Smart ordering and member ops for a restaurant chain: no queues, more repeat visits
Client Profile
Pain Points
Long peak-hour queues
Guests wait to order, table turnover stalls, and customers leave.
Coarse member ops
Members are just phone numbers with no refined operations, so repeat rates are low.
Scattered store data
Each store keeps its own records; headquarters can't see the whole picture.
Live Demo
How We Built It
Smart ordering and recommendations
QR ordering plus taste- and time-based recommendations shorten decision time.
Automated member marketing
Taste-preference tags drive automated offers and new-item alerts.
Multi-store dashboard
Headquarters sees revenue, ticket size and repeat visits across stores.
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
Store survey
Walked stores and mapped ordering and member flows.
System build
Built ordering and member systems, linked to POS.
Pilot
Trial in 2 stores.
Rollout
Expanded to all 15 stores.
Iteration
Continuous optimization of recommendations and campaigns.
Before vs After
“QR ordering with recommendations gets guests ordering faster, and we can upsell combos, lifting the average ticket.”
Where It Fits
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