Equitus Video Sentinel (EVS)

 

"Zero-Trust Visual AI" Proof-of-Concept (PoC)

IBM EVS - Advanced multi-modal video analytics stack—featuring IBM EVS/Video Sentinel on Power11, AMD GPU extensions, Equitus.ai Arcxa integration, Red Hat Enterprise Linux (RHEL), and CI/CD orchestration for ONNX-SAM 2 Meta, Ultralytics YOLO, and Axelera Relate Anything—to IBM Power users with regulatory requirements demands a value proposition centered on uncompromising security, auditability, high throughput, and zero-trust data residency.

Offer targeted IBM Power enterprise clients a pre-configured, on-premises PoC kit. Demonstrate how an auditor can perform a single-click audit query via Equitus Arcxa to trace an alert back through its model version, weights build (CI/CD pipeline), hardware state, and frame-level semantic graph.

Regulated industries (such as finance, defense, critical infrastructure, healthcare, and public safety) cannot afford cloud data-leakage risks, latency bottlenecks, or "black box" AI models that fail compliance audits.

1. Core Value Pillars for Regulated IBM Power Users

Multi-modal video analytics stack to IBM Power users in heavily regulated sectors requires shifting the narrative from raw algorithmic capability to architectural sovereignty, mathematical explainability, zero-trust data boundaries, and continuous compliance automation.

Regulated enterprises—such as Federal/Defense (CJIS, FedRAMP High, CMMC), Financial Services (PCI-DSS, SEC 17a-4, GLBA), Critical Infrastructure (NERC CIP, TSA Directives), and Healthcare/Life Sciences (HIPAA, FDA 21 CFR Part 11)—cannot compromise on data movement, non-auditable "black box" models, or cloud-egress risks.

When positioning this architecture, frame the messaging around four regulatory-friendly pillars:

• In-Boundary Sovereignty & Hardware Trust (Power11 + RHEL): Emphasize that sensitive multi-modal streams never have to leave secure on-premises or private sovereign cloud boundaries. Power11’s enterprise RAS (Reliability, Availability, and Serviceability) combined with RHEL provides a hardened, tamper-resistant environment that satisfies frameworks like NIST, ISO 27001, and GDPR.

• Explainable & Traceable AI Pipelines via Equitus.ai Arcxa: Regulators increasingly demand explainability in automated decision-making. By tying Equitus.ai Arcxa into the ingestion pipeline, every frame processed by YOLO, SAM 2, or Relate Anything gains structural context, metadata lineage, and graph-backed audit trails, turning unstructured video into provable, compliant records.

• Deterministic Performance with AMD GPU + Open ONNX Runtimes: Avoid vendor lock-in and performance degradation. Utilizing standardized ONNX formats ensures reproducibility across environments, while AMD GPU acceleration on Power infrastructure handles heavy concurrent inference streams without choking core system bandwidth.

• Validated CI/CD Governance: Address the compliance nightmare of "shadow AI updates." Highlight a GitOps/CI/CD pipeline native to RHEL that automatically tests, validates, and rolls out model weights (Meta SAM 2, YOLO) under strict change-management controls required by financial and federal auditors.

2. Tailored Messaging by Industry Scope

Critical Infrastructure & Energy (NERC CIP, TSA Security Directives)

• Solves Pain Point: Massive sprawling perimeters, legacy monitoring systems, mandatory real-time intrusion detection, and strict penalties for non-compliance or downtime.

• "Achieve continuous perimeter compliance without cloud dependency. EVS Video Sentinel on Power11 leverages Ultralytics YOLO and Axelera Relate Anything to execute real-time anomaly detection locally. Backed by Power11’s enterprise-grade uptime and immutable audit logs from Equitus.ai Arcxa, your physical security infrastructure satisfies NERC CIP logging mandates natively while eliminating single points of failure."

Financial Services & High-Security Facilities (PCI-DSS, SOC 2, SEC)

• ASC: Protecting sensitive asset zones, tracking compliance breaches, and maintaining absolute data privacy (preventing biometric or proprietary visual data from leaking to public cloud endpoints).

• IBM EVS: "Turns visual data into secure, structured intelligence entirely on-prem. By running our multi-modal ONNX pipeline (SAM 2 + YOLO) on RHEL and Power11, your compliance teams get real-time policy enforcement and behavioral tracking. Every alert is cryptographically and semantically anchored via Equitus.ai Arcxa, ensuring instant compliance reporting for SOC 2 and internal risk audits without exposing raw video feeds."

