**Project: AI-Powered Bot using n8n - Freelancer Requirements**
## **Context & Overview**
The goal is to develop an AI-powered bot using **n8n** as the central orchestration engine. n8n will manage interactions between **Microsoft Teams, Outlook, SharePoint, local file servers, SQL databases, Webchat, API, and an AI language model (LLM)**, allowing seamless communication and automated data processing. The **LLM is hosted in Azure**, making it easier to integrate with other Azure-based services, ensuring a unified and scalable infrastructure.
### **Current Status:**
- A **basic n8n workflow is already in place** with an existing Teams bot.
- The bot can already **respond to Teams chat messages via the LLM** but currently has **no deeper integrations** with emails, files, databases, or other systems.
- The goal is to extend the bot's capabilities to access and process **Microsoft Teams, Outlook, files, SQL data, Webchat, and API-based queries** while keeping it modular and secure.
### **Key Considerations:**
- **n8n as the AI Manager:**
- n8n acts as the **middleware**, dynamically routing queries and responses between the LLM and different data sources.
- The LLM is modular and can be swapped or upgraded without affecting the overall architecture.
- **Centralized Azure Infrastructure:**
- The **LLM, vector database, chat database, and SQL database** should all be hosted in Azure to minimize complexity.
- Azure provides **managed database solutions (Azure Cosmos DB, Azure PostgreSQL, Azure AI Search)** for optimal scalability and security.
- **Technology Flexibility & Security:**
- All suggested technologies are **recommendations based on research**, but we remain open to alternatives as long as they align with the infrastructure requirements.
- The system **must be GDPR-compliant, encrypted, and secure**, preferably with **EU-based hosting**.
- **User Context & Permissions:**
- The bot should only access data visible to the requesting user, adhering to user permissions.
- **Scalability & Security:**
- The solution must be **scalable**, **encrypted**, and **GDPR-compliant** (EU data storage preferred).
- **Proof of Work (PoW) for Each Module:**
- Each module should first be **validated** before full implementation to avoid project roadblocks at later stages.
---
## **Phase 1: Microsoft Teams Integration**
### **Objective:** Enable the bot to read and summarize Microsoft Teams messages (personal chats & team channels) within the user’s permissions.
### **Tasks:**
- **Authentication & Permissions:**
- Register an **Azure AD App** with necessary Microsoft Graph permissions (`[login to view URL]`, `[login to view URL]` for user-scoped access).
- Implement OAuth2 authentication via n8n for user-specific data access.
- **Data Retrieval:**
- Use **Microsoft Graph API** to fetch messages from user-visible **Teams channels and personal chats**.
- Implement filtering to retrieve **unread** or relevant messages for summarization.
- **Summarization & AI Processing:**
- Use **Azure AI Search** or a vector database for better search & relevance.
- Leverage the **Azure-hosted LLM** via n8n for summarizing missed conversations.
- **Proof of Work:**
- Fetch user messages successfully from Graph API.
- Summarize messages using AI and return results to Teams.
---
## **Phase 2: Outlook Integration (Emails & Calendar)**
### **Objective:** Enable the bot to search and summarize emails & schedule meetings based on user queries.
### **Tasks:**
- **Authentication & Permissions:**
- Extend **Azure AD App** permissions (`[login to view URL]`, `[login to view URL]`, `[login to view URL]`).
- Secure **OAuth2 authentication** for each user.
- **Email & Calendar Queries:**
- Fetch **user emails** based on filters (e.g., unread, sender, subject keywords).
- Implement **Graph API’s `FindMeetingTimes`** to find available meeting slots for multiple users.
- **Proof of Work:**
- Retrieve and summarize user emails.
- Successfully return available meeting slots.
---
## **Phase 3: File Search (Local & SharePoint)**
### **Objective:** Enable AI-powered search across **local file servers and SharePoint Online**, with centralized indexing in Azure.
### **Tasks:**
- **File Type Support & Extraction:**
- Identify **searchable formats**: `txt`, `csv`, `xlsx`, `docx`, `pdf`, `json`, `xml`, `md`, `log`, `rtf`, `html`.
- Implement **text extraction pipelines** for non-searchable formats (e.g., scanned PDFs, encrypted files).
- **Vectorized Search & Indexing:**
- Store extracted file content in **Azure AI Search or Azure Cognitive Services** for fast retrieval.
- Implement **real-time updates** (additions, modifications, deletions) to reflect file lifecycle changes.
- **Proof of Work:**
- Extract and store text from various file formats.
- Perform **successful AI-powered searches** across indexed documents.
---
## **Phase 4: Local SQL Database Integration**
### **Objective:** Enable real-time or near-real-time synchronization of local SQL databases with the bot for data retrieval and vectorized search.
### **Tasks:**
- **Database Connectivity:**
- Establish a secure **Azure Hybrid Connection** or **VPN Gateway** to allow secure access to on-premise SQL databases.
- Alternatively, implement **n8n's SQL integration nodes** to directly fetch data at scheduled intervals.
- **Data Extraction & Synchronization:**
- Identify **relevant tables & fields** to be extracted.
- Implement a **change-tracking mechanism** (via triggers, timestamps, or incremental queries) to sync updates efficiently.
- **Vector Database Integration:**
- Convert structured SQL data into **semantic embeddings** and store them in **Azure AI Search or a vector database (Pinecone, Weaviate, Qdrant)**.
- Enable **fast querying** for natural language searches.
- **Proof of Work:**
- Successfully establish a connection to the local SQL database and extract relevant data.
- Validate that updates in the database reflect in the bot’s responses in near real-time.
---
## **Phase 5: Webchat & API Integration**
### **Objective:** Enable a **multi-session Webchat** and API interface for external communication with the bot.
### **Tasks:**
- **Webchat Implementation:**
- Develop a **React/Vue-based frontend** for user-friendly interactions.
- Enable **session tracking** so users can resume previous conversations.
- **API Design:**
- Implement a **REST API** (`POST /api/chat`) for direct bot interactions.
- Ensure **secure authentication** via API keys, OAuth, or JWT tokens.
- **Proof of Work:**
- Establish functional Webchat and API communication.
- Validate message flow and session management.
---
## **Possible Future Integrations**
- **GitLab**: Automate interactions with repositories, issues, and CI/CD pipelines.
- **HubSpot**: Integrate customer data, CRM automation, and lead tracking into the AI-powered system.
---
## **Long-Term Collaboration & Next Steps**
We are looking for **long-term support** from a freelancer who can help maintain and expand this system beyond the initial implementation. Future phases may include **workflow optimizations, additional integrations, and ongoing performance improvements**.
If the initial implementation is successful, there will be opportunities for further **feature enhancements and long-term cooperation** to ensure the system remains scalable and efficient.
This structured approach ensures a robust, scalable AI-powered bot that seamlessly integrates with business tools while maintaining high security standards, all within a centralized Azure-based infrastructure.
---
## **Technology Agnosticism & Future Considerations
While our current implementation is based on n8n and Azure services, we are open to exploring alternative technologies that may offer better efficiency, scalability, or ease of integration. Potential options could include Copilot integrations, alternative AI models, or even different workflow orchestration tools if they better suit the evolving needs of the system.
The solution must remain technically feasible, secure, and organizationally viable, aligning with compliance requirements and business objectives. Any proposed alternatives should be evaluated based on their flexibility, long-term maintainability, and compatibility with the existing infrastructure.
