BlessOrder.com - Centralized On-Premise AI Platform
Budget: $250 – $750 CAD
Before you place your bid, please read it carefully:
This is an AI-driven project, if you don’t have any related experience, please DO NOT bid;
If you decided to bid:
1. Please prepare your past AI projects to show me;
2. Please read the project description to make sure you understand it and to have a right solution;
3. Please let me know the open source AI you would use for my project;
The first platform should be implemented is BlessOrder.com, when it’s fully delivered and tested, we will move to the next project. The vendor of BlessOrder.com can manage the product listings like Amazon, but can update the product listings by uploading the excel as well. It’s better to immigrate from the existing store of Amazon, etc., that will be much easier for the vendor.
BlessOrder.com - Centralized On-Premise AI Platform
Overview
We need a centralized, on-premise AI service to be shared by three products: BlessOrder.com, BlessShipping.com, and BlessAccountingAI.com. The 3 platforms should work on the same AI system, so the customer can just register once, but to use the 3 systems by AI. The centralized AI shall provide natural language parsing, conversational UI, retrieval-augmented generation (RAG), and secure tool integrations (OSRM, pricing engine, accounting APIs). The goal is single user registration, context-aware chat across apps, and shared AI capabilities without repetitive model deployments.
Functional Requirements
Multi-tenant Context Management
Each conversation must carry tenant_id, app_context (shipping/order/accounting), and user_id.
AI responses must be tagged with the origin app_context and relevant metadata.
Unified Authentication & SSO
Support OAuth2 / JWT for single sign-on across apps.
Users register once and choose their role(s).
Conversation & Context Routing
The AI service exposes endpoints:
POST /ai/chat — accept chat messages, app_context, session_id
POST /ai/parse — parse natural language into structured payloads (e.g., shipment JSON)
POST /ai/tools — call tool adapters (OSRM, Pricing)
The system supports switching contexts (e.g., moving the same chat session from BlessOrder to BlessShipping) and preserving chat history.
RAG & Vector DB
Integrate with a vector DB (Milvus/FAISS).
Allow indexing of: policies, SOPs, invoices, past conversations, contract clauses.
Retrieval must respect ACLs.
Tooling & Plugin System
Implement controlled tool adapters for:
OSRM (routing)
Pricing engine (distance/weight/volume formulas)
Accounting API (ERPNext)
Payment gateway (Stripe)
Tools must be invoked through a safe API layer; tool outputs are validated by back-end business logic.
Model Hosting & Inference
Support local models (LLaMA/Mistral), quantized (INT8/INT4) for CPU/GPU inference.
Provide model selection mechanism per task (e.g., light model for parsing, larger for complex reasoning).
Expose inference metrics: latency, throughput, token usage.
Security & Data Governance
End-to-end encryption in transit (HTTPS / WSS).
Sensitive fields redaction rules and encryption at rest.
Audit logs for all AI-tool interactions and data access.
APIs & Contracts
POST /ai/chat — input: {user_id, tenant_id, app_context, session_id, message}; output: {reply, actions[], citations[], session_state}.
POST /ai/parse/shipment — returns standardized shipment JSON: {pickup_address, pickup_window, dimensions, weight, destination_address, delivery_window, price_preference}.
POST /ai/match — calls matching algorithm and returns candidate carriers.
Scalability & Deployment
Containerized microservices (Docker). Kubernetes-ready for horizontal scaling.
Local-first: support CPU-only inference for dev and small-scale production. GPU nodes for low-latency scale.
Monitoring & Observability
Collect logs, metrics, traces. Alerts for inference failures and high latencies.
Non-functional Requirements
Latency: parsing API should return within 2–5s on dev CPU; <1s with GPU for small inputs.
Availability: 99.9% for inference endpoints in production.
Privacy: PIPEDA/GDPR compatibility for user data in Canada/US.
