Intelligence Systems

Purpose

A coordinated ecosystem of specialized agents that operate in coordinated pipelines: Acquisition → Validation → Enrichment → Classification → Storage. Each agent is narrow, replaceable, and failure-tolerant.

How It Works

TAV, Eve, OpenClaw, and ZeroClaw agents operate in coordinated pipelines. Data flows from acquisition through validation, enrichment, classification, and storage. Each agent has a specific, narrow purpose and can be replaced without affecting the entire system. Watchdog processes monitor health and restart failed components automatically.

Architecture

• Multi-agent orchestration with dedicated responsibilities
• Cron scheduling for periodic tasks
• Watchdog monitoring for self-healing
• SQLite WAL + PostgreSQL dual-layer storage
• Docker-containerized services on Linux VPS

Purpose

Recursive spatial partitioning system that maps commercial entities, real estate activity, geospatial patterns, and business density shifts. Does not scrape randomly — maps territories methodically.

How It Works

Uses recursive spatial partitioning to systematically map territories. TAV-APEX evolution adds subject-level harvesting capabilities. Data is stored in append-only JSONL format with deduplication engines ensuring clean datasets.

Architecture

• Distributed scanning across territory partitions
• JSONL append-only ingestion
• Deduplication engine for clean datasets
• Spatial partitioning for systematic coverage

Purpose

Aggregates micro-signals across sector-level data, scoring signals by velocity and novelty. Flags momentum shifts and structural inflection points before they become visible to traditional analysis.

How It Works

Collects micro-signals from multiple sources, applies velocity and novelty scoring algorithms, and surfaces emerging trends through threshold-based alerting. The system learns from feedback to improve signal quality over time.

Architecture

• Signal aggregation pipeline from multiple sources
• Velocity/novelty scoring engine
• Threshold-based alerting system
• Feedback loop for continuous improvement

Purpose

Connects companies to locations, licenses to sectors, ownership patterns to activity density. Continuously updating graph structure that reveals hidden relationships in the data.

How It Works

Builds a graph layer over structured storage, using entity deduplication and cross-reference linking to create a comprehensive relationship map. The system continuously updates as new data arrives.

Architecture

• Graph layer over structured storage
• Entity deduplication engine
• Cross-reference linking system
• Continuous update mechanism

Purpose

Cost-optimized, horizontally scalable storage system designed for high-throughput writes and complex analytical queries.

How It Works

Uses JSONL append-only pipelines for raw data ingestion, SQLite WAL for high-throughput local writes, and PostgreSQL + pgvector for structured queries and semantic search. Compute routing is optimized for cost efficiency.

Architecture

• JSONL append-only pipelines
• SQLite WAL for high-throughput local writes
• PostgreSQL + pgvector for structured + semantic queries
• Cost-optimized compute routing
• Time-based partitioning

Purpose

Ensures system uptime through automated monitoring, health checks, and self-healing mechanisms.

How It Works

Watchdog processes continuously monitor agent health, restart failed components, and alert on critical issues. Circuit breaker patterns prevent cascading failures. Cron orchestration ensures scheduled tasks run reliably.

Architecture

• Watchdog processes for health monitoring
• Health check loops
• Cron orchestration for scheduled tasks
• Circuit breaker patterns for failure isolation
• Automatic agent restart and alerting