Monitors data sources, ingests and processes content, routes queries to specialized agents via a Supervisor AI, stores embeddings in a vector database, and interacts with users on Telegram in multiple languages.
The AI Agent ingests data from websites and Google Drive, creates embeddings, and stores them in a vector store for fast retrieval. A Supervisor AI routes user queries to domain-specific agents (News, Product, Academy) based on intent. It answers in the user's language via Telegram, translating as needed and updating knowledge as data changes.
Routes, retrieves, and responds using a multi-agent, multilingual pipeline.
Ingests data from Google Drive and websites
Embeds content into a vector store for fast retrieval
Detects user language and translates to English when needed
Routes queries to specialized agents via a Supervisor AI
Retrieves information using RAG from the vector store and Postgres
Responds via Telegram in multiple languages
Automates multilingual customer interactions and centralizes knowledge via a Supervisor AI. Reduces manual data handling by consolidating ingestion, routing, and translation into a single AI agent.
A simple three-step process from ingestion to chat.
Ingests content from Google Drive and websites, cleans it with AI, and creates vector embeddings stored in a vector database.
Periodically checks a Google Sheet for records marked as deleted and removes the corresponding data from the vector store and Google Drive.
Receives Telegram messages, detects language, uses a Supervisor AI to route to News, Product, or Academy agents, retrieves data with RAG, and returns translated responses.
A realistic scenario showing task, time, and outcome.
Scenario: A French-speaking user asks for the latest features of Product X in the Sales channel. Within 2 seconds, the bot translates the query to English, delegates to the Product AI Agent via the Supervisor, retrieves current product details from Postgres, and replies in French with a concise summary and relevant links.
Roles that gain concrete value from this AI agent.
Delivers multilingual, accurate domain-specific answers with fewer handoffs.
Accesses live product data across languages for faster decision-making.
Automates data ingestion and keeps the knowledge base current.
Easily extends with new domain agents and prompts.
Monitors data ingestion, data quality, and vector store health.
Presents product information to leads in their language with context.
Core tools the AI agent works with to automate knowledge workflows.
Handles user messages and sends responses in multiple languages via Telegram.
Monitors folders for new or updated documents to ingest and index.
Stores links and metadata to drive ingestion, updates, and deletion workflows.
Fetches content from websites to populate the knowledge base.
Generates embeddings, performs language translation, and runs supervisor prompts.
Stores embeddings in the vector store for fast retrieval.
Stores domain data (e.g., products, content metadata) used by agents.
Practical scenarios where this AI agent shines.
Common questions and practical answers.
The Telegram interface supports multiple languages through built-in translation. The system detects the user’s language, translates to English for processing, routes to the appropriate domain agent, then translates the response back to the original language. You can customize language pairs per agent and prompts to maintain tone. Translation quality depends on the prompt and model choice, but the workflow ensures consistent language handling across agents.
Data ingested includes websites via web scraping, Google Drive documents, and Google Docs, PDFs, and Word files. The ingestion layer converts content into chunks, creates vector embeddings, and stores them in a vector database for fast retrieval. The system also monitors for updates and deletions to keep the knowledge base current.
The Supervisor AI analyzes the user query context and domain cues, then selects the most relevant domain agent (News, Product, Academy). It uses prompts that describe each agent’s responsibilities and access to data, ensuring routing aligns with user intent. If data is missing, it can proceed with partial retrieval and fallback responses.
Data privacy is handled through access controls, credentials management, and secure data storage. The vector store and databases can be hosted on your infrastructure with strict role-based access. All data transfers use encrypted channels, and you can audit who accessed what data and when.
Yes. You can create additional sub-agents and data sources by adding new workflow tools and prompts for the Supervisor. You will update prompts to include new capabilities, and wire new sub-workflows to handle them. This keeps the system extensible while preserving routing accuracy.
Set up test queries in multiple languages and domains to verify routing, retrieval quality, and translation. Validate results against a known data source and measure latency. Use versioned prompts and monitor data freshness to ensure ongoing accuracy.
You’ll need credentials for services such as OpenAI, Supabase, Google Drive/Sheets/Docs, a Telegram Bot, and a Postgres database. Configure these in your workflow credentials, and ensure permissions are scoped to the necessary data. Rotate credentials regularly and monitor access logs for unusual activity.
Monitors data sources, ingests and processes content, routes queries to specialized agents via a Supervisor AI, stores embeddings in a vector database, and interacts with users on Telegram in multiple languages.
