CollieAi Reviews & Product Details

CollieAi — real-time AI control plane and AI security platform for production LLM apps CollieAi is a real-time AI control plane that lets teams safely ship and operate LLM applications, chatbots, RAG pipelines, and AI agents (agentic AI) in production. It sits inline on every model call as a drop-in layer and unifies three things you'd otherwise stitch together from separate tools: AI security, LLM observability, and AI governance. In short, CollieAi helps you protect, observe, and govern every LLM call — without changing your application code or your model provider. Most AI guardrail and LLM security tools stop at blocking attacks. CollieAi goes further: the same inline position that blocks prompt injection also gives you full visibility into every request and response, plus the policy, access, and audit controls your security and compliance teams need. It's an AI firewall, an LLM security gateway, and an AI security posture management (AI-SPM) layer in one. HOW IT WORKS Provider-agnostic — works with any model: OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, DeepSeek, and self-hosted / open-source models. Protects agentic AI, MCP (Model Context Protocol), and tool-calling workflows. Integrate three ways: - Drop-in proxy: point your existing OpenAI-compatible client at CollieAi with a one-line change (swap the base URL) — no SDK, no rewrite. - Native SDKs for Python, Node, and .NET (bring-your-own-model), with real-time token-by-token SSE streaming. - Async API for batch and webhook-driven workloads. Every request and response is inspected bidirectionally in real time at low latency (≤20 ms median), so protection, logging, and policy enforcement happen on the live path — before anything reaches your users. PROTECT — real-time guardrails on every prompt and response A configurable rules engine runs on inbound prompts and outbound responses. For each rule you choose a direction (inbound, outbound, output, or all) and a decision (block, mask, monitor, normalize, or allow), and mix detection methods — fast pattern rules, lightweight ML models, and LLM-based reasoning — each with its own thresholds. Coverage includes: - Prompt injection and indirect (paraphrased) prompt injection protection, plus jailbreak detection - PII detection, redaction, and masking — credit cards, IBAN/BIC, SSNs, emails and more (with checksum validation) - Secrets and API-key detection (tokens, private keys, credentials) - Data loss prevention (DLP) - Output safety — block or monitor unsafe content by category (violence, self-harm, illegal acts, and more) - Profanity and sensitive-word filtering via custom, multi-language dictionaries (brand safety, competitor and sensitive terms) - Malicious-URL filtering and URL-exfiltration protection (block schemes and IP literals; allow trusted domains) - Hidden-payload detection — decode and block base64-encoded payloads and file data - Prompt normalization (NFKC, lowercase, zero-width/homoglyph removal) to defeat obfuscated attacks - Language detection to restrict unsupported or disallowed languages Full coverage of the OWASP Top 10 for LLM Applications. Start in Monitor mode to see what would be flagged, then switch to Protect when you're ready. OBSERVE — full visibility into your AI traffic - Real-time analytics: total requests, blocked, monitored, average latency, and token usage — per environment, over 24h / 7d / 30d - Requests-per-hour trends with blocked volume stacked on top - Threat breakdown: passed vs. monitored vs. blocked - Full request/response traces and logs for debugging and investigation - Anomaly detection and configurable alerts - SIEM integration to feed your existing security stack - A live Security Score summarizing your posture GOVERN — policies, access, and audit for AI - A central policy and rules library, organized by project and environment - Role-based access control (RBAC) and team management - Provider-token management and scoped API keys - Reusable dictionaries for custom filtering - Audit logging with configurable log retention - AI risk management aligned to the OWASP LLM Top 10, NIST AI RMF, ISO 42001, and the EU AI Act - Helps you address shadow AI and meet GDPR, PCI DSS, HIPAA, and SOC 2 BUILT FOR PRODUCTION - Real-time, low-latency enforcement that won't slow your app - Unlimited projects and API keys - Deploy as a managed cloud service or fully self-hosted for complete data control and residency - Configurable retention — keep what you need, drop what you don't WHO USES CollieAi Teams building and running LLM apps, chatbots, RAG systems, and AI agents in production — from early-stage startups to regulated enterprises, worldwide. Primary buyers are engineering, platform/MLOps, and security/compliance teams, with strong fit in fintech, healthcare, SaaS, gaming, and any organization handling sensitive or regulated data in AI. GET STARTED IN MINUTES A guided setup lets you pick the threats you care about — prompt injection, PII & financial data, secrets & API keys, malicious URLs, profanity, and hidden payloads — and CollieAi configures sensible defaults for you. Start free (no credit card) with a generous monthly request allowance, and scale up when you're ready. Early-stage startups can apply for 6 months of the Growth plan free.

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CollieAi Integrations

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Integration information sourced from real user reviews.

