Development of AI Automatic Client Card Filling After Call
Extracts structured data from call transcript and automatically populates CRM fields — names, addresses, dates, amounts, preferences. Operator only verifies and confirms instead of typing manually.
NER for Entity Extraction
Uses LLM (GPT-4o) to extract entities with confidence scores:
- customer_name, address, email, phone
- order_details, complaint_description
- preferred_contact_time, next_appointment
- notes
Confidence Scoring
Each extracted field gets:
- value
- confidence (0.0–1.0): 1.0=explicit, 0.7=implied, 0.3=guess
- source: quote from transcript
- needs_review flag if confidence < 0.6
Smart Merge with Existing Data
Doesn't overwrite old data with low-confidence new data. Proposes suggestions for operator confirmation.
Operator UI
Color-coded: green=high confidence, yellow=needs confirmation, gray=empty.
Timeline: NER + one CRM — 2–3 weeks. Multi-platform system with UI — 1.5 months.