Use Cases for AutoGraph
Real-world enterprise use cases for Arango’s AutoGraph copilot and comparison with traditional RAG approaches, including business benefits and practical applications
AutoGraph enables AI agents and co-pilots across enterprise workflows where contextual retrieval and domain-aware reasoning support human decisions.
Enterprise Knowledge Assistants
Scenario: 10,000+ documents across internal wikis and documentation repositories. Employees waste hours searching for information, often finding outdated or conflicting answers.
AutoGraph Solution: AutoGraph discovers 15-20 natural domains (HR policies, engineering docs, sales playbooks, etc.) and processes each domain with an appropriate strategy. Employees ask questions in natural language, and the two-stage retrieval system finds relevant partitions and answers. All answers include citations to actual policy documents or technical specs.
Business impact:
- Search time reduced from hours to seconds
- Consistent responses across teams
- Faster onboarding for new employees
Support Engineering Co-Pilots
Scenario: Connect runbooks, product docs, troubleshooting guides, knowledge articles across multiple product lines. Support teams juggle multiple knowledge bases—product docs, known issues, past tickets, configuration databases, and runbooks. Finding relevant context requires extensive institutional knowledge.
AutoGraph Solution: Domain discovery automatically separates product lines and document types, giving support agents relevant context for customer issues. Local search finds specific troubleshooting steps, while global search provides product overview context. Custom retrievers can query ticket history and logs to deliver comprehensive support assistance.
Business impact:
- 40-60% reduction in resolution time and average handle time
- 30% improvement in first-contact resolution
- 25% fewer escalations to engineering
- Agent confidence increased significantly
- New agents productive in days, not months
Product Engineering & Technical Documentation
Scenario: Engineering teams maintain massive repositories—design docs, specifications, architecture diagrams, code, and defect history. Engineers spend significant time navigating complexity to understand dependencies or trace historical decisions.
AutoGraph Solution: AutoGraph automatically clusters documentation by product component and layer (frontend, backend, infrastructure). Engineers can query for dependencies, design decisions, or historical context. Deep Search connects documentation with code repositories, defect databases, and issue trackers, preserving institutional knowledge as documentation.
Business impact:
- Investigation time reduced from hours to minutes
- 50% reduction in root cause analysis time
- Better design reuse (find existing components)
- Reduced defects from historical learning
- New engineer onboarding accelerated 3x
Compliance & Risk Management
Scenario: Organize policy documents, regulatory requirements, audit procedures across multiple jurisdictions. Financial institutions must detect anomalies and ensure compliance across vast transaction datasets and regulatory documents.
AutoGraph Solution: AutoGraph discovers domains by jurisdiction and policy type, then imports compliance policies while connecting to transaction databases via custom retrievers. The AI retrieves relevant policies with full regulatory context and identifies suspicious patterns. Audit trails show reasoning for all decisions, and citations provide compliance evidence. When regulations update, only the affected partitions need to be updated.
Business impact:
- Regulatory compliance verification time reduced 70%
- 35% improvement in true positive detection
- 50% reduction in false positives
- Faster investigations with complete context
- Complete audit trails for all decisions
Research & Development
Scenario: R&D teams analyze research papers, clinical trial data, and regulatory documents to identify patterns and development pathways.
AutoGraph Solution: AutoGraph processes research documents, creating knowledge graphs that connect related studies. Researchers can query complex relationships across the entire corpus to identify patterns and insights.
Business impact:
- Accelerates research insights
- Reduces risk of missing critical connections
- Improves decision-making speed
Legal Document Analysis
Scenario: Law firms analyze case law, legal precedents, and client documents to build comprehensive legal strategies.
AutoGraph Solution: AutoGraph processes legal documents, creating graphs that understand precedents, case relationships, and argument patterns. For example, when asked “How have similar contract disputes been resolved in different jurisdictions?”, AutoGraph maps precedent relationships and jurisdictions, showing actual legal reasoning chains.
Business impact:
- Improves case preparation quality
- Reduces research time
- Enables more comprehensive strategies
Infrastructure Management
Scenario: IT teams manage complex infrastructures—networks, servers, applications, dependencies. When incidents occur, operators must correlate data from monitoring, asset databases, and runbooks.
AutoGraph Solution: AutoGraph unifies runbooks while enabling Deep Search across infrastructure topology and real-time telemetry. The co-pilot provides context, impact analysis, and remediation steps to operators.
Business impact:
- 70% reduction in mean time to resolution
- Predict failures before user impact
- Understand blast radius before changes