Public Safety & Government Defense (CJIS, FedRAMP-aligned architectures)

ASC: Strict data chain-of-custody rules, high-volume forensic video search requirements, and the need for rapid situational awareness.

IBM FEDERAL: "Accelerate forensic investigations while maintaining absolute CJIS compliance. The integrated stack indexes multi-modal streams at the edge, using Meta SAM 2 for precise object/segment tracking and Relate Anything for contextual cross-referencing. Because processing happens locally on secure Power infrastructure with automated CI/CD compliance guardrails, chain-of-custody is unbroken and fully auditable."

3. Execution Strategy: How to Go to Market

1. Co-Marketing & Reference Architectures: Partner with IBM and Red Hat field teams to publish a Validated Design Guide demonstrating how EVS Video Sentinel + Arcxa runs seamlessly on RHEL and Power11. Enterprise buyers trust solutions backed by joint ecosystem validation.

1. "Compliance-First" Proof-of-Concepts (PoCs):Instead of leading with raw frames-per-second (FPS) benchmarks, lead with a Compliance Audit Sandbox. Show prospective clients how quickly an auditor can query the Equitus-backed knowledge graph to trace why a specific video event was flagged, verified, and archived.

1. The "No-Cloud-Tax" Economic Argument: Highlight the total cost of ownership (TCO) advantage. Streaming thousands of high-definition video feeds to public cloud AI APIs incurs massive egress fees and compliance exposure. Processing multi-modal AI locally on high-throughput Power11 servers with AMD extensions caps operational expenses while keeping data legally contained.

Pillar	Architectural Enabler	Regulated Value Proposition
In-Boundary Data Sovereignty	IBM Power11 + AMD GPUs on Red Hat Enterprise Linux (RHEL)	Raw, multi-modal video streams never leave the secure hardware boundary. Zero cloud-dependency or external data leakage, fulfilling air-gapped security frameworks.
Auditable & Explainable Visual AI	Equitus.ai Arcxa (Knowledge Graph / KGNN Integration)	Transforms unstructured video pixels into structured, semantically linked relationship graphs. Replaces opaque predictions with traceable, auditable metadata lineage required by regulators.
Regulated ML-Ops & Model Lineage	RHEL Native CI/CD Pipelines + ONNX Runtime	Prevents "shadow AI" and unauthorized updates. Automated build/test pipelines govern how Meta SAM 2, Ultralytics YOLO, and Axelera Relate Anything models are validated and deployed.
Uncompromised Ingestion Throughput	Power11 I/O Subsystem + AMD GPU Acceleration	Handles massive concurrency and ultra-low latency inference for high-density camera feeds without saturating enterprise backbones.

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IBM EVS -  Equitus Video Sentinel (EVS)—their on-premise video analytics platform built for IBM Power11 and AMD GPUs—the broader Equitus ARCXA platform serves as an enterprise Data Migration Engine and Governance platform. [1, 2]

Legacy SQL mapping has traditionally been sold as time-and-materials. Analysts reverse-engineer schemas, hand-write mappings, and bill for every week the scope grows. The client carries the risk, and the SI’s margin depends on how many people it can staff.

Fixed-Fee Works;  ASC provides Systems Integrators tools to service Enterprise Migration/ Integration/ Security more profitably. 

1. Scope in units the platform can measure. Price per source system, per schema, per table or object band, or per mapped domain. Complexity tiers (simple, moderate, heavy stored-procedure logic) keep the price predictable without hiding risk.
2. Paid short discovery, Arcxa profiles the legacy estate first, so the fixed fee rests on measured scope instead of guesswork. The discovery fee can be credited against the build.
3. Defined Deliverable. The package includes the validated mapping set, generated SQL, lineage and documentation, and a reconciliation report showing source-to-target parity.
4. Clear Change-control Terms. Anything outside the agreed object count or complexity tier is repriced by unit, not by hour.

