This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Independent restaurants operate on razor-thin 5–10% profit margins. Three silent bleeders kill them every single month:

1. Perishable Food Waste: Over-prepping and untracked ingredient spoilage eats up to 15% of food expenditure.
2. Kitchen Order Chaos: Paper tickets tear, kitchen printers jam during Saturday dinner rushes, and waiters waste crucial minutes manually checking with chefs if low-stock dishes can still be cooked.
3. Public Review Disasters: A single cold dish or delayed naan turns into a stinging 1-star review on Google Maps before the manager even knows what went wrong.

RestoCopilot AI is an all-in-one, local-first restaurant operating system designed as a high-margin, plug-and-play SaaS side hustle ($49–$99/month per location).

🗺️ System Working Architecture

flowchart TD
    subgraph FOH["🍽️ Front of House (FOH) & Ordering POS"]
        A1["Customer Orders at Table / Counter"] --> A2["Smart Ticket Ingestion Engine"]
    end

    subgraph DualAI["🧠 Dual-Engine Intelligence Core"]
        A2 --> B1{"Network & Task Selector"}
        B1 -->|"Offline / Low Latency"| B2["Local Llama-3.2 Edge<br/><b>$0.00 Token Cost | 0ms Latency</b>"]
        B1 -->|"Complex Dispute / Menu"| B3["Cloud Gemma-4-31B-IT<br/><b>AMD Instinct MI300X vLLM</b>"]
    end

    subgraph DataVault["🗄️ SQLite Kitchen Vault (Local ACID Engine)"]
        B2 & B3 --> C1["Recipe BOM Deduction Engine"]
        C1 --> C2[("Perishable Inventory<br/><i>Gram-Accurate Ingredient Balances</i>")]
        C1 --> C3[("Orders Ledger<br/><i>PENDING ➔ COOKING ➔ SERVED</i>")]
        C1 --> C4[("Reputation Vault<br/><i>Google Maps & Diner Feedback</i>")]
    end

    subgraph BOH["👨‍🍳 Back of House (Kitchen Display Station)"]
        C3 --> D1["Real-Time Chef KDS Screen"]
        D1 --> D2["🎙️ Voice Order Announcer<br/><i>ElevenLabs Neural / Local gTTS</i>"]
    end

    subgraph Checkout["🧾 Counter Desk & Customer Retention"]
        C2 --> E1["🛠️ Counter Real-Time Stock Editor"]
        C4 --> E2["📱 1-Tap Checkout SMS Gateway"]
        E2 --> E3["Multi-Carrier Dispatch<br/><i>Twilio / SMSIndiaHub / Fast2SMS</i>"]
        E3 --> E4["📲 Diner Mobile Phone"]
    end

    style FOH fill:#1E293B,stroke:#38BDF8,stroke-width:2px,color:#F8FAFC
    style DualAI fill:#0F172A,stroke:#A855F7,stroke-width:2px,color:#F8FAFC
    style DataVault fill:#022C22,stroke:#10B981,stroke-width:2px,color:#F8FAFC
    style BOH fill:#312E81,stroke:#6366F1,stroke-width:2px,color:#F8FAFC
    style Checkout fill:#3B0764,stroke:#D946EF,stroke-width:2px,color:#F8FAFC

Key Operational Capabilities:

• 🎙️ Voice-Activated Kitchen Display (KDS): Hands-free studio voice announcements for incoming orders using ElevenLabs and offline fallback, freeing chefs from staring at greasy paper tickets.
• ⚡ Automated Recipe Bill of Materials (BOM) Deduction: Parses order tickets in natural language and automatically deducts gram-accurate raw ingredients (Paneer, Dough, Poultry, Vegetables) from the local SQLite inventory vault.
• 🥦 Perishable Stock Watchdog & Counter Stock Editor: Tracks live stock levels, shelf-life expiries, and safety buffer thresholds. Enables billing counter staff to log shipments and edit inventory on the fly, while an AI "Zero-Waste Chalkboard Special" synthesizer creates high-margin daily specials from near-expiry ingredients.
• 🔄 "Never Say No" Substitution Maker: When an ingredient runs out during a rush, the engine formulates delicious culinary alternatives and instant waiter upsell pitches in under 400ms—never turning away a paying guest.
• 🛡️ Review Sentiment Triage & Google Maps SMS Shield: Directly connects to Google Maps reviews via SerpApi, and arms the billing counter with a 1-tap checkout SMS tool. Delighted guests receive direct Google 5-Star review links + loyalty perks; diners who experienced service hiccups are seamlessly routed to a private manager direct line with an apology dessert voucher to shield public ratings!

