For leadership, the consequences were tangible. Without a unified view of sales interactions, coaching was reactive rather than proactive. Decisions about agent performance and sales strategy were made on incomplete data. Compliance review relied on manual spot-checks. And as call volumes grew, the problem compounded rather than resolved.
The company needed more than a faster version of the existing workflow. It needed a fundamental rethink of how customer conversations could become structured, searchable, and actionable business data, without adding headcount, without sacrificing compliance, and at a cost the business could sustain at scale. That’s the challenge Brillio was brought in to solve.
Solution
Our team at Brillio designed a fully automated, serverless call analytics engine that integrated AWS, OpenAI, and Snowflake to turn raw call recordings into structured business intelligence, minutes after each call ended.
The system connected directly to conversation intelligence platforms Gong and Nice via secure webhooks. The moment a call concluded, its recording and metadata were automatically ingested into AWS, eliminating manual upload delays entirely. An AWS Lambda function then filtered duplicate records and applied AWS Comprehend to redact personally identifiable information, keeping every transcript compliant and audit-ready.
Transcripts and metadata were stored across Amazon S3 and DynamoDB, creating a hybrid data layer built for both high-volume storage and rapid retrieval. From there, GPT-4o mini read each transcript and extracted root causes, sales stage flags, sentiment trends, objections, and conversion cues, delivering consistent, structured insight across every call in the pipeline.
Snowflake enriched these call-level insights with deal-level business context: account hierarchy, revenue stage, and ownership details. Managers gained a 360-degree view of each customer conversation, combining operational call data with commercial context for the first time.
The final layer was a secure, cloud-native dashboard where users could filter insights by agent, team, deal type, or outcome. Auth0 handled access control, while AWS CloudWatch and SNS monitored Lambda functions for reliability.
We ran the engagement as a co-innovation partnership. Discovery workshops brought our data scientists alongside the client’s QA and sales leaders to define prompt logic, metadata structures, and redaction rules grounded in real call examples. Each component was built iteratively, benchmarked against manual QA results, and refined before scaling. Enablement sessions ensured the client’s analysts could manage and evolve the platform independently, long after deployment.
