Pyramid Data Cortex and Equitus.ai KGNN

 Pyramid Data Cortex and Equitus.ai KGNN are both advanced data platforms aimed at enabling high-performance analytics, AI, and data fusion for enterprise and mission-critical users. However, their architectures, use cases, and strategic advantages differ significantly.

Below is an unbiased, technical comparison:

---

🔷 PYRAMID DATA CORTEX

Vendor: Pyramid Analytics
Core Function: Enterprise decision intelligence platform
Positioning: Unified analytics platform that integrates data preparation, business logic, data science, and visualization in a governed framework.

🔑 Key Features:

• No-code / low-code interface for end-users

• Semantic modeling layer that abstracts underlying data

• Built-in machine learning (AutoML, Python/R integrations)

• In-memory data engine for high-speed queries

• Strong governance, role-based access control (RBAC)

• Designed for business intelligence and analytics at scale

Use Cases:

• Enterprise dashboards, KPI tracking

• Augmented analytics for financial, healthcare, and manufacturing sectors

• Decision intelligence for regulated industries

---

🔷 EQUITUS.AI KGNN

Vendor: Equitus Technologies
Core Function: Knowledge Graph Neural Network platform
Positioning: Multi-domain knowledge fusion engine that integrates structured, unstructured, and sensor-derived data into actionable intelligence through graph-based AI.

🔑 Key Features:

• Federated knowledge graph neural network (KGNN) architecture

• Real-time ETL from video, sensor, documents, cyber logs

• Explainable AI through graph queries, lineage, and inference

• Deployable in air-gapped, on-prem, hybrid, or tactical edge environments

• Native integrations with video analytics (EVS), XDR, AIX, and Cyberspatial

Use Cases:

• National security, defense C4ISR

• Border surveillance and real-time event detection

• Enterprise entity resolution, insider threat detection

• Maritime situational awareness (with FMC Starlink support)

---

🧠 Head-to-Head Comparison

Feature/Metric	Pyramid Data Cortex	Equitus.ai KGNN
Core Engine	BI + AutoML + Semantic Layer	Knowledge Graph Neural Network + Multimodal AI
Primary Input	Structured data (SQL, OLAP, Excel, CSV)	Structured + Unstructured + Sensor Data
Output	Dashboards, charts, predictions	Graph analytics, knowledge surfaces, AI alerts
AI Model Transparency	Some XAI support via AutoML	High: graph lineage, context-aware reasoning
Integration	Data lakes, BI tools, Python/R	APIs, sensors, XDR, video, legacy DBs
Deployment Flexibility	SaaS, cloud hybrid	On-prem, tactical edge, air-gapped, SCIF-ready
Security Model	Role-based, governed	Military-grade zero trust + encryption
Client Footprint	Commercial (finance, healthcare, logistics)	DoD, CBP, DHS, USSF, enterprise AI+security
Real-Time Event Support	Limited (scheduled refresh)	High (mission-critical, streaming ingestion)

---

🛰️ Strategic Differentiators

Pyramid Strengths	Equitus Strengths
Business-first, BI-friendly UX	Military-first, graph AI and mission-oriented
Unified decision-intelligence	Real-time fusion from cyber/video/sensor/data
Governed analytics	Multimodal intelligence across domains
Strong commercial enterprise play	Strong defense, public safety, and tactical edge

---

🧩 Summary

Scenario	Recommendation
Enterprise-wide BI modernization	✅ Pyramid Data Cortex
Mission-critical, secure intelligence fusion	✅ Equitus.ai KGNN
Graph-based AI, explainability, multi-domain	✅ Equitus.ai KGNN
C-level reporting and predictive analytics	✅ Pyramid Data Cortex

---

🔧 Combined Use?

A forward-thinking strategy might deploy Pyramid Cortex for executive visualization and governance, while using Equitus.ai KGNN for real-time, AI-driven knowledge creation and anomaly detection at the operational layer—especially across defense, cyber, and logistics domains.

Would you like a system integration diagram, competitive positioning deck, or joint platform whitepaper draft?