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1. Data Movement & Migration Orchestration

• Governed Data Ingestion: Connects diverse operational data sources and materializes clean, governed datasets within a unified environment. [1]
• Repeatable Workflows: Orchestrates reliable data pipelines without requiring separate control planes for ingestion, execution, or interoperability. [1]
• Portable Domain Knowledge: Utilizes a semantic ontology layer so mapping logic can be carried over across different migrations rather than rebuilt from scratch. [1]

2. Semantic Mapping & Automation

• Automated Semantic Mapping: Eliminates manual data preparation by generating a semantic layer that preserves business context across clouds.
• Auto-ETL Integration: Dynamically parses semantic data structures to build self-constructing knowledge graphs.
• Local AI Execution: Allows organizations to run semantic parsing and auto-mapping models locally to keep sensitive data highly secure. [1, 2, 3]

3. Data Lineage & Cryptographic Auditing

• Granular Traceability: Captures every individual transformation at both the rule and value levels, allowing data discrepancies to be traced in seconds. [1]
• Cryptographic Audit Chains: Establishes a tamper-evident lineage layer specifically built for compliance and auditor reviews (e.g., SOX, HIPAA). [1]

4. Enterprise Architecture & Flexibility

• Open-Weight & Local Delivery: Arcxa Builds a modern technical stack (Rust, Docker, Kubernetes, GITHUB, and React) under a Business Source License (BSL 1.1).
• Systems-of-Systems Validation: Adds a comprehensive validation layer to existing migration setups, minimizing rework and compliance risks. [1, 2, 3]

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Summary: Equitus ARCXA; codebase and technical specifications, of the core capabilities of:

If you would like to look deeper into the architecture, I can provide more details on how ARCXA integrates with specific clouds like IBM or SAP, or how it utilizes its Knowledge Graph Neural Network (KGNN) platform. Let me know what you'd like to Learn about.

IBM Equitus Video Sentinel (EVS) Video Analytics Solution Fit

 

[Deep Cyber-Physical Intelligence]

"As the global video analytics market scales rapidly—projected to grow from $12 billion in 2024 to over $38 billion by 2030—organizations face a major bottleneck: processing massive volumes of video and physical camera streams alongside enterprise cyber data without driving up latency, bandwidth, or cloud GPU infrastructure costs"

Equitus Expert Labs (EEL) Assesses Video Analytics Solution Fit

" ARCXA's Semantic Control Plane deployed on IBM Power 11, transforms IBM EVS video feeds and core enterprise data into unified 3-column RDF Triples. Backed by KGNN reasoning, this unlocks true cyber-physical fusion—empowering your core systems to automatically reason over physical security, logistics, and sales flows with zero compromise to enterprise performance or compliance."

EEL Proposes: IBM EVS, combining Equitus intelligent data ingestion products bridging the expanding cyber and physical infrastructure intelligence gap  into a unified core operational stack. IBM EVS: Bridges and Extends, not replaces  your current CYBER/PHYSICAL Infrastructure.  IBM EVS’s GPU interface, expands Power10/11’s on-chip MMA acceleration to GPU-enabled processing, rather than MMA being solely an alternative to GPUs. Contact Us to start Evaluating your Project Scope, Goals and Timeline.

Benefit Area	How IBM Power + Equitus Delivers
Hardware & Cost Efficiency	Eliminates the need for specialized edge GPUs by maximizing IBM Power MMA throughput.
Silo Elimination	Unifies physical camera feeds with cyber logs into a single SPARQL-queryable Knowledge Graph.
Reduced Alert Fatigue	AI flags only multi-vector anomalies, allowing operators (HITL) to focus on verified threats.
Enterprise Governance	Operates securely on RHEL/OpenShift with complete data provenance and compliance tracking.

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Section I. Accelerated Deployment via Equitus Expert Labs (EEL)

EEL  helps organizations rapidly capitalize on the expanding video analytics market—without suffering from protracted integration cycles or complex IT/OT friction—Equitus Expert Labs provides a streamlined, turnkey pathway from initial architecture to operational readiness.

1. Turnkey Delivery & Services

• End-to-End Execution: Equitus Expert Labs manages consulting, architecture mapping, container deployment, integration, and performance testing.

• Rapid Readiness: Pre-configured deployment blueprints significantly reduce installation and validation time, allowing organizations to go live in days rather than months.

2. Pre-Configured Hardware & Platform Bundles

• IBM Power Hardware Optimization: Specifically engineered for deployment on IBM Power IC922 and IBM Power S1022 servers. By harnessing IBM’s native Matrix Math Acceleration (MMA), the platform achieves real-time video inferencing without necessitating separate GPU accelerators.

