How intelligent call orchestration delivers the right experience, every time.
Choose the assistant tier that matches your practice's volume and quality goals. All run on ClaireMed Cloud with the same tools and workflows.
LLM: Efficient LLM · Voice: Native Cloud TTS · STT: Standard
High-volume, cost-sensitive
LLM: Advanced LLM · Voice: Premium Neural · STT: Advanced
Best quality & reliability
LLM: Advanced LLM · Voice: Ultra-HD Neural · STT: Advanced
Premium voice experience
Speech-to-text, intent recognition, LLM reasoning, tool execution, and natural voice response, all orchestrated by ClaireMed Cloud.
Step-by-step flows for book, modify, and cancel, with LLM decisions and tool calls at each stage.
Caller asks to book with a provider or service. LLM captures provider/service and maps to the correct resource (e.g., provider to bay/slot type).
LLM calls checkAvailability with date and resource. ClaireMed Cloud queries the practice management system and returns open slots.
checkAvailabilityCaller chooses a slot. LLM confirms and calls createBooking with date, time, resource, and optional notes.
createBookingPractice management system creates the booking. ClaireMed Cloud returns success. LLM speaks confirmation and can send TCPA-compliant SMS recap.
Transparent, conversational AI. Not rigid trees or legacy IVR.
| Approach | Latency | Personalization | Language support | Deployment time | Cost transparency |
|---|---|---|---|---|---|
| Legacy IVR | Menu-driven delays | None | Single language | Weeks to months | Opaque |
| Decision tree bots | Step-by-step prompts | Limited | Often limited | Days to weeks | Enterprise quotes |
| ClaireMed Cloud | Natural turn-taking | Full context, multi-tier agents | 7+ languages, auto-detect | Days | Clear tiered pricing |
The platforms most practices encounter first look affordable and fast to deploy. The hidden costs emerge over time.
Platforms like Dialogflow, Amazon Lex, IBM Watson, and LUIS are built on older NLU models that require hand-crafted intent definitions, entity extraction rules, and rigid conversation trees. Every new patient scenario demands new training data and flow engineering. There is no general reasoning, only the branches you built.
A single appointment-scheduling flow can require hundreds of sample utterances spread across dozens of intents, plus slot definitions, confirmation prompts, and fallback handlers for each. Changes to one branch often cascade through the entire tree. What takes ClaireMed Cloud an afternoon of configuration can take a decision-tree implementation team weeks.
Intent-based systems operate turn-by-turn within predefined slots. They cannot recall that a patient mentioned a medication sensitivity two turns ago, adapt when a caller changes their mind mid-sentence, or carry forward the clinical nuance that practitioners depend on. Each turn resets to the nearest matching intent.
Many intent-based platforms were not designed for healthcare. BAA coverage is often partial, covering the bot platform itself but not the downstream voice, transcription, or storage vendors in the chain. Practices assume HIPAA compliance flows through automatically, and it often does not. Each vendor in the stack requires its own BAA review.
Without a fully BAA-enabled pipeline, practices cannot safely store, review, or act on call transcripts. ClaireMed Cloud runs a BAA-enabled workflow end to end: every provider in the chain, from voice and transcription to LLM and storage, is covered under a Business Associate Agreement. Staff can review call context knowing the entire pipeline meets HIPAA requirements.
Decision tree bots appear cheaper upfront. But the engineering hours to build and maintain intent libraries, the compliance gaps requiring legal review, and the patient experience cost of rigid flows add up quickly. ClaireMed Cloud's tiered pricing reflects the full cost of a production-ready, HIPAA-compliant voice AI, with no hidden consulting fees.