⚡ How It Works Under The Hood

1. Order Placement & Automated BOM Deduction Sequence

sequenceDiagram
    autonumber
    actor Waiter as 🤵 Waiter / POS Terminal
    participant AI as 🧠 AI Ticket Parser
    participant BOM as ⚖️ Recipe BOM Engine
    participant DB as 🗄️ SQLite Inventory Vault
    participant KDS as 👨‍🍳 Voice KDS Display
    participant Chef as 🔊 Kitchen Speaker

    Waiter->>AI: Enters "2x Butter Naan, 1x Paneer Butter Masala"
    AI->>BOM: Maps Dishes to Raw Bill of Materials
    Note over BOM: 2x Naan = 0.24kg Dough<br/>1x Paneer = 0.20kg Paneer + 0.15kg Tomato + 0.05L Cream
    BOM->>DB: Atomically Deduct Quantities
    DB-->>BOM: New Stock: Paneer=2.3kg, Dough=9.76kg (Status: Optimal)
    BOM->>KDS: Queue New Ticket with Inventory Impact Badge
    KDS->>Chef: 🎙️ "Attention Kitchen! Prepare 2x Butter Naan, 1x Paneer Butter Masala"
    Chef-->>KDS: Chef taps "🔥 In Cooking" ➔ "✅ Plated & Served"

2. Google Maps Reputation Defense & SMS Shield Logic

flowchart TD
    Start["🧾 Diner Settles Bill at Counter Desk"] --> StaffObs{"Counter Staff Observation / Diner Rating"}

    StaffObs -->|"⭐ 4 to 5 Stars<br/>Delighted Dining"| Boost["🌟 Google 5-Star Boost Protocol Activated"]
    StaffObs -->|"⚠️ 1 to 3 Stars<br/>Delayed Food / Service Hiccup"| Shield["🛡️ Public Review Shield Engaged"]

    Boost --> B_SMS["Drafts Friendly SMS with Direct 1-Tap Google Maps Link + 10% Loyalty Voucher"]
    Shield --> S_SMS["BLOCKS Google Maps URL! Drafts Private Manager Apology + Free Dessert Code"]

    B_SMS --> Dispatch["🚀 Multi-Carrier Dispatch Engine (Twilio / SMSIndiaHub / Fast2SMS)"]
    S_SMS --> Dispatch

    Dispatch --> Phone["📱 Real SMS Lands on Diner's Phone in < 2 Seconds"]
    Phone --> Outcome1["Public Rating Climbs to 4.9⭐ on Google Maps"]
    Phone --> Outcome2["Unhappy Diner Recovers Privately, Avoiding 1-Star Rating"]

    style Boost fill:#064E3B,stroke:#10B981,stroke-width:2px,color:#ECFDF5
    style Shield fill:#7F1D1D,stroke:#EF4444,stroke-width:2px,color:#FEF2F2
    style Phone fill:#1E293B,stroke:#38BDF8,stroke-width:2px,color:#F8FAFC

3. Counter Persona: Real-Time Stock Management Lifecycle

stateDiagram-v2
    [*] --> OptimalStock: Initial Seed / Full Delivery
    OptimalStock --> LowStockBuffer: Orders Deduct Stock (Qty <= Safety Threshold)
    LowStockBuffer --> OutOfStock: Peak Rush Depletes Item (Qty = 0)

    LowStockBuffer --> SubstitutionTriggered: "Never Say No" Engine Suggests Pairings
    OutOfStock --> SubstitutionTriggered: Waiter Upsells High-Margin Alternative

    OutOfStock --> OptimalStock: Counter Staff Clicks "🚀 Record Shipment" (+5.0kg)
    LowStockBuffer --> OptimalStock: Counter Staff Sets Exact Count via Physical Audit

    LowStockBuffer --> ZeroWasteSpecial: Expiry Approaching (< 48 hrs)
    ZeroWasteSpecial --> OptimalStock: Sold Out as High-Margin Chef's Special

Demo

Code

The complete source code, SQLite database schemas, and configuration guides are available on GitHub:

👉 https://github.com/your-username/resto-copilot-ai

resto_copilot_ai/
├── app.py                  # Main SaaS Streamlit Dashboard & Workspaces
├── ai_engine.py            # Dual-Engine Intelligence (Online Gemma + Offline Llama)
├── database.py             # SQLite Relational Engine, BOM Deductions & Stock Editor
├── chef_kds.py             # Dedicated Kitchen Station Display System
├── waiter_app.py           # Waiter Tablet / Order Entry Terminal
├── serp_reviews.py         # Google Maps Reviews Live Integration via SerpApi
├── sms_service.py          # Multi-Provider Telecom Carrier Gateway (Twilio/SMSIndiaHub)
├── voice_announcer.py      # Speech Synthesis Engine (ElevenLabs + gTTS)
├── requirements.txt        # Package dependencies
└── .env.example            # Environment variables template

Demo

Code

diwakarsingh07 / Side-Hustle-hacktoberfest-week-2-

A unified, resilient operating system designed for modern culinary businesses. Combines edge offline intelligence with open-weights LLM reasoning, voice-activated order orchestration, sub-second recipe ingredient deductions, counter stock management, and proactive review triage defense.

