How can we better analyze the large amounts of data collected until today, and help the insurance, financial services and healthcare sectors invent better data intelligence ?
DreamQuark puts its intelligence into developing the best algorithms, to detect, very efficiently, rare and otherwise invisible phenomena in a wide variety of data types (images, texts, audio files...)
DreamQuark develops a platform to facilitate access to this expertise of large datasets analysis by offering a transparent solution, with results accessible to all types of business users.
Reducing costs - AI has shown very efficient results at identifying costs across the value chain. Use cases include fraud detection, churn analytics, risk pricing, product segmentation and customer qualification. By gaining a better understanding of their costs, and prioritizing accordingly, professionals will be free to take on a more strategic role.
Targeting Emerging Risks & Product Innovation - New risks are emerging in financial services, and analyzing these trends to evaluate if there is a fitting insurance market for these risks is now a natural machine learning task.
Automation - AI is improving abilities in customer interaction, resolution times, and delivery speed to market of new products. This efficiency is the result of AI accelerating decision-making (automated underwriting, auto-adjudicating claims, automated financial advice).
As former particle physicists, we use our expertise of large datasets analysis and efficient algorithm design to reinvent how data analysis is performed in the insurance, financial services and healthcare sectors. We know how to deal with complex signals, and how to detect even the smallest, invisible effects. Yet, our expertise is not enough, and we believe that it is the business knowledge of our clients' experts that give the final value to the results obtained!
Deep-learning models can be tricky to explain. We worked to develop a solution that not only comes up with results, but also explains the process through which a decision was reached. Learning which element triggered the decision will be a decisive factor in understanding your results, and focusing efforts on key parameters.
We currently develop technologies related to deep neural-networks with sparse architectures that can unveil new patterns inside the input data. This sparcity allows for an higher accuracy and speed. This work combines our knowledge of theoretical physics and artificial intelligence. We apply these new models to both structured and unstructured data, and we transform the initial data in order to create a perfect match between the data representation and the algorithm we want to finally use. Our goal has been to combine different algorithms to benefit from the different characteristics of these algorithms and create analyses that can easily generalize. We embed these algorithms, first trained on specific datasets, into innovative applications for insurance, financial services and healthcare professionals.
Images, time series, texts, audio recordings, Excel files... We treat datasets of all sizes, both structured and unstructured. We also automated a large part of the data pre-processing in order to obtain your results faster.
We develop Brain, a plateform for business users to train and apply models to their specific business case. Our clients are able to build dedicated models quickly, and to easily update these models with new data when needed.
Our platform is a White box: every decision made by the model can be explained, thanks to visualizations illustrating the variables responsible for building a specific model, a specific cluster, or even a specific profile.
We help you embed custom models in your digital systems or infrastructures. You can also use the model data generation abilities to explore new scenarios and build new strategies.
Chief Executive Officer and FounderNicolas is the founder of DreamQuark. He came up with the idea during his PhD in particle physics, and decided to develop a solution that would benefit from the performance of deep learning, accessible to all. He is passionate about his team and vision
Chief Research OfficerTrial-and-error expert
Chief Sales OfficerThe 3310 of data science
Chief Finance Officer360°
Research ScientistDungeon Master
Research ScientistAndrei studied in Russia before moving to France, where he obtained his PhD in theoretical physics. Currently occupied with different kinds of computer vision tasks, he is inspired by the problems connecting physics, computer science and biology. Otherwise he enjoys expressing himself by means of music on a dance floor or in a karaoke club.
Fullstack developerIT Padawan
PhD StudentInterdisciplinary warrior
Data ScientistDeep punster
Pre-Sales ManagerBefore joining DreamQuark, Jean-Marc has evangelized predictive modeling software for the Oil & Gas industry in Middle East, Latin America, Asia and Europe. He is now sharing his passion for deep learning. He holds a Master degree of Geology from ENSG. Away from DQ, he spends his time learning new skills, taking pictures, or climbing.
AssociateMathias is an expert in strategy with a DBA with the highest grade. During his PhD, he studied the impact of work on the health and well-being aspects. He is an entrepreuneur and started his own company HumanBet. He brings his incredible vision and experience to our company !
Artistic Director and AssociateStephane is the artist of the company. Expert in image management and design, he worked in the past in huge projects such as the Evian trademark image, and created his own company Odysseus Communication. Stephane gives his genious touch to our projects!
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