AI Recommendations
Your AI Agent Conversations Contain More Than Support Data. Isara Turns Them Into Action.
Every interaction your AI agent has is a signal. About your documentation, your product, your processes, and how your agent is performing. AI Recommendations surfaces those signals and turns them into concrete next steps your team can act on immediately.
Smarter documentation, less customer frustration
Fix the gaps your AI agent keeps falling into
When customers ask your AI agent the same questions repeatedly and leave unsatisfied, the problem is usually documentation, not the agent. Isara reviews your AI agent conversations against your existing knowledge base, identifies where articles are missing, outdated, or unclear, and recommends the precise updates needed.
The result is an AI agent that resolves more on the first interaction, a lower volume of repeated questions, and a support team that spends less time covering for gaps that should not exist.
Better products, powered by real conversations
Surface what your AI agent conversations are telling you about your product
Recurring friction points, unmet needs, and feature requests do not always make it into a product backlog. They sit in support conversations, handled by your AI agent, and never reach the people who could act on them.
Isara scans your AI agent interactions to identify patterns across requests, complaints, and unresolved issues, and turns that into structured product recommendations your team can take directly into planning. Built from what customers are actually saying, not what teams assume they want.
Improve your AI agent continuously
Every conversation is an opportunity to make your agent better
Deploying an AI agent is not a one-time decision. How it handles specific topics, edge cases, and high-stakes interactions needs ongoing review. Isara analyses your AI agent conversations to identify where the agent is struggling, where responses are inconsistent, and where specific interaction types are repeatedly falling short.
Each finding comes with a clear recommendation so your team can make targeted improvements to how the agent is configured and supervised, without waiting for churn data to tell you something went wrong.
