EQ Syntropy™

How It Works: From Signal to Insight

1. Semantic Embedding of Raw Power Data

Using domain-aware processing pipelines, your facility's waveform data (up to 40 MB per minute) undergoes continuous power quality feature extraction, signal classification, and event detection. The resulting insights are semantically enriched and converted into vector embeddings for search and retrieval.

2. Contextualization

Event data is enriched with operational metadata such as equipment tags, facility layout, system topology, weather feeds, and historical patterns. Structured and unstructured information are co-located in a unified memory layer that combines a vector database and a data lake.

3. Adaptive Retrieval and Query Routing

• “What caused last Thursday’s voltage sag?”
• “Have we seen this harmonic pattern before?”

EQ Syntropy interprets each query and routes it through the appropriate pipeline: statistical analysis, waveform-based processing, rule-based checks, or generative explanation. Queries trigger report generation, visualization, anomaly investigation, or root-cause workflows.

4. AI Orchestration Layer

An orchestration layer coordinates specialized agents that:

• Synthesize and explain results in human language
• Reference standards and past events
• Recommend next steps based on equipment and process context
• Learn from feedback and user input

Access and Interfaces

Insights are accessible through:

• EQ Sight™: A manager- and engineer-focused interface providing situational awareness with context-aware, ask-anything AI assistance and seamless handoff into deeper investigation workflows
• RESTful API: Direct programmatic access for custom dashboards, SCADA integration, and workflow automation
• Data Lake: Parquet-based historical storage for compliance, analytics, and machine learning workflows

Engineers, operators, or AI agents can ask natural-language questions like:

The Three Substrates of Grounded Intelligence

EQ Syntropy is built on three tightly coupled substrates:

• Signal substrate: Continuous waveform memory and event history captured at full fidelity, not compressed into RMS snapshots
• Physics substrate: Deterministic models, constraints, and invariants that define what “physically valid” behavior looks like for your system
• Semantic substrate: LLMs and domain-aware agents that interpret context, language, and operator intent using the evidence from signals and physics

Trusted intelligence emerges from all three working together. Language models alone are not enough for critical power systems.

Why It Matters

Power quality problems are increasing in frequency and impact, while traditional monitoring remains coarse and event-driven. EQ Syntropy brings clarity, precision, and actionable context to what used to be noise.

• Built on complete data, not shallow metrics. EQ Syntropy avoids lossy abstractions such as RMS-only views. Every analysis starts from continuous, lossless waveform data.
• Smarter than threshold alerts. Instead of flooding users with false positives or cryptic metrics, the system focuses on the "what, why, and what next."
• Designed for industrial scale. Handles real-time streaming, on-premises compute, and hybrid edge-cloud coordination with full facility recall.

Key Capabilities

EQ Syntropy provides the intelligence layer your facility has been missing—built on trusted signals and designed for action.

Physics-Informed Insight

Anomaly detection and event tagging grounded in first-principles and real-world electrical behavior.

Explainable AI

Retrieval-augmented diagnostics, root-cause analysis, and transparent reporting that emphasize understanding, not just alerts.

Predictive Intelligence

Health scoring and failure forecasting based on equipment-specific electrical signatures and operating history.

Multi-Domain Signal Analysis

Correlation across time, frequency, and sequence components to reveal subtle or intermittent phenomena.

Context-Driven Customization

Incorporate facility manuals, equipment specifications, and operational procedures so EQ Syntropy aligns its terminology, workflows, and analysis with your environment.

Contextual Search

Vector-based retrieval across historical disturbances and operational knowledge, allowing engineers to find similar events, correlate patterns, and resolve issues faster.

Flexible Integration

Works with EQ Wave™, SCADA systems, third-party meters, and environmental data feeds.

Structured, Scalable Output

RESTful API access and Parquet-based data lake for clean integration with your existing tools and teams.

Adaptive Architecture

• Edge-first by design: Millisecond-class latency, enabled for air-gapped installation.‍
• Cloud-coordinated: Secure cross-facility benchmarking, model updates, and fleet-level insight.
  
• Multi-modal and scalable: Built to handle waveform data, events, and documents in a single, governed environment.

Deployment Options

Hardware Options

• Preconfigured industrial gateway or deployment on your own hardware
• AI at the industrial edge using NVIDIA AGX Orin or Thor-class platforms
• AI on discrete GPUs for higher-throughput or centralized analytics
• Hybrid edge and cloud deployments for large-scale coordination

Software Licensing

• Core platform, with or without advanced AI features
• Industry-specific modules for specialized applications
• Optional models tuned on your data and operating environment
• Enterprise features for multi-site deployment and governance
  

Not Just AI — Amplified Engineering Judgment

EQ Syntropy is not designed to replace power quality experts. It is built to amplify them by surfacing relevant context, automating routine analysis, and supporting faster, more confident decisions.

Whether you are managing a semiconductor fab, a defense microgrid, or a medical imaging facility, EQ Syntropy helps turn raw waveform data into knowledge and knowledge into action.