
RozieAI Insights
- Primary Topics
- Customer Intents
- Root Causes
- Sentiment Analysis
- Journey Moments
Eliminating manual insight reporting in contact centers with self-serve analytics platform to enable faster operational decision making.
Overview
Conversation Insights is an enterprise analytics platform developed by RozieAI for Air Canada's contact centre managers (users). The platform uses AI to analyze hundreds of thousands of customer conversations, surfacing trends and operational signals that help managers understand their customer issues at scale. Although initially built to meet Air Canada's operational needs, the product was designed as a scalable solution for future enterprise customers.
Problem
Before Conversation Insights, AI-generated insights were delivered through weekly reports prepared by RozieAI product owners. While these reports helped Air Canada teams identify emerging issues, they provided limited context for understanding where those issues were occurring or what was driving them.
Investigating an issue meant moving between reports, AWS Connect, and follow-up discussions with RozieAI stakeholders to piece together the operational context. Users therefore depended on a fragmented, people-dependent workflow to move from identifying an issue to understanding it, slowing how quickly they could make operational decisions.
Discovery & Insights
I first aligned with RozieAI product owners, data scientists, and Air Canada stakeholders to understand how insights were generated, delivered, and investigated. This showed me that the existing reports surfaced issues but didn't support the investigation that followed, so I focused the product on the underlying workflow rather than recreating the reports.

Scope
Define a time window to frame the analysis.
Identify Issues
Detect unusual patterns and emerging customer concerns.
Understand Causes
Use summaries and transcripts to understand what customers are experiencing.
Trace Operational Impact
Identify where the issue is occurring using call records and operational data.
Constraints
RozieAI needed to demonstrate value to Air Canada ahead of a contract renewal, while engineering had only days to build a working release. There wasn't enough time to design the product from scratch, so we reused patterns and components from another RozieAI product.
I couldn't change that constraint, but I could control how we validated the experience. We shipped quickly, then ran weekly sessions with Air Canada teams to observe the product in use and identify where the inherited patterns created friction.
Early Designs
I used the investigation workflow I uncovered in discovery to structure the first version of the product. Teams moved through four stages: Scope, Identify, Understand, and Trace. I designed the experience around that sequence, rather than treating the dashboard as a collection of charts and data.
The deeper constraint I was designing against: AI-derived signals and operational metadata had previously lived in separate places, forcing teams to piece together a picture across reports and systems. Combining both layers in a single scrollable view was the core structural decision, not a layout preference, but a direct response to where the workflow broke down.
Learnings from User Test Sessions
After releasing the first version of the product, we conducted weekly feedback calls with our users to identify points of friction and additional requirements. Here's a list of all the problems from multiple user test sessions.
Solution
Over a couple of development sprints, I iteratively refined the product experience based on the observations made in the user test sessions.
I redesigned Conversation Insights around the way teams actually investigated issues. The first version put everything on one scrolling page, but user sessions showed that the workflow had a natural split: Overview helped teams decide what needed attention, while Table View helped them investigate why. I turned that split into two complementary modes.
Outcomes
After shipping all the changes, we noticed significant usage within the product and the product stickiness grew as part of our users everyday workflow. Here's a few outcomes from this project.
Reflection