STATUS
● Production
CLIENT
Uplift Technologies
YEAR
2025–2026
ROLE
Builder & Architect
STACK
VoiceflowClaude SonnetTwilioCRM IntegrationPython
EVIDENCE LINKS
- ● Live demo unavailable (Private)
- ● Architecture details
Note: Where client systems are private, screenshots are anonymized and architecture is shown without protected data.
AI Voice Calling Bot — Automated Outreach
AI voice agent fed client data that autonomously reached out to target customer lists. Real call recordings demonstrate production deployment — reduced callback response time from 8 hours to 3 minutes.
Problem
Healthcare staffing clients needed consistent, rapid outreach to large contact lists. Manual agent calls created inconsistency, missed SLAs, and bottlenecks. The bot needed to handle dynamic data injection per-call and produce structured logs.
Architecture & Solution
// System Pipeline
CRM exports client list → Python enrichment script normalizes fields → Voiceflow agent receives structured payload per call → Claude Sonnet handles dynamic response logic → Call recordings captured and indexed → SLA tracking dashboard updated in real time.
Implementation Decisions
- Data injection at call-time: each call receives fresh client context — no stale data
- SLA governance model: missed events tracked and routed to human review queue
- Recording capture for QA and compliance review
- Callback response time reduced from 8 hours to 3 minutes via auto-prioritization
System Ownership & Proof
System Stack
Voiceflow, Claude Sonnet, Twilio, CRM Integration, Python