Engineered for Autonomy
PlanetGraph is an AI-native graph database built for relationship intelligence. These use cases show how teams use graph reasoning to ground agent decisions, discover multi-hop insights, and ship production AI applications faster.
Discover warm introductions, expertise networks, and hidden relationship paths that accelerate high-trust outreach.
AI Agent Task
Find three potential introduction paths to this target buyer with strongest trust signals.
Ground RAG and agent workflows in explicit entities and relationships for more reliable answers.
AI Agent Task
Summarize technical risks across the last six postmortems and map each to owners and impacted systems.
Expose hidden fraud rings and suspicious network behavior with cycle and cluster analysis.
AI Agent Task
Monitor this account network for laundering indicators and high-risk transaction loops.
Deliver high-intent recommendations using both similarity and relationship context.
AI Agent Task
Recommend top candidates for this role using prior team compatibility and project graph proximity.
Unify fragmented records into durable entity profiles across systems and time.
AI Agent Task
Evaluate whether these four records are the same entity and provide confidence-backed merge rationale.
Map N-tier dependencies, provenance, and disruption risk across suppliers and logistics nodes.
AI Agent Task
Estimate impact on Q4 shipments if this tier-2 supplier goes offline for 14 days.
Track attacker infrastructure and campaign relationships to improve detection and response.
AI Agent Task
Show likely blast radius from this compromised host and rank assets by immediate exposure.
Understand how influence, expertise, and execution actually flow through your company.
AI Agent Task
Recommend a cross-functional working group to unblock this initiative in under two weeks.
Model founder, investor, and company relationships to improve sourcing and diligence.
AI Agent Task
Rank top 20 startups by founder quality and network momentum in this vertical.
Detect coordinated inauthentic behavior and harmful cascades before they scale.
AI Agent Task
Identify origin nodes and top amplifiers for this harmful content cascade.
Uncover beneficial ownership and portfolio dynamics across property ecosystems.
AI Agent Task
Find hidden ownership links between these portfolios and flag concentration risks.
Track idea flow, collaborations, and emerging fields across the research landscape.
AI Agent Task
Find adjacent research communities likely to contribute to this emerging topic.
Model disease, treatment, and outcome relationships for stronger clinical intelligence.
AI Agent Task
Suggest potential repurposing targets based on shared molecular and outcome relationships.
Strengthen sanctions screening and AML controls with relationship-aware monitoring.
AI Agent Task
Assess sanctions exposure for this transaction chain and highlight key risk entities.
Model service dependencies and lineage to improve resilience and deployment confidence.
AI Agent Task
Simulate failure of this core service and return ordered downstream impact list.
Match people and opportunities through skills, project history, and collaboration graphs.
AI Agent Task
Recommend three internal candidates with fastest path to readiness for this role.
Track market structure changes through funding, partnerships, and leadership movement.
AI Agent Task
Identify likely strategic moves by these competitors over the next two quarters.
Model social networks and in-game economies for healthier communities and balanced systems.
AI Agent Task
Flag possible coordinated boosting behavior and produce confidence-scored evidence paths.
Choose an architecture that preserves both semantic meaning and relationships.
| Feature | PlanetGraph | Relational | Vector DB |
|---|---|---|---|
| Data Model | Relationships as First-Class | Tables and Joins | Coordinates and Embeddings |
| Reasoning Ability | Multi-Hop Logic | Static Querying | Similarity Only |
| Performance at Scale | O(1) Traversal | Join Cost Growth | Search-Heavy |
| Explainability | Deterministic Paths | Query Tracing | Distance Metrics |
Pick the right data engine for your agent's job.
Choose when semantic similarity is the primary retrieval mode.
Choose when your agent must reason over connected entities and multi-hop paths.
Choose when keyword ranking on mostly unstructured text is enough.
Graph databases reduce hallucinations, expose explainable reasoning paths, and let agents act with live relationship context across your whole data landscape.
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