AI-powered transcript intelligence on AWS
Brillio built a platform that listens to every conversation – scoring, summarizing, and surfacing what each one reveals.
Brillio built a production-grade, AI-powered transcript intelligence platform on AWS, designed to do automatically and at scale what manual QA could only sample. Every transcript now runs through a structured analysis: summarization, QA scoring, objection detection, escalation analysis, root-cause extraction, recommendation generation, and structured operational reporting.
The architecture is event-driven and modular. Transcripts flow in from the tools the business already uses – Gong, Twilio, and NICE – and move through the pipeline on AWS: Amazon S3 stores transcripts and analytics, Amazon SQS orchestrates asynchronous workloads, AWS Lambda triggers workflows and handles retries, and Amazon API Gateway secures integrations. Amazon Comprehend masks PII before analysis, and Amazon CloudWatch provides monitoring and observability across it all.
Because the platform makes operational decisions, Brillio built it to be trusted as much as fast. Governance controls include prompt versioning, structured JSON validation, retry queues, failure isolation, and auditability – and human-in-the-loop review workflows catch low-confidence outputs before they ever reach a dashboard.
Brillio delivered the engagement end to end, from business problem discovery and AI strategy through prompt engineering, workflow orchestration, production deployment, and ongoing optimization. After go-live, stabilization and hypercare, SLA-based response models, and continuous prompt and workflow tuning kept the platform sharp as it scaled.
From a sample to the whole picture
Near-total call coverage, a fraction of the manual effort, and insight the business could finally act on.
The headline shift is in coverage. Calls reviewed went from 5-10% to 85-90% – from a thin sample to nearly the whole picture. And it took less effort to get there, not more: manual QA effort dropped by 60-70%, and reporting turnaround time fell by roughly 60%.
But coverage is only the start. With nearly every conversation analyzed, escalation detection sharpened, coaching grew more effective and more consistent, and operational visibility – once limited to whatever a reviewer happened to sample – became something the business could finally rely on across the contact center.
