Making Conversations Count
How One Support Firm Reduced Costs and Unlocked Insights by Transcribing Voice Interactions
A large customer support firm was generating thousands of hours of recorded conversations every month across multiple teams and regions. While these audio files contained valuable insights, they were practically unusable—impossible to search, hard to analyze, and expensive to store.
The company needed a solution that could turn raw voice data into structured, searchable content—improving the value of their support data while also reducing their ever-growing cloud storage costs.
The support team faced multiple operational and data-related bottlenecks:
No Searchability: With data locked in audio format, support managers couldn’t search for recurring complaints, product issues, or agent performance trends.
High Storage Costs: Voice recordings were being stored long-term for quality control and compliance purposes, racking up significant cloud expenses.
Compliance Pressure: Audio data wasn’t being tagged or categorized, making it hard to quickly respond to audits or internal reviews.
Underused Intelligence: Customer feedback, tone, and intent data were going uncaptured, leaving potential product and service improvements on the table.
We implemented a custom Speech-to-Text pipeline, purpose-built for customer support scenarios. The system accurately transcribed large volumes of voice recordings and made them instantly searchable and lightweight.
High-Accuracy Speech Recognition: The system handled diverse accents, overlapping speech, and industry-specific terminology with precision, ensuring accurate and readable transcripts.
Speaker Diarization: It automatically labeled who was speaking—agent or customer—so conversations could be understood in full context.
Text-Based Archival: By storing transcripts instead of full audio, the company reduced storage size by over 85%, significantly lowering their cloud bills.
Optional Audio Retention: For compliance-critical scenarios, a lightweight audio snippet was linked to the text, preserving traceability without bulk.
Full-Text Search: Managers could search transcripts instantly—e.g., “delay in delivery” or “cancel my subscription”—and pull up all relevant interactions.
Topic Tagging & Trend Analysis: The transcribed data was enriched with auto-generated tags and timelines, enabling better reporting on common customer issues.
Audit-Ready Format: Transcripts could be filtered by date, agent, region, or topic—dramatically simplifying internal reviews and compliance checks.
By transforming spoken conversations into searchable text, the client gained better insight into their customer experience, reduced cloud storage by over 80%, and opened the door to smarter analytics and automation.
The project turned passive audio recordings into actionable, organized, and cost-effective data—making customer support not just reactive, but insight-driven.
See it in action!