Suggested Open Source Stack
LLM: LLaMA 2/3 or Mistral (quantized); runtime: vLLM / llama.cpp / ggml
Vector DB: Milvus
RAG: LangChain or Haystack
Routing: OSRM + OSM
Backend: FastAPI / NestJS
DB: PostgreSQL + PostGIS
Container: Docker + Kubernetes
https://instashop.com/en-ae
AI-Powered “Message-Based Ordering System” – Open-Source Component List
Development Task
Recommended Open-Source System / Technology
Notes
1. Customer Message Input Interface
React / Vue.js
Frontend framework to create interactive input forms and display AI-generated product suggestions
2. Product Matching via AI
OpenAI GPT API / Hugging Face Transformers
NLP models to parse customer messages and match products based on seller descriptions; can be fine-tuned on seller catalog
3. Backend API / Business Logic
Node.js + Express / Spring Boot
Handles message processing, product matching, order creation, and communication with other modules
4. Order Management
WooCommerce (headless mode) / CRMEB / Saleor
Open-source platforms for storing orders, managing products, tracking status; WooCommerce can be used headless with custom frontend
5. Customer & Seller Data Management (CRM)
SuiteCRM / Vtiger CRM / SugarCRM
Stores customer profiles, order history, seller info; integrates with backend to manage notifications and reporting
6. Notification System (Email & SMS)
Postal (email server) / Twilio (open-source alternatives exist like Mailtrain)
Sends order confirmation, delivery updates, and notifications to customers, sellers, and drivers
7. Delivery Driver Interface / Tracking
React Native / Flutter (mobile) + Backend API
Allows drivers to receive orders, update delivery status; can integrate with backend order system
8. AI Model Training & Product Database
PostgreSQL / MySQL (database) + Python (for training scripts)
Stores seller product descriptions and historical orders; used for AI model fine-tuning and recommendation logic
9. Logging & Monitoring
Prometheus + Grafana
Monitor system performance, order flows, and API health
10. Security & Authentication
Keycloak / OAuth2
Provides secure login for customers, sellers, and drivers; can integrate with frontend and backend
Additional Notes for Developers
MVP Approach: Start with just message input → AI product suggestion → order creation → notification. Later add delivery tracking and analytics.
Integration: Open-source modules can communicate via REST APIs or GraphQL.
Customization: WooCommerce / CRMEB / SuiteCRM can be customized for your workflow.
AI Model: GPT or Hugging Face Transformers for NLP can be hosted on local servers or cloud.
Delivery Orders Integratation with with Swiggy, UberEats, Zomato etc
RoyalPOS provide integration with Swiggy, Zomato, FoodPanda, UberEats and Scootsy.
Merchant can enable/disable items for all aggregators with a single click. Menu Sync and different price can be set for delivery companies and dine in menu. Do not need to use different applications of all aggregators and make your business hassle free.
Inventory Management
Warehouse staff can:
Update inventory quantities directly.
All inventory changes are logged with user and timestamp.
System triggers alerts when stock is low or anomalies are detected.
3.3 Notifications & Messaging
Internal messages allow warehouse staff to notify supervisors.
Multi-channel notifications supported:
In-app push notifications
3.4 AI Assistance
Natural Language Processing (NLP) for interpreting customer requests.
Inventory alerts and suggestions for restocking or allocation.
Detection of unusual orders (e.g., quantity exceeds stock) → automatic supervisor notification.
4. System Architecture
4.1 Frontend
Customer Chat Interface:
Conversational ordering
Product selection & confirmation
Seller Backend Dashboard:
Order management
Inventory table & live updates
Internal messaging system
4.2 Backend
Order Management Service
Inventory Management Service (real-time updates)
AI Service (NLP + product matching)
Notification Service (push)
4.3 Database Design
Users table (customers, warehouse staff, supervisors)
Roles & permissions table
Products & inventory table
Messages/notes table
Orders table
4.4 API Endpoints (Example)
POST /api/order → create customer order
GET /api/inventory → fetch current inventory
POST /api/inventory/update → update inventory
POST /api/message → leave note and notify supervisor
5. Workflow
Customer sends a product request via chat.
AI analyzes the request → recommends matching products → customer confirms.
System creates an order → warehouse staff view inventory.
Warehouse staff leaves notes or updates stock → system logs change → supervisor notified.
Supervisor approves changes or handles anomalies → feedback sent to relevant staff.
Centralized On-Premise AI Service — Development Requirements
Overview
We need a centralized, on-premise AI service to be shared by three products: BlessShipping.com, BlessOrder.com, and BlessAccountingAI.com. The centralized AI shall provide natural language parsing, conversational UI, retrieval-augmented generation (RAG), and secure tool integrations (OSRM, pricing engine, accounting APIs). The goal is single user registration, context-aware chat across apps, and shared AI capabilities without repetitive model deployments.
Functional Requirements
Multi-tenant Context Management
Each conversation must carry tenant_id, app_context (shipping/order/accounting), and user_id.
AI responses must be tagged with the origin app_context and relevant metadata.
Unified Authentication & SSO
Support OAuth2 / JWT for single sign-on across apps.
