Aesthetic clinic consultation and post-op follow-up: better conversion, fewer complaints
Client Profile
Pain Points
Manual conversion
High inquiry volume overwhelms consultants and destabilizes conversion.
Late post-op follow-ups
Missed or delayed follow-ups raise complaint risk.
Scattered client records
Records are stored per branch with no unified view.
Live Demo
How We Built It
AI consultation
AI does initial consultation and needs assessment, routing high-intent clients to humans.
Scheduled post-op follow-ups
Auto-schedule post-op follow-up and care by procedure.
Unified records
Unified, tagged client records for refined operations.
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 consultation and follow-up flows.
Consultation assistant
Deployed AI consultation and assessment.
Follow-up system
Built post-op follow-up scheduling.
Pilot
Trial in one branch.
Rollout
Covered all 3 branches.
Before vs After
“Post-op follow-ups never miss now, trust is stronger, and complaints are down.”
Where It Fits
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