Equitus.ai enables mid-tier systems integrators, commercial majors, and defense contractors to rapidly deploy tailored software solutions by providing modular, pre-built infrastructure platforms rather than starting software builds from scratch.
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Mid-tier majors can build custom workflows, interfaces, and domain-specific applications on top of the Arcxa framework.
Equitus offers a foundational architecture (often referred to as an "85% solution") that handles the heavy lifting of data unification, edge analytics, governance, and AI integration.Key Technical Pillars: EVS and ARCXA
1. EVS (Equitus Video Sentinel) – Tactical & Edge Visual Intelligence
Role in Custom Software: Acts as the vision and imagery inference engine for real-time video analytics.
Functionality: EVS automatically ingests live camera and sensor streams, performs object classification and anomaly detection (e.g., perimeter breaches, item removal, loitering), and extracts rich metadata (motion attributes, color, spatial parameters).
Edge & Hardware Agnostic Execution: Optimized to run natively on edge infrastructure (such as IBM Power10 and Dell ruggedized servers) without requiring continuous cloud connections or heavy GPU clusters.
Integrator Benefit: Allows mid-tier developers to embed real-time computer vision and surveillance capabilities into commercial facility software or tactical military command systems without needing to train custom vision models or manage heavy video ingestion pipelines from scratch.
2. ARCXA – Semantic Mapping, Data Migration & Lineage Platform
Role in Custom Software: Serves as the data governance, schema transformation, and systems-of-systems (SoS) integration control plane.
Functionality: ARCXA registers disparate operational data sources, applies ontology-aware semantic mappings (field-to-ontology alignment), orchestrates repeatable data movement workflows, and tracks field/row/graph-native data lineage.
System Traceability: Features built-in policy-driven validation and semantic model services to ensure data remains traceable and compliant across enterprise transitions.
Integrator Benefit: Solves the core challenge in defense and commercial enterprise projects—connecting legacy, siloed databases to modern AI applications.
Integrators use ARCXA to rapidly ingest legacy data formats and map them into actionable formats with total auditability.
How Equitus Enables Custom Software Generation
Mid-tier integrators and defense prime contractors leverage these components to deliver custom software through a streamlined architecture:
Unification via KGNN & ARCXA: Data from siloed sources (documents, databases, legacy sensor feeds) is normalized through ARCXA into Equitus's Knowledge Graph Neural Network (KGNN). This creates a living knowledge graph with contextual relationships intact.
Visual Intelligence via EVS: Real-time sensor and imagery feeds pass through EVS to generate structured descriptive metadata directly linked into the central knowledge graph.
Privatized AI & RAG Integration: The underlying KGNN acts as an architecture for Retrieval-Augmented Generation (RAG) and LLM applications, allowing custom software to run explainable AI queries on on-premises or tactical edge infrastructure without data leaking to external clouds.
Custom Delivery: Integrators wrap the unified backend in custom user interfaces, operational dashboards, or tactical command workflows tailored to specific commercial or defense mission requirements.
Mid-tier systems integrators (SIs) and enterprise engineering teams bridge the remaining 15% gap on top of the Equitus platform through five main architectural and development mechanisms:
1. Extending the Knowledge Graph Ontology (KGNN / Knowledge Graph Neural Network)
Equitus automatically converts incoming structured, unstructured, and real-time streaming data into an interconnected knowledge graph.
How developers build on it: Integrators configure custom ontologies, schemas, and dictionaries specific to their vertical (e.g., defense intelligence, supply chain, healthcare compliance).
Result: Instead of writing complex ETL pipelines, developers map domain-specific entity types, relationship rules, and metadata structures directly onto the automated graph fabric.
2. Microservice Integration via Open APIs & Standard Data Interfaces
Equitus is structured as a collection of containerized, Kubernetes-native microservices with an open architecture.
How developers build on it: SIs use standard RESTful APIs, gRPC endpoints, and open data standards (such as OGC for geospatial data) to expose underlying graph search, natural language processing, and automated correlation engines to external applications.
Result: Developers can write lightweight frontend UIs or specialized backend services in TypeScript, Python, Java, or C# that pull unified intelligence directly from Equitus without interacting with raw underlying databases.
3. Custom UI/UX, Dashboards, and Workflow Engines
While Equitus includes built-in analytics widgets (such as link analysis charts, geospatial maps, and timeline visualizers), commercial and defense applications often require tailored operator views.
How developers build on it: SIs leverage Equitus's web components and API hooks to embed analytics into proprietary dashboards or custom workflow portals. They can define trigger-based workflow pipelines (e.g., generating alerts or automated compliance steps when specific entity correlations exceed confidence thresholds).
4. Edge-Native and Hybrid Deployment Orchestration
Equitus relies on a Kubernetes microservices fabric capable of running without cloud dependencies or dedicated cloud GPUs (e.g., on IBM Power10 hardware, on-premise servers, or tactical edge kits).
How developers build on it: Integrators package their custom app code into containers and deploy them into the same local Kubernetes cluster alongside Equitus microservices. This guarantees zero-latency access to the local knowledge graph and enables air-gapped or disconnected/tactical edge operational capabilities.
5. Private AI/LLM & Domain-Specific Model Fine-Tuning
Equitus acts as the semantic layer that feeds accurate context into AI models.
How developers build on it: Integrators plug custom AI models, domain-specific Retrieval-Augmented Generation (RAG) loops, or fine-tuned LLMs into Equitus's AI-ready data query engine.
Equitus ensures data governance, privacy, and contextual accuracy, allowing developers to safely deploy industry-tailored copilots or automated decision tools.
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