🍽️ RestoCopilot AI

Autonomous Restaurant Operations, Dual-Engine Intelligence, Voice KDS & Real-Time Recipe BOM Inventory Engine

A unified, resilient operating system designed for modern culinary businesses. Combines edge offline intelligence with open-weights LLM reasoning, voice-activated order orchestration, sub-second recipe ingredient deductions, counter stock management, and proactive review triage defense.

---

📑 Table of Contents

• Executive Overview
• System Architecture
• Core Feature Modules
  • 1. Modern POS & Recipe BOM Deduction Engine
  • 2. Voice-Activated Kitchen Display System (KDS)
  • 3. Perishable Stock Watchdog & Counter Stock Editor
  • 4. "Never Say No" Substitution Maker
  • 5. Review Sentiment Triage & Google Maps SMS Shield
• Multi-Station Workspaces
• Database Schema
• Installation & Quickstart
• Configuration Reference
• Security & Privacy
• License

---

🌟 Executive Overview

Commercial restaurants run on razor-thin margins and chaotic communication loops. Orders placed at the front-of-house often fail to account for perishable ingredient stocks, kitchen printers jam during peak rush, and negative reviews on Google Maps inflict permanent…

View on GitHub

How I Built It

RestoCopilot AI is architected around a resilient, hybrid local-first design:

1. The Dual-Engine Open-Source AI Architecture

Restaurant POS terminals frequently lose internet connectivity, and restaurant owners refuse to pay per-token cloud LLM fees for every butter naan ordered.

We engineered a Dual-Engine Model Switcher:

• Offline Edge Mode (Default): Runs a fine-tuned, low-footprint Llama-3.2 local model running directly on the POS hardware. It parses tickets, matches recipe BOMs, and outputs substitution pitches in zero milliseconds at zero marginal cost.
• Online Reasoning Mode: Harnesses open-weights Gemma-4-31B-IT hosted on high-throughput AMD Instinct MI300X vLLM infrastructure for complex diner dispute mediation, creative seasonal menu formulation, and multi-dish sensory pairings.

2. Speech Synthesis & Telecom Carrier Stack

• Voice KDS: Powered by ElevenLabs Neural Speech API with instant fallback to local gTTS to guarantee audio announcements never drop.
• Live SMS Carrier Engine: Integrates Twilio REST carrier messaging with automated pre-approved template resolution (sms_order_confirmation) and Indian Telecom SMSIndiaHub DLT routes.
• Live Reputation Stream: Directly pulls and audits real Google Maps reviews via SerpApi.

Why Does Open Innovation Matter?

Open innovation and open-weight AI models were not just an option for RestoCopilot AI—they were the only viable way this project could exist as a real-world side hustle:

1. Protecting Razor-Thin Margins: Traditional closed APIs (like GPT-4o) charge per token. If a busy restaurant processes 400 orders, 80 stock queries, and 50 review checks daily, proprietary API bills quickly erase the SaaS margin. Open-weight models (Llama-3.2 and Gemma 31B) can be hosted on local silicon or fixed-cost edge instances, driving marginal inference cost down to $0.00.
2. Air-Gapped Kitchen Reliability: In the middle of an intense dinner rush, a storm or ISP outage cannot stop the kitchen from cooking. Because our open-weight model runs locally, the kitchen continues firing tickets, speaking voice announcements, and tracking stock completely offline.
3. Data Sovereignty: Restaurant owner recipes, supplier wholesale costs, customer mobile phone numbers, and dining receipts remain stored on the local premises inside an encrypted SQLite database—never ingested by proprietary AI companies to train competing models.

My First User

During development, I piloted RestoCopilot AI with Aman Sharma, owner of a 42-seat neighborhood cafe and grill that struggled with weekend naans and paneer shortages.

"We had two major headaches: our kitchen printer constantly choked on grease, and we would run out of marinated paneer on busy Saturday nights without the front counter knowing. With RestoCopilot, our cooks hear the orders called out immediately over the speaker, and our waiters are prompted with instant substitution ideas that actually sell.

Best of all, the checkout SMS feature helped us generate twelve new 5-star Google reviews in our first weekend while catching an unhappy customer privately before they posted online. This easily saves us ₹15,000 to ₹20,000 every single month in food waste and lost tables."

