Every day, contact centers capture thousands of conversations between customers and agents. These calls contain some of the most valuable—and most honest—signals a business can receive: why customers are frustrated, where friction lives, what they tried before picking up the phone, and how effectively agents respond. Yet most of that signal stays trapped inside audio recordings that are impractical to analyze manually at scale.

For a leading US utility serving millions of customers, this gap represented both a risk and a missed opportunity. Only a small fraction of calls was ever reviewed, and most of what customers were sharing with the business went unheard. Supervisors relied on random sampling to assess quality. Emerging issues surfaced weeks after they began. And the intent behind each call, the real reason a customer picked up the phone, was rarely captured beyond a single pre-assigned skill tag.

CGI partnered with Databricks to transform how customer conversations are analyzed and used across the business. Rather than simply expanding analytics coverage, the approach focused on changing how the business interacts with its data. By introducing a conversational layer through Databricks Genie, teams are no longer limited to static dashboards or predefined reports. Instead, they can ask questions in natural language, explore trends as they emerge and investigate the “why” behind customer behavior, without waiting on technical teams or rebuilding queries. Together, we built a Call Center Analytics AI solution that turns raw customer call recordings into governed, analytics-ready insights, delivered at scale, with privacy built in and accessible to the business users who rely on them.

From limited sampling to real-time insights

The core challenge in call analytics is scale. A contact center supervisor reviewing only a small fraction of calls cannot meaningfully assess an operation that handles thousands of calls per day. Trends take too long to surface, coaching opportunities are missed and the voice of the customer—the most valuable dataset a service organization owns—remains locked within audio recordings.

Built end-to-end on the Databricks Lakehouse Platform, our solution addresses this limitation directly. Every call is automatically transcribed, privacy-protected, enriched with AI and made available through dashboards and natural-language tools interfaces. Instead of reviewing a sample of calls, the organization can now analyze every customer conversation.

Just as importantly, these insights are accessible across the business rather than confined to analysts or reporting cycles. With natural-language access to curated call data, teams—from contact center leaders to compliance—can explore patterns, validate assumptions and identify emerging issues in real time. What previously required days of analysis can now as questions arise.

For example, the organization identified a recurring billing issue driving repeat calls within days rather than weeks, enabling teams to address the root cause before it escalated.

Building the solution on Databricks

Turning audio into insight is not just as much a usability challenge as it is a data engineering one. While many organizations can process and store large volumes of data, far fewer can make that data accessible for the teams responsible for acting on it. In this case, the objective was not only to unify ingestion, transcription and AI enrichment on Databricks, but also to make those insights directly explorable by business users through a conversational interface.

Through Databricks Genie, the entire pipeline becomes accessible through a natural-language experience that allows users to ask follow-up questions, investigate anomalies and trace insights back to source data without needing to understand the underlying data structures.

Key elements of the solution include:

  • A unified data foundation that ingests audio recordings, processes them through layered storage, and publishes curated insights ready for analytics and reporting
  • Automated transcription that converts spoken conversation into searchable, structured text with high accuracy
  • Built-in privacy protection that automatically detects and redacts sensitive customer information prior to downstream analysis, supporting compliance and regulatory requirements
  • AI-driven enrichment that categorizes calls, generates summaries and makes each interaction immediately ready for analysis and natural-language exploration
  • Management-ready dashboards combined with conversational analytics, enabling users to move beyond static reporting and explore “why” and “what if” questions in real time
  • Natural-language analytics through Databricks AI/BI Genie, allowing business users to query data in plain English and receive direct, data-backed answers.

Because every layer runs on a single platform, the solution is governed end-to-end. Sensitive data remains within a secure perimeter, and every insight is traceable to the original recording. The pipeline can be reconfigured, extended or re-run without rebuilding from scratch, ensuring the system remains adaptable as business needs evolve. The result is a solution where insights are both generated and immediately accessible and usable by the teams that depend on them.

Why this matters for the business

For a leading utility, the impact goes well beyond transcription. The ability to understand every customer conversation creates value across the organization, enabling faster, more informed decision-making and reducing the lag between identifying issues and addressing them.

  • Customer experience leaders gain visibility into why customers are calling, including the issues raised after the initial reason for the call, enabling more targeted self-service investments, improved agent training and faster response to emerging pain points.
  • Operations and quality teams move beyond small-sample reviews to continuous, objective measurement of agent performance, script adherence and resolution quality.
  • Compliance and risk teams benefit from consistent, automated privacy protection applied to every call, not just those selected for review.
  • Executives gain a real-time view of customer sentiment and call drivers, with the ability to explore trends interactively and investigate root causes without additional analysis cycles.

The result is a contact center that is no longer a cost center with limited visibility. It becomes a source of enterprise-wide insights, informing product decisions, service design and strategic planning.

A pattern that extends beyond a single industry

While this solution was built for a utility, the underlying challenge is common across industries. Banks, insurers, healthcare providers, telecoms and retailers all operate contact centers that generate enormous volumes of customer conversations, much of which remains underutilized. The same Databricks-native approach applies wherever organizations need to turn voice data into governed, scalable business intelligence.

As organizations operationalize AI across the enterprise, the ability to combine governed data with intuitive, conversational access is becoming increasingly important. Solutions like this demonstrate how organizations can move beyond isolated analytics use cases and create shared intelligence that supports decisions across functions.

This reflects a broader shift in how data and AI are applied: not just as tools for analysis, but as capabilities embedded into everyday decision-making. Partnerships between industry expertise and platforms like Databricks enable shift possible at scale.

The voice of the customer has always been present. With CGI and Databricks, enterprises can now understand and act on it.