a galaxy of data

SaaS platform
for automated categorisation and mapping
of digitised documents using
machine intelligence semantic extraction

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 850053.


Disrupting insurance
with Artificial Intelligence

The aim of the omni:us project is to disrupt the insurance industry with a highly scalable, B2B, SaaS platform to automate the processing of insurance claims within the business segment of Insurance Claims & Policy Management.

The Problem


of claims are
manually keyed

With 7.2 billion claims and 5.2 billion contract documents processed annually worldwide (5.8 billion claims and contracts in the EU), the Financial Services sector could save an estimated €73 billion in processing costs by the automation of manual document processing.

In 2015, the European Insurance market (Non-Life (P&C) & Health) amounted €470 Billion with a CAGR of 1.5%. Inefficiencies due to manual operations within the Insurances segment were estimated at €25 billion in Europe in 2015 (€75 billion globally). We estimate that the Insurances market in 2018 has potential for AI and robotics-based optimisation technology services valued at €300 million in Europe, and €2.5 billion worldwide, with strong growth potential in both cases.

Within the insurance sector, claim settlements consist of multiple, semi-structured documents with arbitrary layouts. To ensure proper reimbursements and to prevent fraud, these documents need to be checked for completeness and formal requirements. Preparatory data extraction is time-intensive and currently requires a lot of manual work. To remain competitive, enterprises need reliable, fast and efficient automated document processing.

The omni:us Solution

omni:us is an AI-based software for automated document processing, which provides classification, extraction and mapping. It is the only system which combines computer vision (OCR), handwritten text recognition (HTR) and natural language processing (NLP) to deliver market leading extraction results and quality.

To address the problem, we plan to develop a B2B SaaS solution that will:

  1. Provide our customers software tools to improve their processes and turnover.

  2. Provide a platform on which 3rd party developers can offer novel features, extensions and add-ons.

Increasing efficiency at scale.

omni:us will provide insurance enterprises with automated document processing software to reduce the cost and increase the efficiency, speed and quality of data extraction and archiving compared to existing manual and digital method providing:

Faster Processing.

85-95% faster processing than manual document processing.

Better Quality.

85%-95% of processed documents need no further quality assurance.

Lower Cost.

Up to 90% of cost savings per document compared to alternative methods.

Ease of Use.

State-of-the-art user interface & seamless integration.

About Qidenus Group GmbH

In 2015 Sofie Quidenus Wahlforss and a highly professional team with an impressive entrepreneurial track record formed the Qidenus Group GmbH as a new start-up in Berlin as an Artificial Intelligence as a Service (AIaaS) provider for cognitive claims management. Built on a fully data-driven approach, omni:us is transforming the way insurers interact with their insured parties. It provides all the necessary tools and information to make fast, transparent and empathetic claims decisions, whilst improving operational efficiency and reducing loss adjustment expenses.

The company is headquartered in Berlin, with research partners in Barcelona and representations in the UK, France and the United States.

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omni:us is proud to be one of Europe‘s Horizon2020 SME-Instrument and Fet Flag Champions.
We are grateful to have received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 820323; 850053.

Co-financed by European Fund for Regional Development (EFRE)
Pro Fit-Project "Vollautomatisierung der Wertschöpfungskette im Digitalisierungsprozess von Archivdaten" with support of IBB/EFRE in 2016/2017.