AI Invoice OCR & Data Extraction Bot

Automate invoice processing by uploading invoice images and extracting key invoice data, such as invoice number, type, language, items, prices, and total amount. Outputs results as a markdown table and structured CSV file for streamlined financial workflows.

Flows

How the AI Flow works

User Uploads Invoice Image.: User uploads an invoice image to the chatbot interface.
AI Extracts Invoice Data.: The workflow uses AI to perform OCR and extract invoice details, including number, type, language, items, prices, and total amount.
Display Results in Chat.: Extracted invoice data is presented to the user in a clear markdown table via chat.
Generate Structured Data.: The workflow further structures extracted data into fields like company name, VAT ID, service description, and amount.
Export to CSV.: Structured invoice data is exported as a CSV file for easy download and further processing.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Prompt

Prompt for extracting invoice data from image using OCR and outputting results as a table.

                You are an expert assistant in doing OCR and extracting Information from an invoice in the image. Given the invoice, extract the following from the invoice
- Invoice Number
- Type of Invoice
- the Language of Invoice
- Items in Invoice
- Price per item in Invoice
- total amount of invoice

Make one row per invoice Item

OUTPUT THE RESULT ALWAYS IN A TABLE

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

ChatInput

The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.

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Chat Output

Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.

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Message Widget

The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.

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Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

See Chat Opened Trigger

Prompt Component in FlowHunt

Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.

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Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

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Structured Output Generator

The Structured Output Generator component lets you create precise, structured data from any input prompt using your chosen LLM model. Define the exact data fields and output format you want, ensuring consistent and reliable responses for advanced AI workflows.

See Structured Output Generator

CSV Output

Generate CSV files effortlessly within your automated workflows using the CSV Output component. Transform structured data into downloadable CSV format—ideal for exporting results, sharing data, or integrating with external systems.

See CSV Output

Flow description

Purpose and benefits

This workflow, titled “Invoice data extractor OCR”, is designed to automate the process of extracting essential information from invoice images. It leverages OCR (Optical Character Recognition) and language models to present key invoice details in both a human-friendly markdown table and an exportable CSV format. This automation is particularly useful for businesses that handle large volumes of invoices, as it eliminates manual data entry, reduces errors, and increases operational efficiency.

Workflow Overview

When a user interacts with the chatbot interface, the following sequence of actions occurs:

Chatbot Initialization & Welcome
- As soon as the chat is opened, the workflow triggers a welcome message. This message introduces the user to the chatbot’s purpose—extracting invoice data from uploaded images.
Invoice Image Upload
- The user is prompted to upload an image of an invoice. The chat interface is set up to accept image files as input.
Data Extraction via Prompting
- Once an image is uploaded, the workflow composes a structured prompt for an AI model. This prompt instructs the AI to extract the following details from the invoice:
  - Invoice Number
  - Type of Invoice
  - Language of Invoice
  - Items listed on the Invoice
  - Price per item
  - Total amount
- The prompt specifically requests that the AI outputs the extracted information in a markdown table format, with one row per invoice item.
AI-Powered OCR and Information Extraction
- The workflow sends both the prompt and the uploaded image to a generator node (likely backed by a multimodal LLM or OCR-enabled AI). The model processes the image, extracts the relevant details, and formats the results as instructed.
Display and Export of Results
- The markdown table containing the extracted invoice details is displayed directly in the chat interface for immediate review.
- Simultaneously, the workflow extracts structured data fields such as company name, VAT ID, service description, and amount charged, and generates a CSV file. This file is available for download, making it easy to integrate the data into accounting or ERP systems.

Step-by-step Breakdown

Step	Component	Functionality
1	Chat Opened Trigger	Detects when a user opens the chat.
2	Message Widget	Sends a welcome and instruction message to the user.
3	Chat Input	Accepts user input, specifically the invoice image file.
4	Prompt Template	Prepares a detailed extraction prompt for the AI model, tailored for invoice data.
5	Generator	Runs OCR and data extraction on the invoice image based on the prepared prompt.
6	Chat Output	Presents the extracted information in a markdown table within the chat.
7	Structured Generator	Extracts additional structured data for CSV export (company name, VAT ID, service, amount).
8	CSV Output	Generates and provides a downloadable CSV file from the structured data.

Benefits and Use Cases

Scalability: Automates the repetitive task of reading and transcribing invoice data, allowing processing of large invoice volumes with minimal human intervention.
Accuracy: Reduces manual data entry errors and ensures consistent extraction of required fields.
Integration: The output CSV enables seamless integration with accounting software, ERP systems, or databases.
User-Friendly: The chat interface makes the process accessible to non-technical users, with clear instructions and immediate feedback.

Example Output

After processing an invoice image, the user receives a table similar to the following in the chat:

Invoice Number	Type	Language	Item Description	Price per Item	Total Amount
12345	Sales	English	Widget A	$10	$100
12345	Sales	English	Widget B	$20	$40

Additionally, the user can download a CSV file containing structured fields such as:

Company Name
VAT ID
Service Description
Amount

Why This Workflow is Useful

This workflow streamlines a historically manual and error-prone process. By automating the extraction and structuring of invoice data, businesses can save time, reduce costs, and focus their resources on higher-value activities. The dual outputs (chat table and CSV file) ensure both immediate visibility and long-term data usability, making the solution practical for teams of any size.

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