How Edesy's post-call extraction uses LLMs to convert unstructured voice conversations into structured data — sentiment analysis, payment commitments, lead scoring, and more.
130+
Extractions Completed
INR 0.02
Per Extraction
<3s
Processing Time
Trusted by businesses worldwide
Extractions Completed
Per Extraction
Processing Time
JSON Output
Challenge, solution, and results
Voice conversations contain valuable data — customer sentiment, payment promises, contact details, preferences — but it's trapped in unstructured audio. Manual review is expensive and doesn't scale.
Edesy's post-call extraction pipeline uses LLMs (Gemini, OpenAI, Claude) to analyze call transcripts and extract structured data. Configurable extraction templates per agent/use case. Fields include sentiment, amounts, dates, boolean flags, and free-text summaries.
130+ extractions completed in production. Average processing time under 3 seconds at INR 0.02 per extraction. Real-world example: municipal tax recovery extracts citizen_sentiment, payment_commitment, escalation_required, and call_summary automatically.
From setup to results in 3 steps
Set up your AI voice agent with language, industry, and call flow preferences in under 10 minutes.
Connect your phone number and launch. Test with sample calls, then go live with real customers.
Track call outcomes, success rates, and extracted data in real-time. Optimize continuously.
AI-powered phone calls from ₹6/min — or bring your own AI keys and pay just ₹1.5/min platform fee
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