Handing your customers over to an AI can feel like stepping off a ledge, what if it fails you?

Handing your customers over to an AI can feel like stepping off a ledge, what if it fails you? We’ve felt that fear ourselves. That’s exactly why we built our platform from the ground up to tackle these worries head-on.

• Is it accurate enough? Will mistakes cost me customers?

• How do I monitor performance and fix issues quickly?

• Is it really ready for prime time, or am I risking my brand’s reputation?

• Can I truly trust an AI with my most important processes?

• Have I built this correctly? 🤨

No matter where you fall on the spectrum of early adopters, you’ve likely asked some version of these questions when deploying an AI agent to engage with your customers.

We recognized early on that helping our customers build confidence and trust in the platform was crucial.

The Key: Visibility & Transparency

Our customers needed to see if the agent they built delivered every single time. If something didn’t work, they needed to know and so did we. The first step in fixing a problem is recognizing that something is broken.

To ensure full visibility, we developed a comprehensive suite of monitoring features around end-user interactions and calls. Rather than hiding these tools behind admin panels, we made them directly accessible to our customers.

Visibility Features:

✅ Call Transcripts: Real time transcripts; stored under calls
✅ Call Summaries: Summarizes customer inquiries and resolutions
✅ Call Scoring: Evaluates agent performance during calls
✅ Sentiment Analysis: Tracks caller sentiment throughout the conversation
✅ Call Recordings: Provides access to actual call recordings
✅ Post-Transfer Transcripts: Captures transcripts after calls are transferred
✅ Call Analytics: Displays call trends via Dashboard
✅ AI Tool Calls: Shows how agents utilized tools during calls
✅ Call Reporting: Enables thumbs up/down feedback
✅ Advanced Analytics: Summarizes call, caller, and business trends in Dashboard

By making these insights accessible, any poor scores, unsuccessful calls, unhappy customers, flawed agent implementations, dysfunctional tools / features, and platform deficiencies became visible.

The Result?

• Our customers can improve their implementations and processes, creating more robust solutions and ultimately support their customers better.

• We receive direct feedback to enhance our platform’s functionality and reliability.

• Issues and challenges are tackled head-on, strengthening customer relationships.

While we have several other tools and processes in place to ensure product reliability and quality, this transparency-driven approach has helped us stay close to our customers. By growing alongside them, we build confidence, trust, and a stronger AI-driven future together.