Factual Graph
Summary — Factual Graph for Enterprise and Government
Generative AI Grounded in Verified Entities and Relationships
BrightQuery delivers Factual Graph — Generative AI systems built on verified real-world entities and trusted relationships, not just unstructured text. By combining enterprise knowledge graphs from Neo4j large-scale entity resolution from BrightQuery, and production GenAI orchestration using Meta’s Llama Stack organizations can deploy AI that is accurate, explainable, and audit-ready.
Traditional AI systems answer questions based on statistical language patterns. BrightQuery’s approach enables AI to reason over who is connected to whom, how, and why, using persistent identities and verified networks. This dramatically reduces hallucinations and improves decision quality in high-stakes environments.
Our platform is designed for enterprise and government at scale, supporting hundreds of thousands to millions of users, with built-in security, governance, and compliance controls. It enables organizations to unify fragmented data, detect complex risk patterns, automate investigations, and accelerate research — all while maintaining transparency and accountability.
BrightQuery supports mission-critical use cases including fraud and financial crime detection, procurement and vendor risk, supply-chain intelligence, healthcare analytics, regulatory compliance, and secure productivity tools for public servants and enterprise teams.
With BrightQuery Neo4j and Llama Stack working together, AI becomes not just powerful — but trustworthy, defensible, and operationally ready.
Enterprise-Scale Generative AI Grounded in Verified Entities
BrightQuery delivers Factual Graph — Generative AI systems grounded in verified real-world entities and relationships, not just unstructured text. By combining enterprise knowledge graphs from Neo4j, large-scale entity resolution from BrightQuery, and production GenAI orchestration using Meta’s Llama Stack BrightQuery enables organizations to deploy trustworthy, explainable, and auditable AI across mission-critical workflows.
This architecture is designed for environments where accuracy, compliance, and transparency matter, including government, financial services, healthcare, supply chain, research, and highly regulated enterprise operations.
Why Factual Graph?
Traditional GenAI systems rely heavily on statistical language models and document similarity. While powerful, these approaches can introduce:
- Hallucinated facts,
- Inconsistent answers across systems,
- Limited traceability back to authoritative sources.
BrightQuery’s Factual AI approach anchors every AI response to:
- Resolved real-world entities (people, companies, organizations, locations, assets)
- Verified relationships and networks
- Source-level provenance and lineage
This allows AI to reason over what is true in the data, not just what is most likely to appear in text.
Architecture Overview
BrightQuery’s platform integrates three production-grade layers:
Enterprise Knowledge Graph — Powered by Neo4j
Neo4j Enterprise Edition serves as the system of record for relationship intelligence. It provides:
- Native graph storage optimized for network traversal
- Horizontal scalability and clustering
- Advanced graph algorithms
- Real-time pattern detection
This enables modeling and analysis of corporate ownership and control, transaction networks, supply chains, social and organizational relationships, and cross‑program participation and risk exposure.
Entity Resolution and Data Fabric — Powered by BrightQuery
BrightQuery unifies fragmented data into persistent identities using its BQID (BrightQuery Identifier) framework. Capabilities include:
- Probabilistic and deterministic identity matching
- Cross-source entity reconciliation
- Relationship inference and validation
- Data standardization and normalization
- Source-level provenance tracking
The resulting identity graph enables accurate cross-system analytics, longitudinal tracking, and reliable AI grounding for high-stakes use cases such as fraud detection, vendor risk analysis, and compliance monitoring.
Generative AI Orchestration — Powered by Meta Llama Stack
BrightQuery uses Meta’s Llama Stack to orchestrate GenAI workflows at enterprise scale. Llama Stack provides:
- Production-grade LLM execution
- Retrieval-augmented generation (RAG) pipelines
- Tool calling and workflow chaining
- Secure, scalable inference frameworks
- Integration with enterprise authentication and logging
Llama models and Llama Stack are deployed globally across enterprise and public-facing applications supporting very large concurrent user populations, making them suitable for both government shared services and large enterprise platforms.
GraphRAG: AI That Reasons Over Relationships
BrightQuery integrates GraphRAG — graph-native retrieval-augmented generation — by combining:
- Graph traversal in Neo4j to identify relevant entities and networks
- Vector search for document and narrative context
- Entity-level filtering and policy constraints
GraphRAG dramatically improves:
- Accuracy
- Explainability
- Resistance to hallucination
This enables AI systems to answer questions such as:
- Who controls this company and its subsidiaries?
- Which vendors are connected through shared ownership or addresses?
- What programs has this individual or organization participated in?
Because responses are grounded in verified relationships, not just document similarity.
Built for Enterprise and Government at Scale
The BrightQuery platform is designed to support:
- Hundreds of thousands to millions of users
- Multi-agency and multi-business-unit deployments
- High-availability, fault-tolerant operations
- Data segmentation and tenant isolation
All components are:
- Commercially supported
- Production proven
- Suitable for mission-critical workloads
This makes the architecture appropriate for both:
- Large enterprises with global operations
- Federal, state, and local government shared services environments
Security, Privacy, and Compliance by Design
Data Protection
- Encryption in transit and at rest
- Field-level masking and tokenization
- Dataset-level and entity-level access controls
Identity and Access Management
- Integration with enterprise and government IAM systems
- Role-based and attribute-based access controls
Audit and Monitoring
- Centralized logging
- AI usage tracking
- Data lineage and provenance reporting
These controls support compliance with data privacy regulations, financial compliance frameworks, healthcare data protection requirements, and government security standards.
Applications Across Enterprise and Public Sector
Fraud, Waste, and Abuse Detection
- Identity resolution across systems
- Network-based fraud pattern detection
- Cross-program risk correlation
Procurement and Vendor Risk
- Beneficial ownership analysis
- Shell company detection
- Conflict-of-interest identification
Financial Crime and Compliance
- Transaction network monitoring
- Sanctions and exposure analysis
- Case prioritization and investigation support
Supply Chain Intelligence
- Supplier dependency mapping
- Disruption impact modeling
- Counterparty risk analysis
Policy, Research, and Economic Analysis
- Cross-dataset trend analysis
- Evidence-grounded AI summarization
- Transparent sourcing and citations
Secure Productivity Tools
- AI-assisted research and reporting
- Case file summarization
- Knowledge discovery across systems
Designed for Shared Services and Federated Environments
BrightQuery’s architecture supports:
- Centralized data and AI services
- Federated agency or business-unit control
- Policy-based data sharing
- Cross-domain analytics without data duplication
This enables organizations to:
- Reduce duplicated systems
- Improve data consistency
- Accelerate AI adoption safely
Why BrightQuery
BrightQuery is not a generic AI platform provider. We specialize in:
- Entity intelligence
- Relationship analytics
- Data grounding for AI
Our mission is to make AI:
- More factual
- More explainable
- More trustworthy
By combining Neo4j’s enterprise knowledge graph, Meta’s Llama Stack GenAI orchestration , and BrightQuery’s identity and data fabric, we deliver production-ready AI systems designed for the realities of enterprise and government operations.
