Invoice AI
An open-source tool for intelligent invoice analysis. It extracts invoice data via OCR, cross-validates the data and stores it in a database, after which an LLM conversational assistant queries the data through read-only tools to answer business questions.
Timeline (last 90 days)
Invoice AI is an open-source project submitted by developer dotsource123 in the Hacktoberfest open-source AI challenge.
Invoice AI uses PaddleOCR to extract text from invoice images and identify key fields such as vendor information, invoice number, date, subtotal, tax amount, and total.
Invoice AI has a validation layer that cross-checks the invoice total against the subtotal and tax amount, flags ambiguous or inconsistent extraction results for manual review, and keeps unconfirmed invoices out of the analysis pool.
Invoice AI distinguishes purchase invoices from sales invoices and isolates different currencies to prevent expenses and income from being mixed together and summary figures from being misleading.
Invoice AI's backend uses FastAPI with SQLAlchemy, and the frontend dashboard is built with Streamlit.
Invoice AI integrates a conversational assistant based on Gemma 4 and runs it via Ollama Cloud.
Invoice AI's conversational assistant uses a tool-calling architecture in which the model selects from predefined read-only Python functions to call and does not generate and execute arbitrary SQL queries.
The Invoice AI project is hosted on Render, and both the FastAPI backend and the Streamlit dashboard are accessible via the cloud.
The Invoice AI project currently has limitations in areas such as invoice layout diversity, multi-page invoice handling, handwriting recognition accuracy, and a multi-user permission system, and is still some distance from a production-grade product.
All Facts (9)
Invoice AI is an open-source project submitted by developer dotsource123 in the Hacktoberfest open-source AI challenge.
50%UnverifiedInvoice AI uses PaddleOCR to extract text from invoice images and identify key fields such as vendor information, invoice number, date, subtotal, tax amount, and total.
50%UnverifiedInvoice AI has a validation layer that cross-checks the invoice total against the subtotal and tax amount, flags ambiguous or inconsistent extraction results for manual review, and keeps unconfirmed invoices out of the analysis pool.
50%UnverifiedInvoice AI distinguishes purchase invoices from sales invoices and isolates different currencies to prevent expenses and income from being mixed together and summary figures from being misleading.
50%UnverifiedInvoice AI's backend uses FastAPI with SQLAlchemy, and the frontend dashboard is built with Streamlit.
50%UnverifiedInvoice AI integrates a conversational assistant based on Gemma 4 and runs it via Ollama Cloud.
50%UnverifiedInvoice AI's conversational assistant uses a tool-calling architecture in which the model selects from predefined read-only Python functions to call and does not generate and execute arbitrary SQL queries.
50%UnverifiedThe Invoice AI project is hosted on Render, and both the FastAPI backend and the Streamlit dashboard are accessible via the cloud.
50%UnverifiedThe Invoice AI project currently has limitations in areas such as invoice layout diversity, multi-page invoice handling, handwriting recognition accuracy, and a multi-user permission system, and is still some distance from a production-grade product.
50%