Conversation Insight Extraction PoC – Speech to Insight Accelerator

Voice data is everywhere - contact-center calls, tele-health sessions, sales demos - yet most of it is never mined for patterns. Our PoC proves how AWS GenAI can extract value fast, using your own conversations.

If your organization captures audio - from customer service calls, patient consultations, interviews, or field agents - there's likely untapped insight buried in those recordings. This Proof of Concept helps you extract that value using AWS-native GenAI tooling.

Chaos Gears builds a custom analysis pipeline using Amazon Transcribe, Bedrock models (Claude 3.7/4, Amazon Nova), and optionally SageMaker-hosted models to analyze your real recordings and return structured, meaningful results.

We help you answer key questions: What was the intent of this call? Did the agent express empathy? Was the customer satisfied? What action items or decisions were made? Can we auto-fill this CRM or medical form based on the conversation?

Example Use Cases by Industry:

• Healthcare: Auto-populate EMR fields from doctor–patient consultations; flag medical risks; reduce documentation burden.
• Call Centers: Evaluate agent performance (empathy, helpfulness, script adherence); identify common objections and successful rebuttals.
• Banking & Insurance: Detect compliance breaches, measure resolution rates, summarize conversations for audit trails.
• Public Sector: Analyze citizen service calls, detect sentiment trends, extract requests or intent from multilingual hotlines.
• Field Services & Utilities: Transcribe field engineer voice logs; auto-generate reports and status updates.

The PoC is delivered in five clear phases:

1. Kick-off & goal setting We align with your CX, compliance, or clinical teams on metrics: AHT, sentiment, HIPAA entities, etc.

2. Data onboarding You supply a secure S3 drop with redacted or synthetic audio plus ground-truth summaries or QA labels. We benchmark audio quality, languages, and accents.

3. Pipeline build

• Amazon Transcribe generates timestamped, speaker-separated text.
• Bedrock models classify intent, detect churn signals, and draft summaries.
• SageMaker endpoints run open-source emotion and empathy classifiers.
• Results flow into DynamoDB + QuickSight or plain JSON API—your choice.

4. Evaluation & tuning We score WER, summarization ROUGE, sentiment F1, and compute cost per minute at your call volumes.

5. Read-out & next steps Deliverables include a metrics dashboard, annotated transcripts and a scoped SoW for productionisation if desired.

Timeline: 3–4 weeks, all compute runs in your AWS account so recordings never leave your control.