrity adds

Relationship-aware modeling that correlates across modalities and identifies how one issue cascades into larger events.

Layer 02

Normalize and enrich telemetry into a hierarchical model of sites, systems, assets, and sensors with mapped interdependencies.

Layer 03

Evidence-Based AI

Why it matters

Operators need to trust the system before they act on it. Black-box predictions create alert fatigue, not operational improvement.

What legacy systems miss

Opaque model outputs with no explanation of reasoning, no confidence quantification, and no audit trail.

What Integrity adds

Every recommendation includes the raw data, analysis chain, confidence score, and decision record for full traceability.

Layer 03

Produce auditable intelligence backed by structured evidence packs, confidence scoring, provenance trails, and durable decision records.

From telemetry to decision

Signal to defensible action — without losing the evidence.

Every stage preserves provenance so any recommendation can be replayed, reviewed, and audited by an operator, a regulator, or an insurer.

RAW
Ingestion
01

Telemetry Ingestion

Protocol-agnostic data collection from any source, gateway, or interface.

NORM
Normalization
02

Normalization & Enrichment

Raw signals are cleaned, timestamped, and enriched with operational context.

MODEL
Modeling
03

Asset / System Modeling

Data mapped into hierarchical relationships between sites, systems, assets, and sensors.

EXEC
Execution
04

Capability Execution

Analytics and ML models run against the normalized, relationship-aware data model.

EVID
Evidence
05

Evidence Packs

Structured bundles of raw data, analysis chain, and reasoning behind each finding.

SUFF
Sufficiency
06

Sufficiency Evaluation

Confidence scoring determines whether evidence meets the threshold for action.

DEC
Decision
07

Decision Record

Durable, auditable record of what was found, what was recommended, and why.

CLOSE
Closure
08

Outcome Closure

Operator action is tracked back to the decision record for continuous learning.

Evidence and auditability

Every output is backed by structured evidence — not a black-box prediction.

01

Auditable evidence artifacts

Every alert includes the raw data, feature extractions, model outputs, and reasoning chain that produced it.

02

Confidence-scored outputs

Quantified certainty levels tell operators when to act immediately and when to monitor — reducing false alarm fatigue.

03

Provenance trails

Complete data lineage from sensor measurement through normalization, enrichment, and model inference to final recommendation.

04

Replayable decision records

Any past decision can be reconstructed and reviewed with the exact data and model state that existed at the time.

05

Operationally defensible intelligence

Outputs are structured for compliance reporting, insurance documentation, and mathematically grounded SLA adherence.

Fast and slow reasoning

Two processing layers work in concert — edge-speed response for immediate threats, and deeper contextual analysis for broader patterns.

Fast layer

Edge response

  • Sub-second anomaly detection at the edge
  • Pre-trained models for known failure signatures
  • Low-latency alerts for immediate operator response
  • Runs on constrained hardware near the asset
Slow layer

Contextual reasoning

  • Broader cross-system pattern analysis
  • Historical correlation and trend detection
  • Model refinement from validated outcomes
  • Evidence synthesis across multiple time horizons
Validated outputs from both layers are preserved as durable evidence artifacts — creating a continuous feedback loop that improves accuracy over time.
Continuous evidence loop
<1s
Edge inference
24/7
Contextual synthesis
100%
Evidence retained
Platform at a glance

Engineered for
critical uptime

<2 sec

Edge inference response

1 kHz

Power monitoring resolution

100%

Evidence coverage per decision

Edge ↔ Cloud

Deployment topologies supported

Integration & deployment

Integrity adapts to your environment.

No full rip-and-replace required. Start where the signal matters most and extend from there.

Custom sensor deployments

Purpose-built Integrity sensor stacks for environments requiring new instrumentation.

BMS / DCIM augmentation

Layer higher-resolution monitoring onto existing building management and data center infrastructure.

API and gateway ingestion

Ingest from REST APIs, OPC-UA, MQTT, Kafka, batch files, field gateways, and industrial protocols.

Flexible deployment topology

Edge, cloud, hybrid, or air-gapped — the platform adapts to your connectivity and compliance constraints.

Book a technical deep-dive

See how the platform maps to your specific operational environment.

Schedule a discussion