Splunk’s Machine Learning Toolkit

 

How Splunk’s Machine Learning Toolkit (MLTK) Enhances Cyber Terrain Visibility in Datacenters

Splunk’s Machine Learning Toolkit (MLTK) significantly advances cyber terrain visibility in datacenters by enabling proactive, data-driven detection and response across complex digital environments. Here’s how:

---

1. Anomaly and Outlier Detection Across Diverse Data Sources

• MLTK empowers security teams to build and deploy machine learning models that baseline normal network, user, and system behavior within the datacenter.

• By continuously analyzing logs, network flows, authentication events, and other machine data, Splunk’s ML algorithms can identify subtle deviations that may indicate emerging threats, compromised accounts, or lateral movement—even when traditional signature-based methods fail452.

• For example, MLTK can highlight unusual spikes in failed logins, abnormal data transfers, or atypical command-line usage, providing early warning of attacks or misconfigurations5.

---

2. Contextual and Holistic Visibility

• Splunk normalizes and enriches machine data using schemas like CIM or OCSF, allowing ML models to correlate activity across servers, network devices, applications, and user endpoints56.

• This unified view enables the detection of complex, multi-stage attack patterns that span multiple parts of the cyber terrain, such as coordinated credential abuse or insider threats56.

---

3. Risk-Based Alerting and Prioritization

• MLTK supports risk-based alerting (RBA), which leverages ML to aggregate and score related security events. This reduces alert fatigue by highlighting high-risk incidents that merit immediate investigation6.

• Analysts can focus on the most critical threats, improving both response speed and accuracy6.

---

4. Customizable Models for Datacenter-Specific Threats

• Security teams can use MLTK to develop custom ML models tailored to the unique operational patterns and risks of their datacenter environment54.

• For instance, Siemens used MLTK to classify millions of command-line executions daily, detecting potentially malicious sequences that would be difficult to spot with static rules5.

---

5. Enhanced Threat Hunting and Forensic Analysis

• MLTK’s guided workflows and algorithms facilitate advanced threat hunting, enabling analysts to proactively search for unknown threats and anomalous behaviors within massive datasets45.

• This capability is vital for mapping the cyber terrain, understanding attacker tactics, and conducting root-cause analysis after incidents47.

---

6. Automation and AI-Driven Insights

• Splunk AI and MLTK automate repetitive analysis tasks, freeing up security staff for strategic work while ensuring consistent monitoring of the cyber environment3.

• Integration with domain-specific large language models (LLMs) and deep learning systems further enhances detection, investigation, and response capabilities3.

---

Summary Table: MLTK’s Impact on Cyber Terrain Visibility

Capability	Benefit to Datacenter Cyber Terrain Visibility
Anomaly Detection	Early identification of unknown or subtle threats
Data Normalization & Correlation	Holistic view across all infrastructure layers
Risk-Based Alerting	Focus on high-impact threats, reduce alert overload
Custom ML Models	Tailored detection for unique datacenter behaviors
Threat Hunting & Forensics	Proactive discovery and deep incident analysis
Automation & AI Integration	Continuous, scalable monitoring and rapid response

---

In essence, Splunk’s MLTK transforms raw datacenter data into actionable cyber terrain intelligence, enabling organizations to detect, understand, and respond to threats with greater speed and precision than traditional approaches allow563.

1. https://www.splunk.com/en_us/products/machine-learning.html
2. https://www.teramind.co/blog/splunk-guide/
3. https://cybersecurityasia.net/splunk-unveils-ai-solutions-for-enhanced-detection-investigation-and-response-in-security-and-observability/
4. https://community.splunk.com/t5/Splunk-Tech-Talks/Using-Machine-Learning-for-Hunting-Security-Threats/ba-p/622341
5. https://4datasolutions.com/wp-content/uploads/2024/06/Splunk-security-use-case-enhanced-by-ai-and-ml.pdf
6. https://cybersecurity-excellence-awards.com/candidates/splunk-enterprise-security-2024/
7. https://securityscorecard.com/blog/leveraging-siem-splunk-for-enhanced-cybersecurity-a-comprehensive-guide/
8. https://digital.orange-business.com/en-en/einblicke/blog-numerik/understanding-splunk
9. https://www.splunk.com/en_us/resources/videos/splunk-education-a-taste-of-hands-on-labs-mltk-with-enterprise-security.html

unified intelligence and operations layer

Unified intelligence and operations layer

Integrating Splunk, Elastic, and XDR platforms with Equitus.ai KGNN at the data center level provides a unified intelligence and operations layer—where KGNN acts as the semantic fusion and AI inference engine, while the others serve as telemetry, observability, and security endpoints. Here's a breakdown of how they differ and how Equitus.ai can harmonize their strengths:

---

🔍 1. Key Platform Capabilities

Platform	Core Function	Data Focus	Integration Method
Splunk	Log and event correlation, SIEM	Indexed time-series data, logs	REST APIs, forwarders
Elastic (ELK Stack)	Search, observability	Logs, metrics, traces (unstructured or semi-structured)	Elastic Beats, Logstash
XDR (e.g., Palo Alto Cortex, CrowdStrike Falcon, Microsoft Defender)	Threat detection/response across endpoints	Enriched security telemetry, alerts	APIs, syslog, EDR connectors

---

🧠 2. Role of Equitus.ai KGNN (Knowledge Graph Neural Network)

• KGNN transforms raw telemetry + observability data into actionable knowledge by:

  • Linking disparate sources (log events, alerts, metrics) into context-rich entities.

  • Mapping behavior and dependencies across users, processes, and systems.

  • Using inference on graph topology to detect anomalies, root cause, or mission impact.

---

🧩 3. Integration Architecture at the Data Center Level

🔗 Data Ingestion Layer

• Splunk Forwarders, Logstash Pipelines, or XDR Webhooks send data to an Equitus Ingestion Node.

• Use Kafka or Apache NiFi as middleware for stream processing into the KGNN pipeline.

🔀 Translation and Normalization

• Equitus.ai converts logs/alerts into ontology-based triples or structured events:

  • "User_A" accessed "Server_12" via "SSH" → converted to semantic graph edges.

  • Applies enrichment with Threat Intelligence, IAM data, and Business Logic.

🧠 Knowledge Graph Core (KGNN Engine)

• Events are linked into a real-time knowledge graph, enriched with:

  • Mission context (e.g., CBP border sensor → Fusion Center → Cloud node)

  • Asset classification (e.g., mission-critical vs auxiliary)

  • EVW/EEV metrics for automated risk/value scoring.

📊 Action & Visualization Layer

• Splunk dashboards and Elastic Kibana panels receive graph insights via API push or shared dashboards.

• Equitus generates mission impact maps, attack path reconstructions, and value-based prioritization for SOC and NOC operators.

---

✅ Benefits of Integration

• Operational Awareness: Equitus graph connects endpoint alerts (XDR) with network logs (Elastic) and system events (Splunk).

• Reduced Dwell Time: KGNN infers hidden relationships and threat paths faster than traditional correlation engines.

• Mission-Centric Response: Enables commanders and IT leadership to prioritize based on EVW/EEV scoring, not just alert volume.

• Multi-Tenant Flexibility: Ideal for shared services across government or defense enclaves.

---

📡 Sample Use Case

Scenario: A failed login event on a critical database server is seen in Splunk, while Elastic detects a spike in resource use on the same node, and XDR flags a process anomaly.

Equitus.ai KGNN:

• Links the failed login to a known threat actor via graph reasoning.

• Infers lateral movement potential based on known privilege escalation paths.

• Issues a mission impact alert prioritized by business/mission criticality.

• Sends this to SOC dashboards in Splunk and Elastic, and auto-generates an incident response plan.

---

🔐 Contact & Implementation Support

To explore Equitus.ai KGNN integrations with Splunk, Elastic, and XDR in your data center:

• 📧 Contact: info@equitus.ai

• 🌐 Website: https://equitus.ai

• 📞 Sales & Engineering: Available for DoD/IC, Critical Infrastructure, and Commercial DCs

Would you like a system architecture diagram or integration whitepaper for stakeholder presentations?

