Power-Native Knowledge Graph platform

"Equitus KGNN is the Power-Native Knowledge Graph platform that unlocks the full AI potential of your IBM Power 11 investment.

Instead of spending months and millions manually prepping data, KGNN automatically unifies your complex data into a vectorized, AI-ready fabric in minutes. It runs lean on your Power cores—not expensive, external GPUs—maximizing your server efficiency and dramatically lowering the total cost of ownership for your enterprise AI initiatives."

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The Power11-Native Equitus KGNN platform significantly improves the cost structure and performance of enterprise IT systems in regulated industries by shifting costs from unpredictable cloud consumption and manual effort (OpEx) to optimized, consolidated, and highly efficient on-premise computing (CapEx/OpEx).¹

It achieves this by offering native performance and TCO advantages that competing cloud-native (Databricks, Snowflake) and non-optimized graph solutions (Neo4j) cannot match on the IBM Power 11 platform, especially within the stringent requirements of regulated industries.²

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1. Cost Structure Improvement (TCO Reduction)

KGNN on IBM Power 11 provides a superior TCO model, specifically targeting and eliminating the "hidden" or variable costs associated with cloud and generic data platforms:

TCO Advantage	KGNN on Power11	Competitors (Snowflake/Databricks)
Compute Cost Model	Fixed/Predictable. Runs on existing Power 11 assets. Leverages on-chip Matrix Math Accelerators (MMAs) for AI, eliminating external GPU hardware and associated licensing/cooling costs.	Variable/Unpredictable. Costs are consumption-based (query time, data volume scanned, warehouse size). Egress fees are a major hidden cost for regulated data that must be pulled back on-prem.
Data Ingestion Cost	Automated & Efficient. Features Auto-ETL and schema-less graph construction. Cuts the time for data unification and graph building by up to 80%, drastically reducing data engineering labor costs (OpEx).	High Manual Cost. Requires significant, continuous manual effort for complex ETL/ELT pipelines, schema definition, and data normalization, which drives up expensive data engineering salaries.
Software Licensing	Core Optimization. By being Power-native and highly efficient, KGNN helps clients maximize the utilization of their high-value IBM Power cores, resulting in a better consolidation ratio and lower effective per-core licensing costs for associated software.	Inefficient Core Use. Generic platforms are not optimized for the Power architecture, leading to underutilization of expensive core capacity and a higher hardware footprint for the same workload.
Data Governance Cost	Built-in Context. The knowledge graph structure provides automated data lineage and semantic context, simplifying auditing and compliance checks required by regulations (e.g., GDPR, CCPA), which reduces the cost of manual compliance enforcement.	Requires Add-ons. Governance, lineage, and compliance often require purchasing and integrating separate, costly third-party tools.

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2. Performance Improvement and Capabilities Comparison

KGNN's architecture, specifically its Power11-native design and Knowledge Graph approach, provides a performance leap over traditional data warehouses and generic graph databases for highly complex, connected data workloads:³

Capability	Equitus KGNN (Power11-Native)	Cloud Data Warehouse (Snowflake/Databricks)	Graph Database (Neo4j, etc.)
Data Unification/ETL	Automatic. Ingests structured and unstructured data and builds the semantic knowledge graph automatically (Zero ETL).	Manual/Batch. Requires complex, multi-stage ETL/ELT pipelines and denormalization. Optimized for SQL tables, not connected context.	Manual. Requires significant pre-modeling, schema definition, and manual data loading to define relationships.
AI Workload Execution	Power-Native Acceleration. Directly utilizes Power11's MMA for ultra-fast, on-chip inference and vector processing. No GPU required.	External GPU/Cloud Service. Requires sending data to external, costly GPU clusters (in the cloud) or leveraging non-native hardware acceleration.	CPU-Bound. Generally relies solely on CPU for graph traversal, often struggling with large-scale, high-concurrency vector/AI workloads.
Regulatory Compliance (Data Sovereignty)	Ideal. Designed for on-premise/hybrid deployment with no cloud dependencies, ensuring full data sovereignty and control required by regulated industries (Finance, Gov, Health).	Challenged. Core value proposition relies on public cloud infrastructure, creating data residency and egress cost challenges for regulated data.	Neutral. Can be deployed on-prem, but lacks the deep integration and 99.9999% uptime reliability of a Power11-native solution.
Query Performance	Real-time Context. Optimized for complex, multi-hop relationship queries that are essential for fraud detection, risk analysis, and RAG-based AI.	Slow for Relationships. Excellent for large-scale joins and aggregations, but extremely slow and costly for recursive/relationship-based queries.	Fast for Traversal. Excellent for graph traversal, but often less performant for mixed analytical workloads and AI vector processing at enterprise scale without manual tuning.

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The Value Proposition for Regulated Industries

For regulated enterprises, the key benefit is De-Risked AI Adoption and Data Sovereignty.⁴

KGNN allows IBM Power 11 clients to:

1. Maintain Control: Keep sensitive data on the highly secure, reliable, and auditable IBM Power 11 platform.

2. Accelerate AI: Bypass the massive manual data preparation bottleneck (Data-to-AI Acceleration) to operationalize AI models (like RAG for internal knowledge) in days, not months.⁵

3. Optimize Compute: Lower the cost of complex AI computation by leveraging existing, licensed Power 11 cores rather than incurring new, variable cloud or GPU expenses (Compute Optimization).⁶