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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

Through dida's expertise in the field of natural language processing (NLP) we succeeded in creating a software for the legal review of rental agreements.

Automatic Checking of Service Charge Statements

With machine learning and NLP: Read about the development of software for the automatic verification of settlements using artificial intelligence.

Semantic Search for Public Administration

Machine learning and information extraction: dida's AI-based algorithm simplifies business registrations through intelligent semantic search.

Smart Access Control with Facial Recognition

Machine learning and security systems: Development of a multi-level system with facial recognition and automated access control using AI.

Numeric Attribute Extraction from Product Descriptions

Machine learning & natural language processing (NLP) for online platforms: Development of a software for information extraction from product descriptions for Idealo.

Extracting information from customer requests

As a machine learning service provider, we used natural language processing (NLP) to make software for extracting information from customer requests.

Blog Posts in Back Office

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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.


Ethics in Natural Language Processing

By Marty Oelschläger (PhD) December 20th, 2021

Learn more about the ethics in natural language processing (NLP), the societal impact of machine learning (ML) & why caution should be exercised.

Natural Language Processing

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

By Fabian Gringel October 24th, 2021

Expand your knowledge about GPT-3 and read here about opportunities, weaknesses & troubleshooting as well as alternatives of the AI-based language model.

Natural Language Processing

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

By Fabian Gringel September 27th, 2021

Read here about how GPT-3 works, as well as its dangers & applications, and learn how you can try a GPT-3-like model for free.


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

By Emilius Richter May 21st, 2021

Read about the 21 relevant questions that should be considered & answered upfront to start a successful machine learning software project.

Natural Language Processing

How to extract text from PDF files

By Lovis Schmidt • August 17th, 2020

Comparison of the open source Python PDF text extraction tools PyPDF2, pdfminer & PyMuPDF. Read about tools for extracting text from PDF files here.

Natural Language Processing

BERT for question answering (Part 2)

By Mattes Mollenhauer (PhD) July 2nd, 2020

Expand your knowledge of natural language processing (NLP) with an application example of the BERT model: automated question answering in biomedicine.


The best free labeling tools for text annotation in NLP

By Fabian Gringel March 30th, 2020

Find out about the best free labeling tools for natural language processing (NLP) text annotation, installation & configuration.

Use Cases in Back Office

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