Splunk, Elastic, and XDR platforms

Splunk, Elastic, and XDR platforms differ significantly in their core functionalities, data handling approaches, and integration with advanced network visibility tools like Teleseer. Below is a breakdown of their key differences and synergies in datacenter environments:

---

Core Platforms

Splunk Enterprise

• Focus: Large-scale machine data aggregation, analysis, and visualization1.

• Key Features:

  • Collects and indexes unstructured data from any source (logs, databases, APIs)1.

  • Advanced search processing language (SPL) for complex queries1.

  • Machine Learning Toolkit (MLTK) for predictive analytics and anomaly detection1.

  • Workload management for prioritizing critical tasks1.

• PCAP Handling: Primarily processes parsed network logs rather than raw packet captures (PCAPs). Requires third-party integrations for deep PCAP analysis.

Elastic (ELK Stack)

• Focus: Real-time search, analytics, and visualization of structured/unstructured data2.

• Key Features:

  • Elasticsearch for distributed data storage and retrieval2.

  • Kibana for dashboards and visualizations2.

  • Built-in alerting and security features (RBAC, encryption)2.

  • Horizontal scalability for large datasets2.

• PCAP Handling: Limited native PCAP support; typically processes extracted metadata via Beats or Logstash plugins.

XDR Platforms

Feature	CrowdStrike Falcon Insight	SentinelOne Singularity	Microsoft Defender XDR
Primary Scope	Endpoints, identity, cloud	Endpoints, cloud, identity	Endpoints, SaaS apps, email, cloud
AI/Automation	Charlotte AI for incident prioritization3	Purple AI for threat hunting4	Security Copilot (AI-guided actions)5
Response Capabilities	Remote remediation via RTR3	Autonomous response4	Automatic attack disruption5
Third-Party Integration	Supports 10GB/day free ingestion3	Singularity Data Lake4	Native Microsoft ecosystem integration5

---

PCAP-Driven Cyber Terrain Visibility

Teleseer (Cyberspatial)

• Role: Specialized network traffic analysis tool for PCAP data6.

• Key Advantages:

  • Browser-based analysis of large PCAPs without infrastructure6.

  • Visualizes network structure, device relationships, and threat patterns6.

  • Identifies 6,000+ protocols and extracts critical data segments6.

• Integration:

  • With Splunk/Elastic: Enhances network visibility by feeding parsed PCAP metadata into their analytics engines.

  • With XDR: Provides network-layer context to endpoint/cloud telemetry, closing visibility gaps in cross-domain attacks.

---

Datacenter Use Cases

1. Splunk
   Best for aggregating logs from servers, applications, and security tools, and correlating them with ML-driven insights1. Combines with Teleseer to map network traffic patterns to operational events.

2. Elastic
   Ideal for real-time monitoring of structured data (e.g., authentication logs) and cost-effective scalability2. Less suited for raw PCAP analysis without add-ons.

3. XDR Platforms

   • CrowdStrike: Detects lateral movement by correlating endpoint and identity telemetry3.

   • SentinelOne: Autonomous response to cloud-native threats4.

   • Microsoft Defender: Automatically disrupts phishing and SaaS-based attacks5.
     All benefit from Teleseer’s network-level context to trace attack paths across hybrid environments.

---

Key Differentiators

Aspect	Splunk	Elastic	XDR Platforms	Teleseer
Data Type	Unstructured logs	Structured logs	Cross-domain telemetry	Raw PCAPs
Strengths	Custom dashboards, ML-driven insights	Cost-effective scaling, RBAC	Automated threat response	Network terrain mapping
Gaps	Resource-intensive PCAP processing	Limited native security features	Network-layer blind spots	No native threat detection

For comprehensive datacenter security, organizations often layer Teleseer’s network visibility with Splunk/Elastic for analytics and XDR for automated response67.

