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Modern Data FoundationsClient: Tier-1 Telecommunications & Infrastructure Enterprise
Enterprise Knowledge Graph & SSOT Lakehouse
Consolidated 15+ disparate ERP and document repositories into a unified lakehouse with a domain graph ontology, cutting RAG hallucinations by 90%.
Key Verified Metrics & Results
90% reduction in LLM retrieval hallucination rates
Unified Single Source of Truth (SSOT) across 15+ systems
Sub-second GraphRAG context enrichment for agents
Executive Summary
A major telecommunications enterprise needed to supply deep domain context to AI customer service and network engineering agents. Vector similarity search alone proved inadequate for resolving complex structural relationships between assets, contracts, and customer accounts.
VertexCore Group engineered a Single Source of Truth (SSOT) Lakehouse combined with an Enterprise Knowledge Graph & GraphRAG Pipeline, resulting in a 90% drop in retrieval hallucinations and unified domain context across 15+ legacy systems.
The Enterprise Challenge
- Fragmented Data Silos: Domain knowledge was scattered across Snowflake, SAP ERP, Oracle databases, and unindexed PDF technical manuals.
- Vector Search Limitations: Standard vector embedding search retrieved semantically similar words but failed to maintain logical relationships (e.g., linking customer contracts to specific network hardware IDs).
- Hallucination Risks: Customer service agents frequently generated inaccurate responses due to incomplete context retrieval.
The VertexCore Group Solution
VertexCore Group constructed a modern data foundation and semantic knowledge layer:
- Single Source of Truth (SSOT) Lakehouse Integration:
- Unified structured transactional data and unstructured documentation into a high-performance Snowflake/Databricks lakehouse using dbt data pipelines.
- Domain Knowledge Graph & GraphRAG Architecture:
- Constructed enterprise graph ontologies using Neo4j and GraphQL, mapping complex entity relationships between assets, locations, and accounts.
- AI-Optimized Schemas & Hybrid Retrieval:
- Built hybrid retrieval pipelines combining keyword BM25 indexing, vector embeddings, and graph traversal for 100% accurate context injection.
Business Impact & Results
- 90% Hallucination Cut: Eliminating vector retrieval ambiguity guaranteed that AI agents respond with ground-truth accuracy.
- 15+ Data Sources Consolidated: Real-time Single Source of Truth accessible via clean GraphQL & REST semantic APIs.
- 3x Faster Incident Resolution: Network engineering agents retrieved asset dependency graphs in under 500 milliseconds.
Technology Stack
- Data Lakehouse: Databricks, Snowflake, Delta Lake, dbt
- Knowledge Graphs: Neo4j, GraphQL, RDF/OWL Ontologies
- Vector & Retrieval: pgvector, Hybrid BM25 + Dense Retrieval, Redis Cache
Executive Architecture Advisory
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