Spark Vault
Spark Vault
Spark Vault delivers secure, on-prem enterprise search for medical documents with fast, private, AI-powered retrieval
Spark Vault Enterprise Search for Unified AI Search
Spark Vault Enterprise Search uses AI to unify data search, deliver intent-aware results, and power knowledge discovery across your enterprise for faster insight and decision-making.
Our Solution
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Executive Summary
Spark Vault is a private, on-premises enterprise search solution purpose-built for sensitive medical and healthcare documents. Running entirely on NVIDIA DGX Spark, it enables organizations to search, analyze, and extract insights from complex medical records without relying on external cloud services. By combining containerized large language models, semantic embeddings, vector and graph databases, and local inference, Spark Vault delivers fast, secure, and compliance-ready search at enterprise scale.
Challenges
Medical documents contain highly sensitive patient and clinical data that must remain protected under strict regulations such as HIPAA and internal governance standards
Lock
Data Privacy & Regulatory Compliance
Cloud-based AI search introduces data residency risks, compliance hurdles, and vendor lock-in barriers many healthcare and regulated enterprises cannot accept.
Cloud
No Cloud Dependency
Clinical notes, diagnostic reports, and scanned documents lack consistent structure, making them difficult to index, understand, and retrieve accurately.
Folder
Complex, Unstructured Medical Data
Healthcare systems manage massive volumes of documents that demand fast, low-latency search without sacrificing accuracy or system stability.
TrendingUp
Search Performance at Scale
All AI inference and data processing must execute locally to maintain full control, eliminate data egress, and meet audit requirements.
Briefcase
Secure On-Prem Inference
Solution Overview
Spark Vault addresses these challenges with a fully on-premises, containerized AI search architecture optimized for performance, privacy, and scalability: Runs entirely on NVIDIA DGX Spark with zero cloud dependency Uses local LLM inference, embeddings, and vision-language parsing Combines semantic vector search, fuzzy keyword search, and graph-based querying Maintains full data sovereignty while delivering sub-second search performance This architecture ensures sensitive medical data never leaves the organization’s infrastructure.
How it Works
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Functional Workflow
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1. Intelligent Document Ingestion
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Medical documents are ingested locally and parsed using a vision-language model with semantic chunking.
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2.Semantic Chunking & Extraction
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Documents are broken into meaningful chunks using domain-aware parsing for accurate retrieval.
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3.
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Embedding Generation
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Each chunk is converted into vector embeddings for semantic similarity search.
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Knowledge Graph Construction
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Clinical entities and relationships are modeled into a graph structure for contextual querying.
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5.Hybrid Search Execution
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User queries leverage:
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Semantic vector similarity
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Fuzzy keyword matching
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Graph-based relationship queries
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6.Instant Insight Delivery
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Results are enhanced with LLM-powered contextual insights, all generated locally.
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Business Impact
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Private Search by Design
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Sensitive medical data remains fully on-premises at all times.
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Sub-Second Query Latency
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Fast and reliable search across large document repositories.
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Improved Clinical & Operational Efficiency
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Faster access to relevant information supports better decision-making.
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Reduced Compliance Risk
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Eliminates cloud exposure and simplifies regulatory audits.
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Scalable for Enterprise Growth
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Handles increasing data volumes without performance degradation.
Key Benefits
All inference and storage run locally on DGX Spark Zero data egress ensures full control and compliance
ShieldCheck
On-Prem, Privacy-First AI
Semantic search using embeddings Keyword and metadata-based fuzzy search Graph queries for entity relationships
SearchCode
Hybrid Search Intelligence
Vision-language parsing for scanned and complex medical files Semantic chunking improves recall and precision
FileSearch
Advanced Document Understanding
Sub-second query responses across large datasets High GPU utilization for efficient throughput
Zap
Low-Latency Performance
Fully containerized microservices Scalable, modular, and production-ready
Layers
Enterprise-Grade Architecture
Key Outcomes with Spark Vault
Search
Private Search
Sensitive medical data stays fully on-premises at all times
Bolt
Low Latency
Sub-second query responses across large document collections
Rocket
Hybrid Intelligence
Semantic, keyword, and graph search combined in a single query flow
Deep Understanding
Vision-language parsing enables accurate insight from complex medical documents
Target
Enterprise Ready
Containerized, scalable architecture built for secure production deployment
Technical Foundation
NVIDIA DGX Spark
Server
Hardware
LLaMA 3.3 70B via vLLM
BrainCircuit
LLM Inference
Nomic embed model
Embeddings
PostgreSQL with pgvector and Apache AGE
Database
Vision-language parsing + semantic chunking (Chonkie)
Document Intelligence
NVIDIA NGC containers, Docker-based runtime
Package
Deployment
Hybrid semantic, keyword, and graph search
Search Paradigm
Conclusion
Spark Vault demonstrates a sophisticated enterprise search system that harnesses containerized AI models, advanced vector and graph databases, and high-performance NVIDIA DGX Spark hardware to deliver secure, private, and highly responsive search capabilities. Its hybrid semantic and graph querying uniquely position it as a best-in-class, on-premises solution for medical and sensitive data environments.
