CollieAi has made it remarkably straightforward to secure our customer support assistant. Integration was simple: we only had to change the base_url in our existing OpenAI-compatible client code, with no architecture changes needed. Its three-layer detection pipeline—combining pattern matching, ML classifiers, and semantic LLM analysis—has caught both obvious and more subtle prompt-injection attempts that a single-layer filter would likely miss. Having both input and output filtering in one proxy also gives us protection against malicious prompts as well as unsafe generated responses, which is especially important since the assistant handles real customer and trip data. On top of that, the dashboard and audit trail make it easy to review flagged requests and see exactly why something was blocked. Review collected by and hosted on G2.com.

Setting policies to balance catching genuine threats without over-flagging legitimate but unusual customer queries took some tuning early on. Free tier request limits become a real constraint once traffic grows, requiring an upgrade sooner than expected for production-level usage. Some of the more advanced features, like custom detection models, are locked behind the enterprise tier, which adds cost for teams needing deeper customization. Documentation is solid for the basics, but fine-tuning detection sensitivity for our specific domain took a bit of trial and error. Review collected by and hosted on G2.com.