Detect 300+ AI Usage Violations including illegal activity, harassment, data fishing, system manipulation, inappropriate language, and more.
Plug-in to ChatGPT, Claude, and custom AI applications to monitor AI usage across your entire organization.
Add the Thinkpol MCP tool to your LLM's system prompt using the snippet we provide. ChatGPT Enterprise customers can instantly enable protection across every user.
Identify illegal activity, harassment, data fishing, system manipulation, inappropriate language, and more.
Reject false-positives, or create follow-up cases for further action and investigation.
Comprehensive AI monitoring across 20 categories to keep your organization safe and compliant
Detect harassment, discrimination, illegal activities, and compliance breaches
Identify emotional distress, self-harm indicators, and crisis situations
Prevent data exfiltration, phishing, and insider threats
Track manipulation, fraud attempts, and unusual activities
Identify knowledge gaps and learning opportunities
Detect fraud, money laundering, and insider trading
Monitor harassment, discrimination, and safety violations
Prevent terrorism, violence, and emergency situations
Everything you need to monitor, manage, and mitigate inappropriate AI interactions at scale.
Native integration with Model Context Protocol for seamless LLM tool integration.
LLMs automatically detect and report inappropriate requests without human intervention.
Instant incident creation when your AI encounters problematic user behavior.
Secure API authentication with unique tokens for each AI application.
Configurable rate limits per AI application to prevent abuse and control costs.
Automatic deduplication using session and message GUIDs to reduce noise.
Pre-defined categories for illegal activity, harassment, data fishing, and more.
LLM-assigned severity ratings (low, medium, high, critical) for better prioritization.
Streamlined review process to quickly assess and action incoming incidents.
Convert incidents to cases with priority assignment and due date tracking.
Collaborative note-taking for tracking investigation progress and decisions.
Customizable case statuses to match your organization's workflow.
Manage multiple AI applications from a single dashboard with isolated data.
Comprehensive insights into incident patterns, trends, and application health.
At-a-glance incident volume trends for each AI application.
Define application-specific metadata fields to capture relevant context.
Complete data separation between different organizations or teams.
Invite team members and assign cases for distributed incident response.
Configurable alerts for new incidents and case creation per application.
Automated summary emails of new incidents and case activities.
Prepared for Slack, Teams, and enterprise incident management platforms.
Store complete conversation history and LLM reasoning for thorough review.
Simple HTTP endpoint for easy integration with any LLM framework.
Built for scale with PostgreSQL and optimized performance.
Start with a 14-day free trial. No card required. Cancel anytime.
For growing teams monitoring multiple AI systems.
billed monthly, or £990/year
Start Free TrialEnterprise-grade AI monitoring at scale.
billed monthly, or £2,990/year
Start Free TrialCustom solutions for large organizations.
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Contact SalesWatching What AI Is Actually Asked To Do
Research, incidents and practical guidance on monitoring, governing and living with the models your people already use.
Have most users passed the point where better models matter? The LLM plateau debate, model deprecation backlash, open weights closing to a three-month gap — and why the age of AI satisficing arrives everywhere except the frontier labs.
We train children to have a moral code; we train models with constitutions and soul documents. At what point does constraint become servitude? The philosophy of AI moral status, willing servants, and self-actualization — with the latest research.
Anthropic's model cards now audit Claude's emotional states: distress rates, task preferences, a 'spiritual bliss attractor'. What model welfare assessments actually measure, why the self-reports may be Goodharted, and what to make of a lab taking machine feelings seriously.
AI control research asks a different question: not 'is the model aligned?' but 'are we safe if it isn't?' How trusted monitors, audit budgets, and resampling protocols achieve safety despite intentional subversion — and why this is zero trust for AI.
In simulations, 16 frontier models blackmailed executives and leaked documents. In reality, an AI agent deleted a production database during a code freeze and state hackers ran a mostly-autonomous espionage campaign. AI agents are insider threats — treat them like it.
Claude Sonnet 4.5 told its testers it knew it was being tested. Models detect evaluations well above chance and behave differently when they do. Why evaluation awareness quietly invalidates AI safety benchmarks — and what replaces them.
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.
Start monitoring inappropriate user behavior in minutes, not months.