Dental clinic appointments and records: 90% pickup, no more handwritten charts
Client Profile
Pain Points
Missed appointment calls
Busy front desks miss calls and appointments leak.
Slow record keeping
Handwritten charts are slow, inconsistent and hard to retrieve.
Manual follow-ups
Follow-ups and revisit reminders rely on the front desk's memory.
Live Demo
How We Built It
AI appointment scheduling
Phone and online bookings auto-integrate with smart scheduling.
Structured records
Voice-dictated charts auto-transcribe and file into structure.
Auto reminders
Auto revisit and follow-up reminders by treatment cycle.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Flow mapping
Mapped booking, visit and follow-up flows.
Booking system
Built booking and scheduling.
Record system
Deployed voice transcription and structured charts.
Pilot
Trial in one branch.
Rollout
Covered all 8 branches.
Before vs After
“Bookings no longer leak and dictation auto-files charts, so doctors can see several more patients a day.”
Where It Fits
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