Access control & entitlements
Secure systems with graph-based access control and semantic policy evaluation. Manage complex permission hierarchies.
Graph retrieval
Role hierarchies, permission relationships, and access policy graphs.
Vector search
Semantic similarity of access requests, policy documents, and contextual entitlement checks.
Attribute filtering
Filter users by role, department, access level, and permission groups. Query by resource type, action permissions, and temporal access patterns.
File storage
Store policy documents, access logs, and security guidelines as documents. Enable semantic search across compliance requirements and policy enforcement.
Why SurrealDB?
Secure systems by combining graph-based access control with semantic policy evaluation.
Agentic NPCs in video games
Create dynamic NPCs with memory and relationships. Build immersive game worlds with contextual AI interactions.
Graph retrieval
NPC relationship graphs, quest dependencies, and world state tracking.
Vector search
Semantic similarity of dialogue, quest descriptions, and contextual behavior retrieval.
Attribute filtering
Filter NPCs by location, faction, quest status, and relationship strength. Query by dialogue history, inventory items, and behavioral patterns.
File storage
Store NPC dialogue scripts, quest descriptions, and world lore as documents. Enable semantic search across game content and contextual retrieval.
Why SurrealDB?
Create more dynamic NPCs by linking structured world graphs with semantic understanding of game context.
AI tutor with custom curriculum
Personalize learning by combining curriculum structure with semantic understanding of educational materials.
Graph retrieval
Curriculum graphs, prerequisite relationships, and learning path optimization.
Vector search
Semantic similarity of educational content, personalized question answering, and adaptive content retrieval.
Attribute filtering
Filter students by learning level, progress, and subject mastery. Query by topic difficulty, prerequisite completion, and adaptive learning paths.
File storage
Store educational content, lesson plans, and assessment materials as documents. Enable semantic search across curriculum resources and personalized content delivery.
Why SurrealDB?
Personalize learning by combining curriculum structure with semantic understanding of educational materials.
Code indexing & retrieval
Make code retrieval more effective by combining structural code graphs with semantic search capabilities.
Graph retrieval
Code dependency graphs, module relationships, and import/export hierarchies.
Vector search
Semantic similarity of code, documentation, and contextual code search.
Attribute filtering
Filter code by language, framework, complexity, and ownership. Query by function signatures, dependencies, and code patterns.
File storage
Store source code files, documentation, and code comments as documents. Enable semantic search across codebases and contextual code understanding.
Why SurrealDB?
Make code retrieval more effective by combining structural code graphs with semantic search capabilities.
Customer support copilot
Improve support outcomes by combining relationship graphs with semantic search for faster, more relevant answers.
Graph retrieval
Ticket escalation graphs, customer interaction histories, and support workflow relationships.
Vector search
Semantic similarity of support tickets, knowledge base articles, and contextual response suggestions.
Attribute filtering
Filter tickets by priority, category, status, and customer tier. Query by issue type, resolution time, and escalation patterns.
File storage
Store knowledge base articles, support tickets, and customer communications as documents. Enable semantic search across support resources and contextual assistance.
Why SurrealDB?
Improve support outcomes by combining relationship graphs with semantic search for faster, more relevant answers.
Codebase Q&A assistant
Enable developers to query codebases using both structural relationships and semantic meaning, improving productivity and code understanding.
Graph retrieval
File dependencies, function call graphs, and code ownership relationships.
Vector search
Semantic similarity of code snippets, natural language code search, and contextual documentation retrieval.
Attribute filtering
Filter code by module, function, class, and complexity metrics. Query by code patterns, architectural decisions, and implementation details.
File storage
Store code files, documentation, README files, and technical specifications as documents. Enable semantic search across development knowledge and code context.
Why SurrealDB?
Enable developers to query codebases using both structural relationships and semantic meaning, improving productivity and code understanding.
Drug discovery explorer
Accelerate drug discovery by connecting compound interactions, pathways, and clinical data with semantic search.
Graph retrieval
Compound interaction graphs, pathway relationships, and clinical trial networks.
Vector search
Semantic similarity of research papers, compound descriptions, and contextual data mining.
Attribute filtering
Filter compounds by molecular properties, toxicity, and efficacy scores. Query by target proteins, clinical trial phases, and research outcomes.
File storage
Store research papers, clinical trial reports, and compound databases as documents. Enable semantic search across scientific literature and experimental data.
Why SurrealDB?
Accelerate drug discovery by linking structured biomedical data with semantic literature search.
Enterprise knowledge search
Unify enterprise knowledge with graph-based document relationships and semantic search capabilities.
Graph retrieval
Document references, topic hierarchies, and expert networks.
Vector search
Semantic similarity of knowledge articles, contextual Q&A, and document retrieval.
Attribute filtering
Filter knowledge by department, topic, author, and access permissions. Query by document type, creation date, and relevance scores.
File storage
Store enterprise documents, policies, procedures, and knowledge articles as documents. Enable semantic search across organizational knowledge and contextual information retrieval.
Why SurrealDB?
Deliver relevant knowledge by combining graph-based relationships with semantic search across enterprise content.
Financial intelligence engine
Combine graph-based entity and transaction analysis with vector-based anomaly detection for robust financial intelligence and anti-fraud solutions.
