AI Integration with Hyland Case Management

AI integrates into Hyland's case management solutions—like OnBase and Perceptive Content—by connecting to three primary surfaces: the case object model, the document repository, and the workflow engine. At intake, AI can triage new cases by analyzing attached documents (emails, forms, scanned records) and incoming form data to auto-populate critical fields like Case Type, Priority, and Assigned To. Within active cases, AI agents can monitor the workflow queue, summarizing lengthy case history from activity logs and attached correspondence to give agents instant context. For resolution, AI can draft standard responses, recommend next-best-actions based on similar closed cases, and automatically classify and file all related documents upon closure.

Implementation typically uses Hyland's REST APIs and webhook subscriptions to create an event-driven layer. For example, a Case Created event triggers an AI service to process the intake packet. The service calls a document intelligence model (like an LLM with RAG over your policy KB) to extract key entities, then uses the Hyland API to update the case record and route it via the Workflow Manager. This keeps the core Hyland platform as the system of record, with AI acting as an intelligent orchestrator. A production rollout starts with a single, high-volume case type (e.g., 'Customer Inquiry' or 'Claims Intake'), using a human-in-the-loop design where AI suggestions are presented to agents in a side-panel copilot interface for review and approval, ensuring control and building trust.

Governance is critical. All AI-generated content and routing suggestions should be logged in a dedicated audit object linked to the case, creating a transparent decision trail. Implement role-based access control (RBAC) so AI features are gated by user role and case sensitivity. Start with a phased rollout: first automate metadata tagging and summarization, then introduce triage suggestions, and finally deploy autonomous routing for low-risk, high-volume case patterns. This measured approach de-risks the integration, aligns with change management, and allows you to measure impact—typically reducing average handle time by prioritizing complex cases and cutting manual data entry by 30-50% for processed documents.

The integration is built on Hyland's REST API and webhook infrastructure. A typical flow begins when a new case is created or a status changes in the Hyland platform. A webhook event payload—containing the case ID, metadata, and document references—is sent to a secure, middleware orchestration layer. This layer, often built with tools like Azure Logic Apps or n8n, manages authentication, logging, and the sequential calling of AI services. It fetches the full case record and any attached documents (e.g., intake forms, correspondence, scanned records) via the Hyland API for processing.

The core AI processing involves two primary patterns: 1) Case Triage & Routing: An LLM analyzes the case description, category, and initial documents to predict complexity, suggest a priority score, and recommend assignment to a specialist queue or agent. This output is written back to custom fields in the Hyland case object via a PATCH API call. 2) History Summarization: For cases with lengthy activity logs or dense document threads, an agent summarizes key events, decisions, and open items. This summary is appended as a note or stored in a dedicated summary field, giving agents instant context. All AI calls are grounded using RAG against internal knowledge bases and policy documents to ensure accuracy and compliance.

Governance is wired into the data flow. Every AI-suggested action (like a priority change or routing suggestion) can be configured for human-in-the-loop approval via a task in the Hyland workflow or a separate dashboard. Audit trails log the original case data, the AI prompt, the model's reasoning, and the final action taken. Rollout is typically phased, starting with a single case type or pilot team, using A/B testing to compare AI-assisted routing times and resolution rates against baseline manual processes before scaling.

A production AI integration for Hyland case management is built on a secure middleware layer. This layer, often deployed as a containerized service, sits between Hyland's APIs (like the OnBase API or Perceptive Content API) and the AI models. It handles authentication, request/response logging, data masking (e.g., redacting PII before sending to an external LLM), and prompt management. This ensures all AI interactions are auditable, rate-limited, and never bypass your existing security perimeter. The AI service should be configured to process only the necessary case fields—such as CaseDescription, HistoryNotes, and attached document text—while respecting field-level security and data residency requirements.

Rollout follows a phased, risk-managed path. Phase 1 typically targets internal back-office operations, such as auto-summarizing lengthy case history for supervisors or recommending internal knowledge base articles. This happens in a "copilot" mode where AI suggestions are reviewed before action. Phase 2 introduces AI into agent-facing workflows, like real-time next-best-action prompts during case updates or automated triage into priority queues. Each phase includes A/B testing against control groups to measure impact on Average Handle Time (AHT) and First-Contact Resolution (FCR). Governance is maintained through a centralized prompt registry and regular audits of AI-generated case summaries or recommendations for accuracy and bias.

Critical to success is aligning the integration with Hyland's existing workflow and audit infrastructure. AI-generated actions—like a suggested case category or a draft response—should be logged as a system note in the case record. Any automated routing decision should be executable via Hyland's native workflow engine, maintaining a complete audit trail. This approach ensures the AI augments, rather than replaces, the governed processes your compliance and legal teams have already approved, enabling measurable efficiency gains without introducing unmanaged risk.