1. https://www.bluevoyant.com/knowledge-center/splunk-enterprise-architecture-features-and-capabilities
2. https://www.elastic.co/elastic-stack/features
3. https://www.exabeam.com/explainers/crowdstrike/crowdstrike-xdr-solution-overview-pricing-pros-and-cons/
4. https://www.sentinelone.com/cybersecurity-101/endpoint-security/benefits-of-xdr/
5. https://www.microsoft.com/en-us/security/business/siem-and-xdr/microsoft-defender-xdr
6. https://www.cyberspatial.com
7. https://fidelissecurity.com/threatgeek/xdr-security/cyber-terrain-mapping-with-fidelis/
8. https://www.peerspot.com/products/comparisons/elastic-security_vs_splunk-enterprise-security
9. https://last9.io/blog/elastic-vs-splunk/
10. https://www.peerspot.com/products/comparisons/crowdstrike-falcon_vs_elastic-security
11. https://deploy.equinix.com/customers/cyberspatial/
12. https://www.selecthub.com/siem-tools/splunk-enterprise-security-vs-elastic-security/
13. https://paradigmitcyber.com/know-what-is-splunk-elastic-and-xdr/
14. https://www.peerspot.com/products/comparisons/elastic-security_vs_sentinelone-singularity-complete
15. https://www.splunk.com/en_us/solutions/splunk-vs-elastic.html
16. https://www.splunk.com/en_us/products/splunk-cloud-features.html
17. https://www.splunk.com/en_us/products/splunk-enterprise-features.html
18. https://www.splunk.com/en_us/products/platform.html
19. https://www.fortinet.com/resources/cyberglossary/what-is-splunk
20. https://go.teleseer.com
21. https://www.youtube.com/watch?v=JhJJ3KB2-3c
22. https://www.peerspot.com/products/comparisons/elastic-search_vs_splunk-enterprise-security
23. https://www.gartner.com/reviews/market/security-information-event-management/compare/product/elastic-security-vs-splunk-enterprise-security
24. https://www.exabeam.com/explainers/splunk/splunk-siem-key-features-limitations-and-alternatives/
25. https://www.teramind.co/blog/splunk-guide/
26. https://www.linkedin.com/products/cyberspatial-teleseer/
27. https://www.linkedin.com/company/cyberspatial
28. https://www.carahsoft.com/cyberspatial
29. https://www.vectra.ai/resources/pcap-packet-capture
30. https://www.cybersecurityintelligence.com/cyberspatial.html

Enterprise data center security stacks

Closing a critical blind spot in modern cyber defense stacks:

Cyberspatial integrates into enterprise data center security stacks by acting as a network telemetry intelligence layer, augmenting existing tools like Splunk, Elastic, and XDR platforms (e.g., CrowdStrike, SentinelOne, Microsoft Defender) with advanced PCAP-driven cyber terrain visibility. It provides context-aware packet intelligence, closing a critical blind spot in modern cyber defense stacks.

---

🔹 Strategic Integration of Cyberspatial in the Data Center

1. Data Enrichment & Flow Injection

• Packet Capture (PCAP) Deep Analytics: Cyberspatial operates at the packet level, not just metadata or logs.

• Integrates via:

  • Syslog forwarding

  • Kafka ingestion

  • RESTful API or custom connectors

• To Splunk or Elastic:

  • Injects enriched metadata from PCAP sessions.

  • Provides L7+ protocol fingerprinting, anomaly scores, and threat indicators not visible in NetFlow or traditional logs.

➡️ Outcome: Your SIEM/XDR gains ground-truth-level data from raw network packets, enabling higher-fidelity alerts and analytics.

---

2. XDR Augmentation

• Cyberspatial acts as a sensor grid across east-west and north-south traffic inside data centers.

• Integrates with:

  • CrowdStrike Falcon XDR

  • SentinelOne Singularity

  • MS Defender for Endpoint

• How: Sends enriched indicators and behavioral insights from network-level events that endpoints may miss.

➡️ Outcome: Closes endpoint visibility gaps and detects command-and-control, lateral movement, or data exfiltration occurring outside endpoint control.

---

3. Dashboards & Threat Terrain Mapping

• Cyberspatial provides its own visual dashboard interface:

  • Topology heatmaps

  • Critical asset overlays

  • Threat path simulation

• These insights can be:

  • Framed within Elastic/Splunk dashboards

  • Accessed via API

  • Or fed into SOAR platforms for playbook automation

➡️ Outcome: Converts raw data into actionable threat terrain maps supporting SOC operations and Tier 1/2 analysts.