MA

Muhammed A.

Technical Project Manager

Logistics and Supply Chain

Mid-Market (51-1000 emp.)

8/15/2026

"Straightforward Integration and Strong Prompt-Injection Protection"

4.5/5

What do you like best about CollieAi?

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.

What do you dislike about CollieAi?

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.

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

CollieAi has addressed a security gap that generic application security tools weren't built for, protecting our customer support assistant from AI-specific threats like prompt injection, jailbreak attempts, and accidental PII leakage in responses. This has let us deploy the assistant to real users with more confidence, knowing there's a dedicated, low-latency layer actively filtering both what goes into the model and what comes out. Review collected by and hosted on G2.com.

WJ

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.

JS

Joseph S.

beta tester

Small-Business (50 or fewer emp.)

8/13/2026

"AI support for courses with useful automation and analytics"

4/5

What do you like best about CollieAi?

What I like most about Collie AI is that it keeps the AI focused on the actual course material instead of giving completely open ended answers. Being able to use notes, syllabi, and assignments makes it more useful for students because the responses stay relevant to the course. The analytics can also help identify common questions and areas where students may be struggling. I also like that it can integrate with existing learning and communication tools, which makes it easier to fit into an existing teaching workflow instead of creating a completely separate system.

From a performance perspective, it can handle repetitive student questions and provide support outside normal class hours. The main value from a pricing and ROI perspective is the potential time saved for instructors when the same questions come up repeatedly. The interface is fairly straightforward, and getting started is not overly complicated. The onboarding and support are useful for understanding how to set up the content and get the most out of the platform. For me, the biggest benefit is having the AI handle routine student queries so instructors can spend more time on actual teaching. Review collected by and hosted on G2.com.

What do you dislike about CollieAi?

One limitation with Collie AI is that the integration options seem fairly limited at the moment, with Telegram currently listed as the supported messaging integration. The interface is simple, but some parts could be more polished as the platform grows. I would also like more flexibility around pricing and clearer ROI information for smaller users or institutions before committing. The AI generally fits its intended purpose, but it would be helpful to have more transparency around how it handles uncertain questions and how administrators can review or control those responses. More guided onboarding examples and broader integration support would make the product easier to adopt. Review collected by and hosted on G2.com.

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

Collie AI is mainly solving the problem of repetitive student questions and the lack of support outside normal teaching hours. Instead of instructors repeatedly answering the same questions, students can ask Collie about course material and get answers based on the approved notes, syllabi, and assignments. That can reduce routine workload and give students access to help when the instructor is unavailable. The analytics can also highlight common questions and learning gaps, which can be useful when improving course material. The setup is relatively straightforward, and the integration with messaging tools such as Telegram helps fit it into an existing workflow rather than requiring students to learn another separate system Review collected by and hosted on G2.com.

NK

Nirmal K.

Manager

E-Learning

Small-Business (50 or fewer emp.)

8/12/2026

"Virtually No Refactoring—One-Line Setup to Activate CollieAi’s Firewall"

5/5

What do you like best about CollieAi?

It requires virtually no code refactoring. Because it acts as an API proxy, developers only need to change one line of code (pointing their existing OpenAI or Anthropic client's base_url to CollieAi) to activate the firewall. Review collected by and hosted on G2.com.

What do you dislike about CollieAi?

While 50ms is very fast, adding any inline proxy introduces an additional network hop and a potential single point of failure between your application backend and the core LLM provider. Review collected by and hosted on G2.com.

YM

Yousef M.

Technical Consultant

Mid-Market (51-1000 emp.)

8/14/2026

"Seamless AI-Driven Knowledge Management"

4.5/5

What do you like best about CollieAi?

I like CollieAi for its accurate semantic search across various integrations and quick response time when pulling information from company wikis. The seamless integration with Slack and Notion, along with easy permission controls that respect document access levels, works extremely well. The setup was fast and easy, connecting our main data sources tool in only a few clicks, and onboarding was very straightforward. Review collected by and hosted on G2.com.

What do you dislike about CollieAi?

I find that indexing large unstructured PDF files can sometimes take longer than expected, and I would like more customization options for search filters. Review collected by and hosted on G2.com.

UC

Verified User in Computer Software

Small-Business (50 or fewer emp.)

7/16/2026

"Visual, Plain-Text Firewall Creation That’s Easy for Non-Technical Users"

4.5/5

What do you like best about CollieAi?

I best like the making of each firewall is visual and just uses plain text which makes it easy for non-technical cybersecurity individuals. Review collected by and hosted on G2.com.

What do you dislike about CollieAi?

The recommendations offered by CollieAI are not effective in maximizing security for apps that use it. Review collected by and hosted on G2.com.