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Back Office

Back Office

Relieve employees from monotonous tasks while maintaining accuracy

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With Machine Learning, back office processes can be optimized by:

saving time and resources in non-value creating tasks, e.g. by assigning incoming documents or requests automatically
helping employees and customers finding relevant information to their request easily and without additional human interaction
reducing time lapse between request and answer and increase customer service quality

Projects in Back Office

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Legal Review of Rental Contracts

Different methods from the field of NLP helped us to create a software that spots errors in legal contracts

Automatic Checking of Service Charge Statements

Semantic Search for Public Administration

dida developed an AI based algorithm to extract relevant information from authority documents

Numeric Attribute Extraction from Product Descriptions

Automatically extract numerical attributes from product descriptions in order to enrich the existing database.

Smart Access Control with Facial Recognition

We developed a multi-level security system with facial recognition for automatic access control.

Extracting information from customer requests

In this project we created a model that when given a free form vet appointment reason can extract symptoms, diseases and requested services. This data can then be used by our client to improve scheduling and preparation.

Blog Posts in Back Office

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Introductions

Project proposals - the first step to a successful ML project

By Emilius Richter • July 18th, 2022

For a software provider, the project proposal is the first step toward meeting the needs of the customer. In this article, I will describe the most important modules in machine learning project proposals.
Introductions

Ethics in Natural Language Processing

By Marty Oelschläger (PhD) • December 20th, 2021

I explain why language models tend to reproduce stereotypes and prejudices with potentially harmful consequences - and how to use them with care.
Natural Language Processing

GPT-3 and beyond - Part 2: Shortcomings and remedies

By Fabian Gringel • October 24th, 2021

Here I explain in which situations GPT-3 fails and why it is far from having proper natural language understanding, which approaches can help to mitigate these issues and might lead to the next breakthrough and what alternatives to GPT-3 there are already.
Natural Language Processing

GPT-3 and beyond - Part 1: The basic recipe

By Fabian Gringel • September 27th, 2021

In this blog article I will explain how GPT-3 works, why some people think it’s dangerous, and how you can try out a GPT-3-like model yourself for free.
Projects

21 questions we ask our clients: Starting a successful ML project

By Emilius Richter • May 21st, 2021

We discuss what questions should be considered and answered up front to launch a successful machine learning software project.
Natural Language Processing

How to extract text from PDF files

By How to extract text from PDF files • August 17th, 2020

In the following I want to present the open-source Python PDF tools PyPDF2, pdfminer and PyMuPDF that can be used to extract text from PDF files. I will compare their features and point out some drawbacks.
Natural Language Processing

BERT for question answering (Part 2)

By Mattes Mollenhauer (PhD) • July 2nd, 2020

Here we are going to have a closer look at BERT - a state-of-the-art model for various problems in NLP. We will use BERT to tackle the problem of automated question answering with biomedical research papers as a specific use case.
Tools

The best free labeling tools for text annotation in NLP

By Fabian Gringel • March 30th, 2020

In this blog post I present the three best free text annotation tools for the manual labeling of documents in NLP projects. You will learn how to install, configure and use them and find out which one of them suits your purposes best. The tools I'm going to present are brat, doccano and INCEpTION.

Use Cases in Back Office

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Automated question answering for CVs of candidates

Classification of customer requests

Classification of PDF documents

Extracting information from tables

Extraction of entities from invoices and orders in SAP

Identification and validation of contract clauses