• Cloud-Native & Hybrid Orchestration: Standardized on Red Hat OpenShift (as well as SUSE Rancher), enabling consistent container management across on-premises edge nodes, private data centers, and multi-cloud environments.

3. Seamless Infrastructure Integration

• Identity & Access Management: Direct integration with Active Directory / LDAP for unified role-based access control (RBAC), multi-factor authentication, and enterprise governance.

• Video Ecosystem Compatibility: Connects natively with existing IP camera networks and enterprise Video Management Systems (VMS) via standard protocols (ONVIF, RTSP), preserving prior hardware investments.

• Enterprise-Scale Ingestion: Scales intelligently from local multi-camera setups to thousands of high-definition video streams across distributed enterprise, defense, transit, and public safety environments.

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SECTION II.

Integrating IBM Equitus Video Sentinel (EVS) with advanced multi-modal computer vision and reasoning frameworks—such as Ultralytics (YOLO), Meta SAM 2 (Segment Anything Model 2), and spatial/relational reasoning models like Axelera's RelateAnything—creates a high-performance, real-time pipeline.

IBM EVS: EMA is a productized cyber-physical integration fixed-fee service: connect cameras and enterprise systems, turn observations into contextualized events, run suitable AI locally, and use GPU cloud capacity selectively.

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IBM EVS - Video Analytics Solutions Fit - provides Power 10/11 Enterprise Users a defined operational capability at a fixed-fee and scope — not an open-ended AI integration project.

IBM EVS anchors enterprise infrastructure to IBM Power 10/11 and Axelera's AI Processing Units [AIPUs], this stack transitions security architectures from basic motion detection to deep cyber-physical intelligence.

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III. IBM EVS - Video Ai Inter-connection

Equitus Video Sentinel gives IBM Power10/11 customers a practical entry point into video intelligence using built-in MMA acceleration, with a path to expand into GPU-accelerated multimodal analytics through IBM’s forthcoming GPU interface. Customers can start with existing Power infrastructure and extend processing capacity as their cyber/physical security requirements grow.

1. IBM EVS (Ingestion & Stream Orchestration): Acts as the foundational video bridge. It captures live feeds from disparate cameras and VMS architectures, standardizes stream formatting, and allocates frame chunks to the appropriate vision pipeline.

2. Ultralytics YOLO (Fast Localized Detection): Handles first-pass, low-latency bounding-box detection (identifying humans, backpacks, vehicles, or weapons) at frame rates required for real-time alerting.

3. Meta SAM 2 (Precision Segmentation & Tracking): Takes object prompts from YOLO and performs zero-shot spatial-temporal segmentation. SAM 2 tracks exact pixel masks across video frames even through occlusions, cluttered environments, or erratic movements.

4. Axelera RelateAnything (Open-Vocabulary Relational Mapping): Evaluates image regions from SAM 2/YOLO and applies open-vocabulary predicates to map real-time interactions (“Person X is concealing Object Y” or “Person A is handing item to Person B”). Accelerated via hardware like Axelera's Metis/Europa AIPUs, this executes edge intelligence with high energy efficiency.

5. Equitus KGNN & SPARQL (Semantic Fusion): KGNN can translate 2 column data into triple store RDF Knowledge Graphs and Ingests  the structured relational metadata produced by the vision models and binds it to enterprise data ( core financial logs, access control records, and asset inventories) using schema-less knowledge graphs queried via SPARQL.

IV. Tailored Cutting-Edge Use Cases

1. Advanced Theft Protection (Retail Loss Prevention & Asset Tracking)

• The Fusion: EVS streams store feeds to YOLO to spot localized concealment behavior. When a suspicious motion or item handling is flagged, SAM 2 segments the exact object (e.g., high-value retail electronics) and tracks its boundary as the suspect moves through the store.

• Relational Reasoning: RelateAnything computes dynamic predicates between the individual and the inventory item ("holding", "concealing inside bag").

• Enterprise Action: Equitus KGNN instantly cross-references this visual event with POS (Point of Sale) logs and inventory tracking databases. If no payment event matches the timestamp via a SPARQL query, an automated, evidence-backed alert is sent directly to security personnel.

