Add PolicyAI to README alongside ActiveFence

Author: vitchor

README.md

@@ -295,9 +295,9 @@ Evaluating the safety of a LLM-based conversational application is a complex tas
 
 ## How is this different?
 
-There are many ways guardrails can be added to an LLM-based conversational application. For example: explicit moderation endpoints (e.g., OpenAI, ActiveFence), critique chains (e.g. constitutional chain), parsing the output (e.g. guardrails.ai), individual guardrails (e.g., LLM-Guard), hallucination detection for RAG applications (e.g., Got It AI, Patronus Lynx).
+There are many ways guardrails can be added to an LLM-based conversational application. For example: explicit moderation endpoints (e.g., OpenAI, ActiveFence, PolicyAI), critique chains (e.g. constitutional chain), parsing the output (e.g. guardrails.ai), individual guardrails (e.g., LLM-Guard), hallucination detection for RAG applications (e.g., Got It AI, Patronus Lynx).
 
-NeMo Guardrails aims to provide a flexible toolkit that can integrate all these complementary approaches into a cohesive LLM guardrails layer. For example, the toolkit provides out-of-the-box integration with ActiveFence, AlignScore and LangChain chains.
+NeMo Guardrails aims to provide a flexible toolkit that can integrate all these complementary approaches into a cohesive LLM guardrails layer. For example, the toolkit provides out-of-the-box integration with ActiveFence, PolicyAI, AlignScore and LangChain chains.
 
 To the best of our knowledge, NeMo Guardrails is the only guardrails toolkit that also offers a solution for modeling the dialog between the user and the LLM. This enables on one hand the ability to guide the dialog in a precise way. On the other hand it enables fine-grained control for when certain guardrails should be used, e.g., use fact-checking only for certain types of questions.