---

🔹 Integration Architecture Diagram (Conceptual)

[Core Switches / Firewalls]
        ↓
[Cyberspatial Sensors (PCAP)]
        ↓
[Cyberspatial Engine]
        ↘      ↙       ↘
     [Splunk] [Elastic] [XDR Stack]
        ↓
[SIEM Dashboards & SOAR Actions]

---

🔹 Strategic Benefits in Data Center Context

Capability	Added Value
Full-packet telemetry	Enhanced visibility in encrypted/cloud-native traffic
Lateral threat mapping	Identifies east-west attacks inside segmented networks
Protocol & anomaly detection	Adds deterministic layer to probabilistic AI-based alerts
Defense-grade fidelity	Built to support mission-critical infrastructure (USSF, NIWC)

---

Would you like this turned into a slide deck, whitepaper format, or a partner solution brief aligned to Splunk or Elastic audiences?

Significant value to data centers

 A commercial division of Cyberspatial, featuring Teleseer and Wingman, could bring significant value to data centers and critical mission applications in the private sector by adapting and productizing its proven defense-grade capabilities. Here's how it could help:

---

1. Cyber Resilience for Critical Infrastructure

➤ Real-Time Network Visualization (Teleseer)

• Converts abstract PCAP/network traffic into dynamic 3D cyber terrain maps.

• Enables SOC operators to detect anomalies, lateral movement, and misconfigurations instantly.

• Applicable to critical infrastructure sectors (finance, energy, healthcare) needing active cyber terrain monitoring.

➤ Autonomous Defense Assistant (Wingman)

• Augments analyst workflows by offering real-time threat correlation, natural-language search across logs, and AI-generated threat assessments.

• Reduces mean time to detection (MTTD) and response (MTTR) for enterprise incident response teams.

---

2. Protecting Commercial Data Centers

➤ Multi-Tenant Visibility

• Helps colocation providers monitor segmented environments for compliance, insider threats, and bandwidth abuse.

• Visual overlays can show data ingress/egress, protocol use, and volumetric anomalies per tenant.

➤ East-West Traffic Inspection

• Layer 2–7 inspection using Teleseer's parsing of full packet captures (FPC), detecting threats that bypass traditional firewalls (e.g., APTs or insider movement).

• Essential for zero-trust architectures and microsegmentation policies.

---

3. AI/ML Model Assurance for Mission-Critical Workloads

• Integrates into model pipelines to monitor and log inference traffic, providing:

  • Confidence scoring of input/output

  • Monitoring of adversarial behavior (e.g., model probing or poisoning)

  • Audit trails for AI compliance (useful in regulated sectors)

---

4. Edge-to-Core Security for Hybrid Environments

• Ideal for organizations managing edge compute nodes (e.g., IoT, remote industrial facilities) connected back to centralized data centers.

• Wingman provides remote SOC augmentation, while Teleseer maps edge traffic, detecting spoofing or sensor tampering.

---

5. Threat Intel Integration & Reporting

• Commercial division could offer modular APIs for integrating Teleseer’s visual analytics into platforms like:

  • Splunk

  • Elastic

  • CrowdStrike Falcon/XDR dashboards

• Reports could be automated for compliance frameworks:

  • NIST 800-171 / 53

  • ISO 27001

  • SOC 2

---

6. Unique Competitive Advantage

Cyberspatial's defense pedigree (USSF, NIWC) brings credibility to sectors that demand provable, zero-failure solutions. A commercial suite could be marketed as:

“Military-grade visualization and AI cybersecurity, adapted for your enterprise's most critical applications.”

---

🔧 Next Steps to Develop the Commercial Division

1. Build integrations with commercial observability stacks (Datadog, Azure Sentinel, etc.)

2. Develop multi-tiered licensing for SME to enterprise (on-prem / hybrid / SaaS).

3. Package Wingman as a white-labeled SOC augmentation module for MSSPs.

4. Target industries with critical uptime and compliance needs: banking, energy, pharma, aviation, and maritime.

Would you like a one-pager or slide deck to present this to stakeholders or potential commercial partners?

