One cup of coffee, eight AI agents — ordering, supply chain, and operations all orchestrated by an Agent core.
About this page
The NiMCoffee system is fully built and has passed 30+ end-to-end tests. www.nimcoffee.com is currently being prepared for launch — contact us for a sandbox account or a private-deployment plan.
In short
NiMCoffee is an AI-native operating system for coffee shops, aimed at chain owners, store managers and front-line service staff. It treats the AI Agent as the business core rather than a bolt-on: any operation can be carried out in natural language, the agent orchestrates backend services through function calling, and the GUI only assists visualization. Eight Butler roles cover analytics, marketing, content, customer service and knowledge, running scheduled self-checks. Its most distinctive trait is that every pricing action passes through one shared `pricing_guard` validator, backed by a one-click kill switch.
What the agent takes off people's plates
The AI Agent is the business core, not an add-on. Function Calling + Agentic Workflow orchestrate every backend service; any operation can be completed by talking, with the GUI only assisting visualization.
A Director coordinates Analytics / Marketing / Content / Customer-Service plus Conversation-Coach / Knowledge-Curator / Search-Optimizer. Daily 23:00 feedback mining, 03:30 hallucination scan, weekly 23:30 rule decay — all AI-driven.
Local product cache + slim tool set + keyword fast paths. 30/30 scenarios passing, average 3,068 tokens per turn, 92% token reduction vs. pure-LLM routing.
Scene-scoped tool sets: general / ordering / support / knowledge / management / channel-bot. Tools are dynamically pruned by role and scene — store managers see 33, channel bots read-only 15.
Web (Nuxt SSR) + store mini-program (uni-app) + product mini-program + channel bots (WeChat OA / WeCom, AES-encrypted). The store mini-program has its own chat session and isolates manager vs. customer views.
Base pricing is owner-only; AI marketing can move within ±20%, single coupon ≤¥50, daily budget ≤¥300, auto-refund ≤¥30 × 5/day. All pricing paths share a single `pricing_guard` validator (13 pytest tests). One-click kill-switch disables AI rules instantly.
Side by side
| Dimension | Traditional F&B SaaS + manual work | NiMCoffee (AI as the business core) |
|---|---|---|
| What sits at the centre of the system | GUI forms are the centre; AI is a chat plug-in added later | The AI agent is the core; operations run through natural language and function calling, with the GUI only assisting visualization |
| Who does the daily operations work | Managers count stock by hand, coupons go out on gut feel, service staff answer the same question over and over | Eight Butler roles run in parallel: daily 23:00 feedback mining, 03:30 hallucination scan, weekly 23:30 rule decay |
| How long a promotion takes to launch | Change code, redeploy, canary — and wait for the engineering queue | The owner states the intent in plain language, AI Marketing drafts within the guardrails, then publish or kill-switch in one click |
| Controlling cost and risk | Pricing logic is scattered across entry points and reviewed by hand; once AI is bolted on, token cost grows linearly with conversation volume | Every pricing path shares one `pricing_guard` validator with a kill-switch, and the hybrid Fast-Path architecture cuts per-turn token use by 92% versus pure-LLM routing |
Quantified before / after
Subjects and capability matrix
Director
Coordinate / decide
Analytics
Trends / anomalies
Marketing
Pricing / coupons
Content
Posts / posters
Customer service
Tickets / refunds
Conversation Coach
Feedback → rules
Knowledge Curator
Knowledge gap scan
Search Optimizer
Synonyms / intent
Verifiable facts
30 of 30 end-to-end scenarios pass, averaging 3,068 tokens per turn — a 92% reduction versus pure-LLM routing.
The tool registry spans 6 scenes and 33 tools, pruned dynamically by role and scene: store managers see all 33, channel bots see 15 read-only.
The Butler team consists of 8 roles with three fixed self-check jobs: daily 23:00 feedback mining, 03:30 hallucination scan and weekly 23:30 rule decay.
Pricing guardrails allow AI marketing ±20% movement, a ≤¥50 single coupon, a ≤¥300 daily budget and auto-refunds of ≤¥30 up to 5 per day; all pricing paths share one `pricing_guard` validator covered by 13 pytest cases.
Where it fits
Further reading
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