Fast
Fast-layer edge response
Pre-trained models execute locally for sub-second anomaly detection and response. No cloud dependency in the critical path.
Industries / Defense
Evidence-based decision intelligence for edge environments
Constrained connectivity, high-consequence decisions, and low-latency requirements demand intelligence systems built for trust and traceability.
Edge-only capable
Runs in denied and air-gapped environments.
Confidence-scored
Every decision includes quantified certainty.
Auditable
Preserved evidence, replayable decision records.
The gap today
Operating context
Edge environments impose constraints that most predictive intelligence platforms were never designed to handle.
- 01
Constrained connectivity
Intermittent, bandwidth-limited, or denied network environments where cloud-dependent systems fail.
- 02
High-consequence decisions
Operational decisions with significant impact. False positives and false negatives both carry serious consequences.
- 03
Low-latency requirements
Time-critical response windows where decisions cannot wait for cloud round-trips or human review chains.
- 04
Trust and traceability
Every decision must be explainable, auditable, and reproducible. Operators need to understand why the system recommended an action.
Why it matters
Edge-native
Pre-trained models run locally for sub-second response. Slower contextual reasoning runs when connectivity allows. Every decision ships with evidence — by default, not as an afterthought.
How we deploy
Architecture fit
Integrity's dual-layer processing architecture is designed for environments where connectivity is intermittent and decisions cannot wait.
Deep
Slower contextual learning
Deeper analysis runs when connectivity allows, refining models and building broader situational awareness from accumulated evidence.
Score
Confidence-scored outputs
Every recommendation includes quantified certainty. Operators know the reliability of each assessment before acting.
Audit
Preserved evidence trails
All decisions, model outputs, and supporting data are preserved as durable artifacts — available for review, replay, and audit.
Deploy
Adaptable topology
Edge-only, edge-cloud hybrid, or air-gapped configurations. The architecture adapts to connectivity and classification constraints.
What we see
Why this matters
Edge decision support requires more than prediction — it requires explainable, confidence-scored intelligence operators can act on immediately.
Onboard / edge decision support
Intelligence runs where the asset operates — not in a distant data center. Decisions are made at the point of action.
Multi-system coordination
Correlate across mechanical, electrical, environmental, and operational signals for comprehensive situational awareness.
Explainable operational intelligence
Every recommendation is backed by structured evidence. Operators understand what was detected, why it matters, and the confidence assigned.
Outcomes
Trust and governance, by design
What high-consequence environments demand — built into the architecture, not bolted on.
Traceability
Sensor → model → recommendation
Decision records
Durable, structured, replayable
Outcome validation
Actions tracked back to decisions
Governed evolution
Model updates with controlled drift
Strategic context
John Vollmer — Advisor
Boeing Space Program leadership, large-scale systems engineering, and government contracts expertise. Aerospace-grade operational credibility.
Discuss mission-specific architecture fit
Every operational environment has unique constraints. We'll map our architecture to your specific requirements.