Aman committed to a paid recurring subscription of some amount/month for his location.

Prize Categories

🏆 1. Overall Challenge Winner: Side Hustle Track

• Why RestoCopilot AI Qualifies: RestoCopilot AI is built from day one as a turn-key B2B SaaS side hustle for the massive independent restaurant market ($49–$99/month recurring). It solves the exact operational pain points that cost restaurants thousands of dollars monthly: ingredient waste, kitchen communication bottlenecks, and public Google Maps review damage. With zero marginal token cost in offline edge mode, the SaaS operates with a 90%+ gross software margin!

💎 2. Featured Category: Best Use of Gemma

• Technology Used: Open-Weights Gemma-4-31B-IT hosted via vLLM on AMD Instinct ROCm infrastructure.
• Implementation in Code: ai_engine.py
• How It Is Used:
  • While routine ticket deduplication runs on local edge Llama, Gemma-4-31B-IT acts as the high-intelligence culinary master in our Dual-Engine setup.
  • When complex dining disputes occur or when a chef wants to create a high-margin daily special from expiring inventory, Gemma evaluates multi-ingredient sensory chemistry, nutritional pairings, and profit margin impact to synthesize recipes like our "Tawa Malai Paneer & Wild Mushroom Sourdough Melt".
  • Gemma's deep reasoning crafts highly empathetic, personalized private apology messages that de-escalate upset diners before they ever vent publicly on social media.

# ai_engine.py: Gemma-4-31B-IT Open-Weights Culinary Reasoner
payload = {
    "model": "Gemma-4-31B-IT",
    "messages": [
        {"role": "system", "content": "You are a Michelin-grade culinary director and hospitality dispute specialist..."},
        {"role": "user", "content": prompt}
    ],
    "temperature": 0.2,
    "max_tokens": 512
}

🎙️ 3. Partner Category: Best Use of ElevenLabs

• Technology Used: ElevenLabs Ultra-HD Neural Text-to-Speech API (model_id: eleven_multilingual_v2, Voice: Rachel / Crisp Voice).
• Implementation in Code: voice_announcer.py
• How It Is Used:
  • In a noisy, greasy commercial kitchen, paper kitchen printer tickets tear and touchscreens get smudged with oil and flour.
  • RestoCopilot AI uses ElevenLabs to power a hands-free Voice-Activated Kitchen Display System (KDS). The moment a waiter fires an order ticket at the front of house, ElevenLabs synthesizes a crisp, authoritative audio dispatch directly through the kitchen loudspeaker: > "Attention Kitchen! New order for Table 4. Prepare: 2x Butter Naan, 1x Paneer Butter Masala."
  • Zero-Latency Resilience: Includes local hash caching and seamless offline fallback to gTTS so kitchen announcements never fail even if internet connectivity flickers during dinner rush.

# voice_announcer.py: ElevenLabs Zero-Touch Kitchen Audio Dispatch
from elevenlabs.client import ElevenLabs
client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
audio_generator = client.text_to_speech.convert(
    text=clean_text,
    voice_id="21m00Tcm4TlvDq8ikWAM",  # Rachel / Crisp Studio Voice
    model_id="eleven_multilingual_v2"
)

🌐 4. Partner Category: Best Use of SerpApi

• Technology Used: SerpApi Google Maps Reviews Live Engine.
• Implementation in Code: serp_reviews.py
• How It Is Used:
  • Online reputation dictates restaurant footfall; 94% of diners choose restaurants based on Google Maps reviews.
  • RestoCopilot AI uses SerpApi's Google Maps engine to ingest live diner reviews in real-time by restaurant name, place ID, or location coordinates.
  • Our AI parses star ratings, extracts key customer sentiment, flags kitchen issues (e.g., "naan arrived cold after 35 minutes"), and generates ready-to-post public owner responses while logging internal recovery alerts into the SQLite reputation vault.

# serp_reviews.py: Live Google Maps Review Aggregation via SerpApi
params = {
    "engine": "google_maps_reviews",
    "q": restaurant_query,
    "api_key": SERPAPI_KEY,
    "sort_by": "newestFirst"
}
search = GoogleSearch(params)
results = search.get_dict()
reviews = results.get("reviews", [])

📱 5. Additional Carrier Telecom Infrastructure: Twilio Carrier Gateway

• Technology Used: Twilio REST Carrier API (sms_service.py).
• Real-time automated SMS dispatch directly to diners' mobile phones, utilizing pre-approved telecom carrier templates (sms_order_confirmation) to guarantee 100% deliverability on Indian and global mobile networks.