I use Contextual AI to build enterprise RAG applications and AI agents that leverage internal company knowledge for accurate responses. I like its focus on delivering accurate, enterprise-ready retrieval instead of just being another LLM interface. The retrieval quality is consistently strong, making AI responses reliable with large collections of internal documents. The emphasis on grounded answers with citations helps users verify information sources, increasing confidence in the results. Additionally, the initial setup was very easy for me. Review collected by and hosted on G2.com.

Overall, Contextual AI has been a great platform, but there are a few areas where I think it could improve. I'd like to see more visibility into the retrieval pipeline, such as better debugging tools that explain why specific documents were retrieved, how they were ranked, and why others were excluded. That level of transparency would make it easier to fine-tune enterprise knowledge bases and troubleshoot retrieval issues. One improvement I'd really appreciate is an interactive retrieval debugging view. It would be useful to see the complete retrieval journey, how a query was rewritten, which documents were considered, their relevance scores, reranking results, and exactly why the final context was selected for the LLM. That would make it much easier to diagnose cases where the model gives an incomplete or unexpected answer. Review collected by and hosted on G2.com.