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Healthcare / IT Operations

AI Voice Agents — HelpDesk & Clinic Receptionist

~9,000 employees, multi-site

Real-time AI voice

The Challenge

  • High-volume repetitive inbound calls to the IT helpdesk consuming agent capacity on routine troubleshooting
  • Manual reception workflow at multi-site healthcare locations with no caller identification capability
  • No automated ticket creation — helpdesk agents manually logging every call into the ticketing system
  • English and French language requirements across the organization with no bilingual automation in place

The Approach

Our Approach

This engagement delivered two AI voice agents on a shared platform: an IT helpdesk agent handling repetitive troubleshooting calls and a clinic receptionist agent managing appointment scheduling and caller routing. Both agents needed to operate in English and French with real-time performance.

Phase 1: Caller Identification & Voice Pipeline

The foundational challenge was caller identification. Traditional IVR systems rely on phone number lookup, but in a multi-site healthcare organization, employees call from personal devices, shared clinic phones, and different locations. We solved this with a novel approach: vector search on HR profile embeddings via Supabase pgvector.

When a caller speaks their name and department, the voice AI performs a real-time vector similarity search against embedded HR profiles (synced from Microsoft Graph), identifying the caller within seconds — no account number, no PIN, no IVR menu.

The voice pipeline was built on Twilio for telephony, Ultravox (fixie-ai/ultravox-70B) for speech-to-speech AI, and FastAPI for the orchestration layer. Language detection happens automatically in the first few seconds of the call, switching the agent's language model and response templates accordingly.

Phase 2: Helpdesk Agent — Troubleshooting & Ticket Creation

The IT helpdesk agent was designed for the highest-volume call categories:

  • Password resets — guided self-service with identity verification
  • Application access requests — automated provisioning via Microsoft Graph
  • Common troubleshooting — RAG-based knowledge retrieval from IT runbooks
  • Ticket creation — automatic logging with call transcript, intent classification, and priority assignment into Zendesk

Every action requiring a state change (ticket creation, password reset, access grant) includes an explicit confirmation step — the AI never takes action without the caller's verbal confirmation.

Phase 3: Clinic Receptionist Agent & Deployment

The clinic receptionist agent extends the same platform with a different knowledge base and tool set:

  • Appointment booking — integration with the clinic scheduling system
  • Caller routing — intelligent transfer to the appropriate department based on caller intent
  • After-hours coverage — full capability outside business hours, replacing voicemail

Both agents share the same infrastructure, authentication layer, and monitoring dashboard, with knowledge bases and tool permissions isolated per agent type. The platform now handles calls across multiple sites with sub-3-second response latency and full bilingual support.

System Architecture

Technology Stack

FastAPITwilioUltravox (fixie-ai/ultravox-70B)Supabase pgvectorOpenAI EmbeddingsMicrosoft Graph

Key Outcomes

Real-time

Caller Identification

Instant caller identification via vector search on HR profile embeddings — no manual lookup required

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Languages Supported

Full English and French voice interaction with automatic language detection and switching

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Response Latency

End-to-end voice response time from caller speech to AI agent reply under 3 seconds

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Ticket Creation

Automatic helpdesk ticket creation with call transcript, intent classification, and priority assignment

What Made It Different

  • Caller identity resolved in real-time via vector search on HR profile embeddings — no IVR menu or manual lookup needed
  • Fully integrated with existing helpdesk (Zendesk) — tickets created with transcript, intent, and priority automatically
  • No data persistence in cloud LLM — voice transcripts processed in-stream with only structured outputs stored
  • Dual-purpose architecture: IT helpdesk agent and clinic receptionist agent sharing the same platform with different knowledge bases

Lessons & Transferable Patterns

  • Vector search on HR profiles for caller ID is more reliable than phone number lookup — employees call from personal devices, shared lines, and different locations
  • Multi-language support must be designed from the start — retrofitting bilingual capability into a monolingual voice pipeline requires significant rearchitecture
  • Tool-calling for ticket creation and appointment booking must have explicit confirmation steps — AI should never create a ticket or book an appointment without caller confirmation
  • Call transcript storage requires careful data handling — store structured summaries and intents, not raw voice recordings, to minimize compliance surface area

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