ReceiptInsight Telegram AI
Automates receipt extraction from Telegram messages via OCR + LLM, returning structured expense summaries instantly
Trusted by
Built using established OCR technology (Tesseract) and modern LLM frameworks (LangChain + OpenRouter), with standard API integrations and secure messaging
Success Story
Smart Receipts (via Taggun OCR) reduced expense fraud by 75 % and processes 100,000 receipts daily
Integrates with
Problem
People often snap photos of receipts or paste transaction text and then spend time manually transcribing merchant, item, and total data into expense reports—error-prone and tedious
Solution
ReceiptInsight Telegram AI automates the process: it listens to Telegram input, applies OCR if necessary, uses a language model to parse and categorize details, and sends back a clean expense summary—no manual entry
Result
- Up to 80 % reduction in manual transcription time - Near real-time receipt summarization - Fewer entry errors, faster expense reporting
Use Cases
ReceiptInsight Telegram AI is a smart agent designed to simplify expense tracking by accepting receipt data via Telegram, processing it using OCR and AI, and returning structured expense reports instantly. When a user sends a receipt image or related text to the Telegram bot, the workflow triggers: if the input is an image, it is downloaded and processed with Tesseract OCR; if text, it is passed directly. Next, a Llama-based AI model (via OpenRouter + LangChain) analyzes the text and categorizes it—extracting fields like store name, date/time, line items, total, and expense category. The agent then formats this information into a human-readable message and returns it to the user in Telegram, with checks in place to detect errors or anomalous inputs. The result: users get instant, reliable expense summaries with no manual data entry. This agent is ideal for freelancers, small teams, or anyone capturing receipts via mobile and wanting fast expense logging
Integrations
Connect to your existing tools seamlessly
Technology Stack
Automation
Automation
Infrastructure
Implementation Timeline
Set Up Telegram Bot & Trigger
1–1.5 hCreate a Telegram bot via BotFather, obtain API token, and connect the bot to the automation platform. Enable message triggers for incoming receipts
Configure Input Detection Logic
1 hImplement branching logic to distinguish between image and text messages. Route each input type to the correct processing path
Integrate Tesseract OCR for Image Processing
1–1.5 hSet up Tesseract OCR to extract text from receipt images, ensuring accurate recognition of key fields like totals and dates
Connect Llama-Based AI Model for Parsing & Categorization
1–1.5 hLink the OpenRouter + LangChain integration with a Llama model to interpret extracted text, identify merchant, items, totals, and categorize expenses
Format Output & Respond in Telegram
0.5–1 hBuild a formatted message template for results, validate extracted data, handle errors, and send the structured expense summary back to the user in Telegram
Support Included
Includes sample n8n JSON workflow, prompt template for Llama, OCR setup instructions, and example receipts to test
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