Seafood restaurant reservations and scheduling: calm even at peak traffic
Client Profile
Pain Points
Missed reservation calls
Busy lines miss calls, reservations leak, and turnover suffers.
Scheduling out of sync
Fixed kitchen shifts mean understaffing at peak and idle staff off-peak.
Slow serving invites bad reviews
Peak-hour congestion in the kitchen makes guests wait too long.
Live Demo
How We Built It
AI reservations and seating
Phone and online reservations auto-integrate with smart seating.
Traffic-driven scheduling
Forecast hourly traffic for the week to drive dynamic shifts.
Serving progress board
Kitchen progress goes live with early peak-congestion alerts.
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 reservation, scheduling and serving flows.
System build
Built reservation and scheduling systems.
Forecast model
Trained the traffic-forecast model.
Pilot
Two-week trial in one store.
Rollout & iteration
Covered 3 stores with continuous tuning.
Before vs After
“Reservations no longer leak and kitchen shifts follow traffic — we handle peak hours calmly.”
Where It Fits
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