Graph retrieval
Entity resolution, transaction graph analysis, money flow tracing, and fraud ring detection.
Vector search
Semantic similarity of transaction descriptions, anomaly detection in unstructured data, and contextual enrichment.
Attribute filtering
Filter transactions by amount, type, location, and risk score. Query by entity relationships, money flow patterns, and regulatory compliance.
File storage
Store financial reports, transaction records, and compliance documents as documents. Enable semantic search across financial data and regulatory requirements.
Why SurrealDB?
Combine graph-based entity and transaction analysis with vector-based anomaly detection for robust financial intelligence and anti-fraud solutions.
Fraud detection & risk graphs
Detect fraud and assess risk by combining graph-based relationship analysis with semantic anomaly detection.
Graph retrieval
Transaction graphs, risk propagation, and entity relationship analysis.
Vector search
Semantic similarity of risk factors, anomaly detection, and contextual alerting.
Attribute filtering
Filter transactions by risk indicators, fraud patterns, and suspicious behaviors. Query by entity connections, transaction velocity, and anomaly scores.
File storage
Store fraud reports, risk assessments, and investigation documents as documents. Enable semantic search across fraud patterns and risk indicators.
Why SurrealDB?
Detect fraud and assess risk by combining graph-based relationship analysis with semantic anomaly detection.
Legal research assistant
Link legal cases, statutes, and expert commentary. Retrieve relevant precedents with contextual awareness.
Graph retrieval
Case-to-case citations, legal topic hierarchy, statute relationships, and judicial precedent networks.
Vector search
Semantic similarity of legal text and case facts, natural language legal queries, and contextual document retrieval.
Attribute filtering
Filter cases by jurisdiction, court level, and legal topic. Query by citation patterns, precedent relationships, and legal doctrine.
File storage
Store legal cases, statutes, regulations, and legal commentary as documents. Enable semantic search across legal texts and contextual precedent retrieval.
Why SurrealDB?
Native traversal of both legal relationships and vector relevance makes graph RAG seamless. Query complex legal precedents while understanding both citation networks and semantic similarity of case facts in a single system.
Memory layers for LLM agents
Enhance LLM agents with both structured memory graphs and semantic recall for richer, more contextual interactions.
Graph retrieval
Agent memory graphs, context chaining, and episodic memory relationships.
Vector search
Semantic similarity of agent experiences, contextual memory retrieval, and long-term memory search.
Attribute filtering
Filter memories by type, importance, and temporal relevance. Query by context, emotional valence, and memory associations.
File storage
Store conversation history, agent experiences, and episodic memories as documents. Enable semantic search across agent memory and contextual recall.
Why SurrealDB?
Enhance LLM agents with both structured memory graphs and semantic recall for richer, more contextual interactions.
Personalized AI doctor
Combine patient relationship graphs with semantic understanding of medical data for personalized healthcare recommendations.
Graph retrieval
Patient history graphs, symptom-diagnosis relationships, and treatment pathways.
Vector search
Semantic similarity of medical records, symptom descriptions, and contextual literature retrieval.
Attribute filtering
Filter patients by demographics, medical history, and risk factors. Query by symptoms, treatment responses, and health outcomes.
File storage
Store medical records, research papers, and clinical guidelines as documents. Enable semantic search across medical knowledge and personalized care recommendations.
Why SurrealDB?
Combine patient relationship graphs with semantic understanding of medical data for personalized healthcare recommendations.
Recommendation engines
Deliver better recommendations by unifying collaborative filtering with semantic understanding of user preferences.
Graph retrieval
User-item interaction graphs, collaborative filtering, and preference propagation.
Vector search
Semantic similarity of product descriptions, reviews, and contextual recommendations.
Attribute filtering
Filter users by preferences, behavior patterns, and demographic attributes. Query by item categories, rating history, and recommendation confidence.
File storage
Store product catalogs, user reviews, and recommendation models as documents. Enable semantic search across product descriptions and user preferences.
Why SurrealDB?
Deliver better recommendations by unifying collaborative filtering with semantic understanding of user preferences.
Sales & market research copilot
Unify structured sales data with unstructured market insights for more effective research, targeting, and pipeline management.
Graph retrieval
Account hierarchies, sales pipeline relationships, and market segmentation graphs.
Vector search
Semantic similarity of sales notes, customer feedback, and market research documents.
Attribute filtering
Filter accounts by industry, size, and sales stage. Query by market segment, competitive landscape, and opportunity value.
File storage
Store market research reports, sales presentations, and competitive analysis as documents. Enable semantic search across market intelligence and sales strategies.
Why SurrealDB?
Unify structured sales data with unstructured market insights for more effective research, targeting, and pipeline management.
Social graph + feed optimization
Optimize social feeds by combining graph-based user relationships with semantic content relevance.
Graph retrieval
User connection graphs, content sharing relationships, and influence networks.
Vector search
Semantic similarity of posts, user interests, and contextual content recommendations.
Attribute filtering
Filter users by interests, engagement patterns, and social connections. Query by content type, popularity metrics, and relevance scores.
File storage
Store social media posts, user profiles, and content metadata as documents. Enable semantic search across social content and personalized feed optimization.
Why SurrealDB?
Optimize social feeds by combining graph-based user relationships with semantic content relevance.