We were founded in 2005 in a tiny apartment in Bucharest, Romania as DeskOver.

Fittingly, it was a customer who, in 2012, first saw the immense possibilities for us in the fledgling RPA market. Our desktop automation product launched in 2013. In 2015, we introduced our enterprise platform along with our new name.

Today, UiPath is the fastest-growing enterprise software company in history with scores of offices, thousands of employees, partnerships with the world’s best consulting and BPO firms, a 500,000-strong developer community, and 5MM+ robots at work in over 5,000 global enterprises.

Our mission is to deliver the leading Robotic Process Automation platform to our clients for an automation first mindset.

We build software robots that automate repetitive processes. Our software robots take on the tedious, mind-numbing work and free people to do the more stimulating, creative work worth doing.

But no matter how much we’ve grown, one thing hasn’t changed. We’re still building a company we love to work for

The goal of this project is to ensure the smooth extraction of insurance-related entities from unstructured data. The unstructured data is to be provided as input which is obtained from any of the OCR engines by processing their documents in the image of PDF formats.

This AIFabric compatible ML model analyses incoming insurance documents and extracts entities i.e. policy number, policy tenure & client name and the extracted data can be utilized by the bot to perform data entry activities to their respective application.

The model has been trained on the client and carrier-specific insurance documents compatible with the Accord template.

1. Download the zip file from drive and upload the package to AIFabric

2. Input for the model is string obtained from OCR(Preferably tesseract) after passing the insurance document.

3. Output will be json object with keys "POLICY_NO","POLICY_PERIOD","INSURED_ORG".

Statistical ML model is used for entity extraction specific to the insurance industry for Insurance industries for extraction for entities i.e. policy number, policy tenure, client name.


MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Product Manager

I tried to use this model but i am getting below error message.

  "message": "a bytes-like object is required, not 'str'",

  "stacktrace": "  File \"/microservice/main.py\", line 29, in predict\n    document_list = text.split(\"\\n\")\nTypeError: a bytes-like object is required, not 'str'",

  "trace_id": "d9a32bb8-0b5b-44b2-a1b8-4d20d658b28f"

Input is text file after performing ocr on pdf.

Model 1.0 will extract Policy No, Date and Insured Party

Insurance Entity Extraction Model

Insurance Entity Extraction Model

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