AI Conversation Monitoring for Enterprise Safety

Thinkpol uses the Model Context Protocol (MCP) to provide a tool that your LLM can use. Simply add our incident reporting tool, make a small adjustment to your system prompt, and your LLM will automatically detect and report inappropriate user behavior by calling our MCP endpoint.

Our platform supports detection of illegal activities, harassment, data fishing attempts, system manipulation, and inappropriate content. Each incident is automatically categorized and assigned a severity level (low, medium, high, critical) by your LLM.

Each AI application has configurable rate limits (default 100 incidents/hour) to prevent abuse and control costs. The system automatically tracks usage per application and returns appropriate error responses when limits are exceeded.

Yes! While we provide default categories for common incident types, you can add custom categories through the dashboard. You can also define custom metadata fields for each AI application to capture context specific to your use case.

Our system automatically detects duplicates using session_guid and message_guid fields. When a duplicate is detected, we return the existing incident ID instead of creating a new one, reducing noise in your incident queue.

Incidents appear in your triage queue where you can review details, create cases for further investigation, or reject false positives. Cases can be assigned to team members, tracked with internal notes, and managed through customizable workflow statuses.

Currently, incidents are created via our MCP API endpoint and managed through the web dashboard. We're planning to add a comprehensive REST API for retrieving and managing incidents programmatically in future updates.

All incident data is encrypted in transit and at rest. Each workspace has complete data isolation, tokens are unique per application, and we follow security best practices including rate limiting, input validation, and regular security audits.