Users register once and choose their role(s).
Conversation & Context Routing
The AI service expo
This is an AI-driven project, if you don’t have any related experience, please DO NOT bid;
If you decided to bid:
1. Please prepare your past AI projects to show me;
2. Please read the project description to make sure you understand it and to have a right solution;
3. Please let me know the open source AI you would use for my project;
The first platform should be implemented is BlessOrder.com, when it’s fully delivered and tested, we will move to the next project. The vendor of BlessOrder.com can manage the product listings like Amazon, but can update the product listings by uploading the excel as well. It’s better to immigrate from the existing store of Amazon, etc., that will be much easier for the vendor.
BlessOrder.com - Centralized On-Premise AI Platform
Overview
We need a centralized, on-premise AI service to be shared by three products: BlessOrder.com, BlessShipping.com, and BlessAccountingAI.com. The 3 platforms should work on the same AI system, so the customer can just register once, but to use the 3 systems by AI. The centralized AI shall provide natural language parsing, conversational UI, retrieval-augmented generation (RAG), and secure tool integrations (OSRM, pricing engine, accounting APIs). The goal is single user registration, context-aware chat across apps, and shared AI capabilities without repetitive model deployments.
Functional Requirements
Multi-tenant Context Management
Each conversation must carry tenant_id, app_context (shipping/order/accounting), and user_id.
AI responses must be tagged with the origin app_context and relevant metadata.
Unified Authentication & SSO
Support OAuth2 / JWT for single sign-on across apps.
Users register once and choose their role(s).
Conversation & Context Routing
The AI service exposes endpoints:
POST /ai/chat — accept chat messages, app_context, session_id
POST /ai/parse — parse natural language into structured payloads (e.g., shipment JSON)
POST /ai/tools — call tool adapters (OSRM, Pricing)
The system supports switching contexts (e.g., moving the same chat session from BlessOrder to BlessShipping) and preserving chat history.
RAG & Vector DB
Integrate with a vector DB (Milvus/FAISS).
Allow indexing of: policies, SOPs, invoices, past conversations, contract clauses.
Retrieval must respect ACLs.
Tooling & Plugin System
Implement controlled tool adapters for:
OSRM (routing)
Pricing engine (distance/weight/volume formulas)
Accounting API (ERPNext)
Payment gateway (Stripe)
Tools must be invoked through a safe API layer; tool outputs are validated by back-end business logic.
Model Hosting & Inference
Support local models (LLaMA/Mistral), quantized (INT8/INT4) for CPU/GPU inference.
Provide model selection mechanism per task (e.g., light model for parsing, larger for complex reasoning).
Expose inference metrics: latency, throughput, token usage.
Security & Data Governance
End-to-end encryption in transit (HTTPS / WSS).
Sensitive fields redaction rules and encryption at rest.
Audit logs for all AI-tool interactions and data access.
APIs & Contracts
POST /ai/chat — input: {user_id, tenant_id, app_context, session_id, message}; output: {reply, actions[], citations[], session_state}.
POST /ai/parse/shipment — returns standardized shipment JSON: {pickup_address, pickup_window, dimensions, weight, destination_address, delivery_window, price_preference}.
POST /ai/match — calls matching algorithm and returns candidate carriers.
Scalability & Deployment
Containerized microservices (Docker). Kubernetes-ready for horizontal scaling.
Local-first: support CPU-only inference for dev and small-scale production. GPU nodes for low-latency scale.
Monitoring & Observability
Collect logs, metrics, traces. Alerts for inference failures and high latencies.
Non-functional Requirements
Latency: parsing API should return within 2–5s on dev CPU; <1s with GPU for small inputs.
Availability: 99.9% for inference endpoints in production.
Privacy: PIPEDA/GDPR compatibility for user data in Canada/US.
