We have built a robust, industrial, strength platform that can process up to 20 million transactions a day. The platform has a set of configurable production and development tools that we use to create our products. It has 4 components Digitisation Loaders that structure and import data into the Digital Cash Ledger (DCL) DCL – a PaaS Digital Contracts management platform Micro-applications- a series of process specific UIs APIs – for easy integration and deployment.
|Technologies||Infrastructure as code, Custom framework based on Python and Django, Postgresql, Machine Learning|
|Security strategy||Our current version of the framework integrate three levels of standard security for data protection. First we apply to all users, even to our staff or the automated scripts, controls of what type of data they can create, access and update based on their internal roles. Then all business data is attached to an entity owning this data, so only users that have been authorised by the client or have an administrative role can see those data. This filtering is done at the lowest level of database request, so the data not accessible never leave the database. And finally only identified servers can access the database through reverse proxies. To this standard approach we have added specifically for Fennech, and it's clients, the use of V-Racks from the OVH infrastructure which allow use to use the full extend of the data protection from OVH (DDoS, load balancers, multi country data centres, session expiration and CRSF management).|
|Business domain||Finance, insurance and banking||Project management approach||TDD, Agile, Product driven, ISO 27001||Project team size||4|
|Maturity level (TRL DOD)||7||Reference||fennech.com|
Security observations and dashboards
We were asked by the Canadian government to answer to a challenge with our partner Drone Vision International (DVI). The goal is to deploy at the lowest cost possible an integrated system to detect and prevent ground and air intrusion into Federal prisons.
This is a multi layered solution involving many technologies and very sophisticated deep learning algorithms to help decision making based on pattern recognition. Integrity of the event logs are based on DLT technology. Ethereum is the current framework used for non financial DLT.
The project is done in three phases. We are at the proof of concept phase.
|Technologies||Infrastructure as code, Custom framework based on Python and Django, Postgresql, Deep Learning (neuraxle), mobile application, DLT (Ethereum)|
|Security strategy||This project is not yet in the commercialisation phase, but the strategy is the same as Fennech. The physical security will be based on high level of encryption enclosed networks.|
|Business domain||Prison security||Project management approach||TDD, Agile, R&D process||Project team size||4|
|Maturity level (TRL DOD)||4||Reference||DVI.com|
Since 2014 we have build different bridges between behaviour pattern recognition in the real world and in virtual worlds. This has allowed us develop many tools that are now used in specific product development as the others above.
This was first built around R&D projects that we were asked to help with, in animal behaviour and human behaviour science. Through some work in gaming and augmented reality, we were able to bring this very niched expertise to a broader market and we use it to help create user experience based on a AI first approach as well as monitoring long-term behaviours in finance and other type of applications like security observation. We use both Machine learning for optimisation purposes and Deep learning to tackle bigger volumes of data.
|Technologies||Infrastructure as code, Custom framework based on Python and Django, Postgresql, pandas, deep Learning (neuraxle), mobile application, 3D|
|Security strategy||The different strategies used are dependant on the actual context of implementation. In goggles 3D mobile application, there is almost no security, where personal data is involved the security is the same as in banking applications.|
|Business domain||Multi domain||Project management approach||TDD, Agile, R&D process, big data analysis||Project team size||6|
|Maturity level (TRL DOD)||7||Reference||---|
Nothing better than the words of the founder of Miniminus to present this project in which we were greatly involved.
We are Miniminus, a new publishing company of children’s pictures stories, based in Montreal. We’re a startup, 100% digital and 100% original. We aim to do publishing differently, by rethinking the balance between author and reader. Our business model is based on two key principles: membership for readers and profit sharing for authors.
We work with creative storytellers (writers and illustrators). We develop original and innovative content. And we distribute the stories through our app, in six languages.
|Technologies||Framework based on Python and Django, Postgresql, mobile application|
|Security strategy||This application needs security around personnal data. The standard functionnalities of the framework are covering this aspect.|
|Business domain||Youth publications||Project management approach||Agile||Project team size||3|
|Maturity level (TRL DOD)||9||Reference||miniminus.com|