2. Anti-Money Laundering (AML) & Physical-Digital Fraud Fusion

• The Fusion: AML is traditionally limited to database text logs (wire transfers, account flags). This architecture bridges physical branch video with banking systems. EVS monitors high-value teller lines or ATM vestibules.

• Relational Reasoning: YOLO and RelateAnything detect behavioral anomalies—such as an individual repeatedly switching physical burner phones, handling multiple unassociated debit cards, or coercing a customer—and map the physical actors involved.

• Enterprise Action: The KGNN binds the visual identity/biometric vector of the suspect to core banking ledger data, flagging accounts showing sudden structuring or rapid cash-out behaviors that correlate precisely with physical surveillance triggers.

3. Missing Persons & Vulnerable Subject Searches

• The Fusion: Rapidly locating a missing child or vulnerable adult across a sprawling enterprise campus, smart city, or transit hub.

• Precision Tracking: EVS ingests feeds across multiple disparate cameras. When an operator inputs a description or reference image, SAM 2 provides robust video segmentation to maintain tracking continuity as the subject moves across different camera angles with changing lighting or clothing partial occlusions.

• Relational Reasoning: RelateAnything evaluates contextual relationships ("accompanied by unfamiliar adult", "entering restricted maintenance zone").

• Enterprise Action: The system updates the central Knowledge Graph in real-time, executing a SPARQL query to map the subject's projected path against access control logs and historical foot-traffic data to guide first responders directly to their current location.\

V. EVS on IBM Power Core Idea - Tech Specs

Equitus Video Sentinel (EVS) provides large‑scale, on‑premises video analytics on IBM Power11 systems using AMD Instinct GPUs (MI210 today, MI350 expected). It turns live camera feeds into searchable events and real‑time alerts without relying on the cloud.

Validated Performance

• ~100–200 active cameras per AMD MI210 GPU in baseline testing.

• 2× expected capacity on MI350 (not yet benchmarked).

• Performance varies by resolution, frame rate, and analytic profile.

Key Capabilities

• Real‑time analytics:

  • Person & vehicle detection

  • Classification & re‑identification

  • Cross‑camera tracking

  • Behavioral analytics (tripwire, loitering, left‑object, parked‑vehicle, weapons detection)

• Alerts:

  • Real‑time notifications with keyframes and metadata

• Forensic search:

  • Search across live and recorded video

• Data layer:

  • Sentinel API

  • PostgreSQL

  • S3‑compatible storage

Architecture Overview

• Input: Fixed, PTZ, mobile cameras; RTSP; VMS integration; uploaded video files

• Compute: IBM Power11 + AMD GPUs

  • EVS ONNX inference engine optimized for MI210

  • Containerized stack running in a Power LPAR using ROCm

  • Deployable via Helm on OpenShift or Kubernetes

• Operators:

  • Alert feed, search, playback

  • Keycloak SSO and role‑based access

Why IBM Power

• Run analytics next to systems of record

• Hardened platform with GPU acceleration for large camera counts

• Power11 MMA enables smaller or edge deployments

• Fully on‑prem, no cloud dependency

Component	Role in the solution	Important boundary
EVS	Analyze video, generate alerts, and make footage searchable across cameras.	Confirm supported cameras, VMS integrations, analytics, and throughput for the actual deployment. ibm+1
Ultralytics	Develop and deploy vision models for tasks such as detection, segmentation, and pose estimation.	Treat integration with EVS as an engineering workstream unless documented as native. Ultralytics does document an Axelera deployment path. ultralytics+1
Meta SAM 2	Segment selected objects and follow their masks through video.	Useful for detailed object isolation or investigative workflows; segmentation alone does not determine whether an event is a threat. meta
ONNX / ONNX Runtime	Provide a model-interchange and execution path across supported runtimes and acceleration backends.	Hardware portability depends on supported operators, execution providers, and software versions—not simply on exporting a model. onnxruntime+1
Axelera	Accelerate suitable vision workloads at the edge.	Ultralytics’ documented path includes export, compilation, and accuracy validation. This does not prove EVS or SAM 2 compatibility. ultralytics
IBM Power MMA	Accelerate supported AI computations on premises.	Equitus advertises MMA use for EVS and KGNN; do not extend that claim to arbitrary third-party models without testing. equitus+1
KGNN and Fusion	Connect video events with other information and present contextual intelligence.	Correlation needs verified mappings, permissions, and source provenance. equitus+1