AI Integration with Relativity APIs and Scripts

Relativity's architecture is built for extension, providing three primary surfaces for AI integration: its REST API for programmatic data access and object manipulation, Event Handlers for triggering AI workflows on data changes, and Relativity Scripts for executing custom logic within the application context. This means AI can be wired to act on specific objects—like documents, fields, or custom objects—at defined moments in a review workflow, such as after ingestion, on field update, or before production.

For a production implementation, you typically architect a middleware service (often using Python or .NET) that listens for webhooks from Relativity Event Handlers. This service calls your AI models—for summarization, classification, or entity extraction—and then uses the Relativity API to write results back as field values, apply tags, or create related custom object records. Common integration patterns include:

• Batch Enrichment: Processing a saved search of documents via the API, applying AI analysis, and populating a custom field for reviewer guidance.
• Real-time Trigger: An Event Handler on a Document field change fires a webhook to an AI service that analyzes the new text and updates a Risk Score field.
• Agent Workflow: A Relativity Script presents a UI button for reviewers; when clicked, it calls an external API to summarize the current document and displays the result inline.

Governance is critical. All integrations should leverage Relativity's native audit trails and RBAC. AI services should be deployed as containerized microservices with their own logging, and prompts, model versions, and confidence scores should be stored in a dedicated Relativity custom object for traceability. Rollout follows a phased approach: start with a pilot workspace, use the API for non-destructive field writes, and implement human-in-the-loop review steps before full automation. This ensures the AI augments—rather than disrupts—the rigorous legal process Relativity is designed to support.

A production-grade AI integration for Relativity typically follows an event-driven, asynchronous pattern to avoid blocking core review workflows. The architecture centers on Relativity's REST API for CRUD operations on documents, fields, and custom objects, and its Event Handler system for triggering AI processing. Common entry points include:

• /Relativity.REST/api/ endpoints to push enriched data back into workspace fields or custom objects.
• Post-Save Event Handlers on the Document object or a custom "AI Processing Queue" object to trigger analysis when new data arrives or statuses change.
• Relativity Scripts executed via the API or console for batch operations, such as sending a set of prioritized documents to an external AI service for summarization or issue coding.

The data flow for an AI-assisted review agent often looks like this:

1. Trigger: A document is saved with a PENDING_AI_REVIEW status (set by a reviewer or an ingestion script). A Post-Save Event Handler fires.
2. Extract & Queue: The handler bundles the document's extracted text, metadata (Author, Sent Date), and Relativity Artifact ID into a JSON payload, posting it to an external message queue (e.g., AWS SQS, RabbitMQ) or directly to an AI orchestration service. This keeps Relativity responsive.
3. Process: The AI service (hosted on your infrastructure or a cloud provider) retrieves the payload, runs the document through configured models (e.g., for privilege detection, concept tagging), and generates results.
4. Write Back: Using a service account with appropriate permissions, the AI service calls the Relativity REST API to update the source document. This involves:
   • Patching choice fields (e.g., AI_Issue_Codes) with multi-select values.
   • Updating long text fields with generated summaries or extracted clauses.
   • Creating related custom object records (e.g., AI_Extracted_Entities) to store structured findings without altering the native document table.
5. Audit: All API calls and field changes are logged within Relativity's native audit trail, and the external service maintains its own execution logs for traceability.

Governance and rollout require careful planning. Start with a single-workspace pilot, using a dedicated custom object as a processing queue for better control and error handling. Implement rate limiting and retry logic in your external service to respect Relativity API thresholds. For sensitive data, ensure your AI service is deployed in a compliant environment and that prompts are configured to avoid generating sensitive information. Use Relativity's RBAC to restrict which users can trigger AI jobs or view AI-generated fields, and establish a human-in-the-loop review step for critical tags like privilege before they are used in production sets. This architecture provides the foundation for scalable, maintainable AI enhancements that feel native to legal teams.

Integrations should leverage Relativity's robust security model. AI services are invoked via the Relativity REST API using service accounts with role-based access control (RBAC) scoped to specific workspaces. All data exchanges are encrypted in transit, and sensitive PII/PHI can be processed in a private, VPC-isolated AI environment. Audit trails are maintained by logging all AI actions—such as tag creation via Relativity Scripts or object updates via Event Handlers—back to Relativity's audit system, ensuring a complete chain of custody for AI-assisted decisions.

A phased rollout mitigates risk and builds stakeholder trust. Start with a non-production workspace to validate API calls, prompt effectiveness, and output formatting. Phase 1 often targets a single, high-volume workflow like email threading analysis or initial batch tagging for a defined custodian set. Use Relativity's batch processing capabilities to queue documents for AI review, allowing for human QC of the AI's output before tags are committed. Phase 2 expands to real-time processing via Event Handlers for new document ingestion, and Phase 3 introduces more complex, multi-step agent workflows for timeline generation or privilege prediction.

Governance is maintained through a feedback loop. Establish a review committee to approve AI use cases and monitor performance metrics like precision/recall on a sample set. Implement a human-in-the-loop approval step for critical actions, such as applying Privileged tags, which can be managed through a custom Relativity object and workflow. Regular reviews ensure the AI agents align with matter strategy and evolving review protocols. This structured approach allows legal teams to harness AI's efficiency while maintaining the defensibility and control required for complex litigation and investigations.