Deploy Spark Vault: A Privacy-First, On-Prem Enterprise Search Platform
Discover secure, compliant AI search powered locally on NVIDIA DGX Spark, schedule a demo and experience next-level search intelligence.
Book a Demo
https://calendly.com/contact-genaiprotos/3xde
Spark Vault
Executive Summary
Spark Vault is a private, on-premises enterprise search solution purpose-built for sensitive medical and healthcare documents. Running entirely on NVIDIA DGX Spark, it enables organizations to search, analyze, and extract insights from complex medical records without relying on external cloud services. By combining containerized large language models, semantic embeddings, vector and graph databases, and local inference, Spark Vault delivers fast, secure, and compliance-ready search at enterprise scale.
Challenges
Solution Overview
Spark Vault addresses these challenges with a fully on-premises, containerized AI search architecture optimized for performance, privacy, and scalability: Runs entirely on NVIDIA DGX Spark with zero cloud dependency Uses local LLM inference, embeddings, and vision-language parsing Combines semantic vector search, fuzzy keyword search, and graph-based querying Maintains full data sovereignty while delivering sub-second search performance This architecture ensures sensitive medical data never leaves the organization’s infrastructure.
How it Works
Functional Workflow
1. Intelligent Document Ingestion
Medical documents are ingested locally and parsed using a vision-language model with semantic chunking.
2.Semantic Chunking & Extraction
Documents are broken into meaningful chunks using domain-aware parsing for accurate retrieval.
3.Embedding Generation
Each chunk is converted into vector embeddings for semantic similarity search.
4.Knowledge Graph Construction
Clinical entities and relationships are modeled into a graph structure for contextual querying.
5.Hybrid Search Execution
- User queries leverage:
- Semantic vector similarity
- Fuzzy keyword matching
- Graph-based relationship queries
6.Instant Insight Delivery
Results are enhanced with LLM-powered contextual insights, all generated locally.
Business Impact
- Private Search by Design Sensitive medical data remains fully on-premises at all times.
- Sub-Second Query Latency Fast and reliable search across large document repositories.
- Improved Clinical & Operational Efficiency Faster access to relevant information supports better decision-making.
- Reduced Compliance Risk Eliminates cloud exposure and simplifies regulatory audits.
- Scalable for Enterprise Growth Handles increasing data volumes without performance degradation.
Key Benefits
Key Outcomes with Spark Vault
Private Search
Sensitive medical data stays fully on-premises at all times
Low Latency
Sub-second query responses across large document collections
Hybrid Intelligence
Semantic, keyword, and graph search combined in a single query flow
Deep Understanding
Vision-language parsing enables accurate insight from complex medical documents
Enterprise Ready
Containerized, scalable architecture built for secure production deployment
Technical Foundation
Hardware
NVIDIA DGX Spark
LLM Inference
LLaMA 3.3 70B via vLLM
Embeddings
Nomic embed model
Database
PostgreSQL with pgvector and Apache AGE
Document Intelligence
Vision-language parsing + semantic chunking (Chonkie)
Deployment
NVIDIA NGC containers, Docker-based runtime
Search Paradigm
Hybrid semantic, keyword, and graph search
Conclusion
Spark Vault demonstrates a sophisticated enterprise search system that harnesses containerized AI models, advanced vector and graph databases, and high-performance NVIDIA DGX Spark hardware to deliver secure, private, and highly responsive search capabilities. Its hybrid semantic and graph querying uniquely position it as a best-in-class, on-premises solution for medical and sensitive data environments.