Suggested Open Source Stack
LLM: LLaMA 2/3 or Mistral (quantized); runtime: vLLM / llama.cpp / ggml
Vector DB: Milvus
RAG: LangChain or Haystack
Routing: OSRM + OSM
Backend: FastAPI / NestJS
DB: PostgreSQL + PostGIS
Container: Docker + Kubernetes
https://instashop.com/en-ae
AI-Powered “Message-Based Ordering System” – Open-Source Component List
Development Task
Recommended Open-Source System / Technology
Notes
1. Customer Message Input Interface
React / Vue.js
Frontend framework to create interactive input forms and display AI-generated product suggestions
2. Product Matching via AI
OpenAI GPT API / Hugging Face Transformers
NLP models to parse customer messages and match products based on seller descriptions; can be fine-tuned on seller catalog
3. Backend API / Business Logic
Node.js + Express / Spring Boot
Handles message processing, product matching, order creation, and communication with other modules
4. Order Management
WooCommerce (headless mode) / CRMEB / Saleor
Open-source platforms for storing orders, managing products, tracking status; WooCommerce can be used headless with custom frontend
5. Customer & Seller Data Management (CRM)
SuiteCRM / Vtiger CRM / SugarCRM
Stores customer profiles, order history, seller info; integrates with backend to manage notifications and reporting
6. Notification System (Email & SMS)
Postal (email server) / Twilio (open-source alternatives exist like Mailtrain)
Sends order confirmation, delivery updates, and notifications to customers, sellers, and drivers
7. Delivery Driver Interface / Tracking
React Native / Flutter (mobile) + Backend API
Allows drivers to receive orders, update delivery status; can integrate with backend order system
8. AI Model Training & Product Database
PostgreSQL / MySQL (database) + Python (for training scripts)
Stores seller product descriptions and historical orders; used for AI model fine-tuning and recommendation logic
9. Logging & Monitoring
Prometheus + Grafana
Monitor system performance, order flows, and API health
10. Security & Authentication
Keycloak / OAuth2
Provides secure login for customers, sellers, and drivers; can integrate with frontend and backend
Additional Notes for Developers
MVP Approach: Start with just message input → AI product suggestion → order creation → notification. Later add delivery tracking and analytics.
Integration: Open-source modules can communicate via REST APIs or GraphQL.
Customization: WooCommerce / CRMEB / SuiteCRM can be customized for your workflow.
AI Model: GPT or Hugging Face Transformers for NLP can be hosted on local servers or cloud.
Delivery Orders Integratation with with Swiggy, UberEats, Zomato etc
RoyalPOS provide integration with Swiggy, Zomato, FoodPanda, UberEats and Scootsy.
Merchant can enable/disable items for all aggregators with a single click. Menu Sync and different price can be set for delivery companies and dine in menu. Do not need to use different applications of all aggregators and make your business hassle free.
Inventory Management
Warehouse staff can:
Update inventory quantities directly.
All inventory changes are logged with user and timestamp.
System triggers alerts when stock is low or anomalies are detected.
3.3 Notifications & Messaging
Internal messages allow warehouse staff to notify supervisors.
Multi-channel notifications supported:
In-app push notifications
3.4 AI Assistance
Natural Language Processing (NLP) for interpreting customer requests.
Inventory alerts and suggestions for restocking or allocation.
Detection of unusual orders (e.g., quantity exceeds stock) → automatic supervisor notification.
4. System Architecture
4.1 Frontend
Customer Chat Interface:
Conversational ordering
Product selection & confirmation
Seller Backend Dashboard:
Order management
Inventory table & live updates
Internal messaging system
4.2 Backend
Order Management Service
Inventory Management Service (real-time updates)
AI Service (NLP + product matching)
Notification Service (push)
4.3 Database Design
Users table (customers, warehouse staff, supervisors)
Roles & permissions table
Products & inventory table
Messages/notes table
Orders table
4.4 API Endpoints (Example)
POST /api/order → create customer order
GET /api/inventory → fetch current inventory
POST /api/inventory/update → update inventory
POST /api/message → leave note and notify supervisor
5. Workflow
Customer sends a product request via chat.
AI analyzes the request → recommends matching products → customer confirms.
System creates an order → warehouse staff view inventory.
Warehouse staff leaves notes or updates stock → system logs change → supervisor notified.
Supervisor approves changes or handles anomalies → feedback sent to relevant staff.
Centralized On-Premise AI Service — Development Requirements
Overview
We need a centralized, on-premise AI service to be shared by three products: BlessShipping.com, BlessOrder.com, and BlessAccountingAI.com. The centralized AI shall provide natural language parsing, conversational UI, retrieval-augmented generation (RAG), and secure tool integrations (OSRM, pricing engine, accounting APIs). The goal is single user registration, context-aware chat across apps, and shared AI capabilities without repetitive model deployments.
Functional Requirements
Multi-tenant Context Management
Each conversation must carry tenant_id, app_context (shipping/order/accounting), and user_id.
AI responses must be tagged with the origin app_context and relevant metadata.
Unified Authentication & SSO
Support OAuth2 / JWT for single sign-on across apps.
Users register once and choose their role(s).
Conversation & Context Routing
The AI service expo