AIX-based architecture

 

AIX-based architecture proposal integrating Equitus.ai KGNN & EVS with IBM's Hybrid Cloud, Data & AI, and Automation stack—tailored to support ROI justification through EVW/EEV metrics and Gen AI enablement.

---

📘 Custom AIX Architecture Proposal:

“Modernizing Mission-Critical Workloads with Gen AI and Equitus.ai”

---

1. 🎯 Executive Summary

This proposal outlines how integrating Equitus.ai's Knowledge Graph Neural Network (KGNN) and Video Sentinel (EVS) into IBM AIX environments can reduce ETL overhead, lower FTE demand, and increase mission-aligned outcomes. It provides stakeholders with a defensible business case to justify Gen AI investments through measurable EVW (Equivalent Value of Work) and EEV (Equivalent Employee Value) outcomes.

---

2. 🧩 Solution Stack Overview

Layer	Components	Partners/Tech Stack
Edge & Input Layer	Sensors, Cameras, Network Logs	EVS (Equitus Video Sentinel)
AI Ingestion Layer	Data feeds, video streams, structured/unstructured	KGNN (Knowledge Graph Neural Network)
AIX Core Layer	IBM Power10, AIX OS, Virtual I/O Servers	IBM
Middleware	Red Hat OpenShift, IBM MQ, IBM Cloud Pak	IBM, Equitus, Watsonx
Data Fabric / ML	Watsonx, DataStage, Db2 AI, Instana, Turbonomic	IBM AI & Automation
User Interface Layer	Dashboards, Command & Control Apps, Reporting	Maximo, Equitus UX, Custom UIs

---

3. 📉 Legacy Pain Points vs. Equitus-Enabled Improvements

Pain Point	Improvement via Equitus.ai	EVW / EEV Impact
ETL process delays AI modeling	KGNN auto-generates structured knowledge from data	↑ EVW, ↓ FTE for data prep
High analyst hours for video/audio review	EVS extracts and tags video metadata automatically	↑ EEV per analyst, ↓ SOC costs
Data silos across workloads	KGNN federates and contextualizes disparate datasets	↑ EVW across domains (Ops, Intel, Maint.)
Low explainability in AI outcomes	Transparent graph lineage and relevance scoring	↑ Trust, faster adoption of Gen AI tools

---

4. 📐 Reference Architecture Diagram

Let me generate a high-resolution architecture graphic showing:

• IBM AIX + Power10 as the backbone

• Equitus KGNN as the cognitive core for ingesting and linking data

• EVS processing video feeds at the edge

• Watsonx and IBM Cloud Paks using KGNN outputs for AI/ML

• Red Hat OpenShift enabling hybrid deployment

• Dashboards or operational UIs on top

🟢 Would you like the diagram in:

• PowerPoint Slide Format?

• High-resolution PNG or PDF?

• Whitepaper-style diagram with explainer notes?

---

5. 💰 Business Case Using EVW/EEV Metrics

Metric	Legacy Cost Estimate	With Equitus.ai	Savings (%)	Value Expression
ETL Engineering Hours	$2.2M/year	$0.8M/year	63%	↑EVW = $1.4M annual
SOC Video Analysts	$1.5M/year	$0.6M/year	60%	↑EEV = $900K annual
Time-to-Model AI Workloads	12–18 weeks	3–4 weeks	70% faster	Faster mission impact

---

6. 📍 Deployment Model Options

Deployment Type	Description	Use Case
On-Prem AIX	Full-stack on IBM Power10, closed-loop systems	Defense, CBP, Fed Integrators
Hybrid Cloud	Local KGNN + Watsonx on IBM Cloud/Red Hat OpenShift	Enterprise, Intelligence Fusion
Edge + Cloud	EVS on border edge; KGNN + Watsonx in cloud	Border surveillance, Public Safety

---

7. 📞 Next Steps & Contact

🧪 Proof of Value:

• Available 60–90 day PoV with IBM + Equitus teams.

• Custom sandbox with redacted/simulated mission data.

📍 Contacts:

• IBM Federal AI Practice – ibmcloud@us.ibm.com

• Equitus.ai Federal Solutions – info@equitus.ai , +1 (813) 540-6740