G2

Willem J.

Senior AI Engineer

Mid-Market (51-1000 emp.)

6/4/2026

"Great AI Firewall with smart guardrails - Prompt Injection, PII, Content Moderation, and Sandboxbox"

5/5

What do you like best about CollieAi?

What I value most is how quickly CollieAI let us put real guardrails around our LLM features without building everything in-house.

We had it deployed and protecting real traffic remarkably fast, and the support has been excellent throughout - the team is hands-on and responsive whenever we need them. We use it as an AI firewall in front of our chatbot and agent workflows - prompt injection and jailbreak attempts get caught before they ever reach the model, and PII redaction on both inputs and outputs works out of the box.

The detection itself is genuinely smart: CollieAI models catch obfuscated and multi-step attacks that simple keyword filters miss, with a low false-positive rate so legitimate traffic isn't disrupted.

The standout for me is flexibility - the content filtering policies are highly configurable (custom rules, wording, allow/deny lists and detection thresholds you can tune per use case), and policy enforcement is just as granular, almost like policy-as-code, applied consistently across every request.

On top of that, the pricing is significantly lower than the better-known vendors in this space, yet you get more features and far more configuration flexibility for the money. Latency overhead is low enough that users don't notice, the dashboard gives clear visibility into what's blocked and why, and integration with our existing stack took days, not weeks. Review collected by and hosted on G2.com.

What do you dislike about CollieAi?

Really very little. If I had to name something, a few more pre-built policy templates would make ramping up new teammates even faster - but that's about it. Everything core works well and the team is quick to help whenever we have a question. Review collected by and hosted on G2.com.

What problems is CollieAi solving and how is that benefiting you?

We ship AI-agents-powered features into production, and that opens up a whole class of risks a normal app security stack doesn't cover.

CollieAI solves them in one platform. On the input side it blocks prompt injection and jailbreak attempts - including indirect prompt injection coming through retrieved documents and tool calls in our agent workflows - before they reach the model.

On the data side, PII detection and redaction stops sensitive information (names, emails, payment and account data) from leaking into prompts, logs, or model responses, which keeps us aligned with GDPR and our own data-handling policies.

On the output side it handles content moderation and unsafe-output filtering - toxicity, off-topic responses, and leaked system prompts get caught and rewritten or blocked according to the policy we define. The wording and policy controls are flexible enough that we tune all of this per use case instead of accepting one rigid ruleset.

One feature our security team especially values is the sandbox: any file an agent ingests - PDFs, images, and other document types - is scanned not just for malware and file legitimacy, but also for hidden prompt injections, jailbreaks, and embedded attempts to manipulate the system through the content itself. That closes off a file-based attack vector most tools miss entirely. CollieAI also integrates directly with our SIEM, so every blocked event and security signal flows into the same pipeline our Cyber Security department already monitors - no separate console to babysit.

Practically, CollieAI acts as our AI firewall and LLM security gateway, and it maps cleanly to the OWASP Top 10 for LLM Applications, so when security or compliance asks how we cover those risks we have a concrete answer plus audit-ready logs of every blocked event.

The benefit is that security stopped being the bottleneck on our roadmap: we ship AI-agent features faster, with real-time protection, low latency, and the confidence that prompt injection, data leakage, malicious files, and unsafe output are handled consistently across every model and endpoint we run. Review collected by and hosted on G2.com.