Ai Automation specialist
Starting from
$14/hr
≈ €12/hr
Typically responds within New provider
I build AI-powered automation assistant built to simplify tasks, optimize operations, and help businesses scale faster with minimal manual input.
This automation layout represents a classic, highly efficient Retrieval-Augmented Generation (RAG) System split into two core phases: data ingestion (knowledge base building) and real-time AI querying. It allows users to feed unstructured documents into a database and immediately chat with an AI that retrieves answers directly from those files. How the Workflow Works The system operates via two parallel tracks to seamlessly manage internal data and user interaction: 1. The Ingestion Pipeline (Left Side) Data Input: The pipeline is initiated by an Upload your file here trigger, which accepts external documents, spreadsheets, or text files. Document Parsing: The file passes through a Default Data Loader to chunk and clean the text. Vector Insertion: Using Embeddings Google Gemini, the text chunks are converted into mathematical vectors and saved via the Insert Data to Store node into a connected vector database. This forms the agent's long-term internal knowledge base. 2. The Chat & Query Pipeline (Right Side) User Interaction: The workflow listens for incoming customer messages via the When chat message received trigger node. Autonomous Triage: The message hits the AI Agent node, which is powered by a Google Gemini Chat Model. Semantic Search: Instead of guessing or hallucinating, the agent utilizes a Query Data Tool linked to the same Google Gemini Embeddings to run a real-time semantic search against the vector database, fetching only the exact paragraphs relevant to the user’s question. Contextual Output: The agent synthesizes the retrieved facts and the conversation history to deliver an incredibly precise, context-rich response back to the user. The Results Deploying this architecture provides businesses with immediate operational wins: Elimination of LLM Hallucinations: Because the AI agent is tightly anchored to the uploaded files via a semantic search tool, it only answers based on verified company data. Instant Knowledge Base Deployment: Allows teams to uplo
Workflow Architecture Overview This automated workflow is an intelligent receipt and invoice management system. It intercepts images sent via chat, extracts and logs the financial data automatically, stores the physical document in the cloud, and utilizes an AI Agent to handle confirmation and follow-up communication. How the Workflow Works Ingestion & File Retrieval: The process is initiated by a Telegram Trigger when a user sends a message (typically containing an invoice or receipt image). The workflow immediately triggers a Download File node to pull the raw media into the system. OCR & Data Extraction: The downloaded image is passed to an HTTP Request node (Analyze Image) via the OCR.space API to extract raw text from the document. A Parse Text node then formats and structures this extracted text into clean data fields (like vendor, date, and total amount). Database Logging: The structured financial data is instantly passed to an Update Database node, which appends the information as a new row into a Google Sheet for seamless bookkeeping. Cloud Storage: Simultaneously, the workflow routes the downloaded document to Google Drive via the Add Invoice Image to Drive node to ensure a secure, organized backup of the physical receipt. Intelligent AI Confirmation: After data mapping is configured via the Set node, the details are passed to an advanced Invoice Agent node. Backed by a Google Gemini Chat Model and equipped with a Window Buffer Memory node to remember the conversation context, the agent generates a dynamic reply. Automated Response: The workflow finishes by executing a Reply node via Telegram, sending the user a conversational confirmation that their invoice has been successfully processed and logged. The Results By deploying this automation, the system achieves three powerful outcomes from a single user action: Zero-Touch Bookkeeping: Eliminates manual data entry by extracting text from receipt images and instantly logging details directly into an expense spreadsheet. Automated Document Archiving: Guarantees compliance and audit readiness by automatically saving and organizing physical files in Google Drive. Instant Conversational Feedback: Delivers an immediate, intelligent confirmation response back to the user over Telegram, creating a smooth, interactive experience.
Workflow Architecture Overview This automated, AI-driven workflow is designed to intercept incoming emails, classify their intent, and route them dynamically to either a standard notification channel or an advanced, RAG-enabled (Retrieval-Augmented Generation) Customer Support Agent. How the Workflow Works Ingestion & Structuring: The process kicks off automatically via a Gmail Trigger whenever a new email arrives. A Set Content node then cleanses and structures the incoming email data for downstream AI processing. Intent Classification: The email is passed to a Message a model node where a light LLM evaluates the text. It then hits a Router (Customer Support?) which splits traffic based on intent. Path A (Non-Support Routing): If the email is classified as Not Customer Support, the workflow immediately routes it to a Telegram node (Response Not Customer Support) to send a targeted alert or notification. Path B (Intelligent Support Agent): If it is a legitimate support inquiry, it is handed off to an advanced Customer Support Agent node. The AI Stack & RAG Integration The Customer Support Agent doesn't just guess the answer; it leverages a sophisticated ecosystem to draft precise responses: Brain: Powered by a Google Gemini Chat Model for natural language generation. Memory & Context (RAG): Connects to a Vector Store Tool linked to a Pinecone Vector Store. By using Google Gemini Embeddings, the agent can query internal knowledge bases, documentation, or FAQs to find facts relevant to the customer's specific issue. Action Execution: It utilizes a createDraft tool to automatically generate a contextual reply draft directly back inside Gmail. The Results Depending on the nature of the incoming message, the workflow yields two distinct outcomes: Filtered Notifications: Non-essential or non-support emails are automatically filtered out of the email queue and logged/alerted via Telegram. Automated Support Drafts: Complex support queries are researched against internal company documentation in real-time, and a highly accurate, context-aware email response is drafted in Gmail, drastically reducing manual triage and resolution time.
Praise N.
$92
≈ €80
Starting from $14/hr
Secure escrow payments
Responds within New provider
Typical turnaround: Varies
$12/hr
