Tea-drink franchise ops assistant: one standard across 20 stores
Client Profile
Pain Points
Standards hard to unify
Franchise stores drift apart on product and service standards.
Costly inspections
Supervisors travel store to store, which is slow and expensive.
Late issue detection
Store problems surface only after complaints or bad reviews.
Live Demo
How We Built It
SOP Q&A
Stores ask the AI assistant anytime for the standard answer.
Photo self-check
Stores upload standard photos and AI checks hygiene and display compliance.
Auto anomaly alerts
Business-data anomalies auto-alert and dispatch supervisors.
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
SOP catalog
Turned store SOPs into a structured knowledge base.
Self-check system
Built photo self-check with AI recognition.
Pilot
Trial in 3 stores.
Rollout
Covered all franchise stores.
Iteration
Optimized supervision from inspection data.
Before vs After
“Supervisors don't need to visit every store — a self-check photo shows exactly what's non-compliant.”
Where It Fits
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