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A smarter way to manage soil remediation


Partners in cognitive solutions

WikiNet specializes in the development of cognitive-powered digital products for the environmental sector. The use of natural language processing (NLP) and machine learning technologies allows Wikinet to provide managers of contaminated sites with fast, practical and effective technological solutions that help them select the best remediation methods for each specific site in their portfolio.

Arcbees has been a WikiNet technological partner since 2016. This type of collaborative development approach has enabled Arcbees to provide support on the software engineering aspects of the cognitive solutions developed at WikiNet: WatRem, the first environmental remediation management platform that harnesses the full potential of artificial intelligence, and Trace, a cognitive tool designed to help experts in contaminated soil tracking, certification and disposal.

Our contribution

  • Data strategy
  • Software engineering
  • UX design
  • Infrastructure architecture


The Ministry of Sustainable Development, the Environment and the Fight against Climate Change is impressed. The Port and City of Montreal as well as Hydro Québec have confirmed their interest in requiring the use of Traces-Québec in their daily practices to better control and manage the disposal of contaminated soil.

In a context where seizing market opportunities in a timely fashion are an important factor to be a leader in the environmental field, Arcbees' expertise and flexibility have enabled us to achieve our goals.
Marc Paquet President, WikiNet

Actions Taken

  1. Context analysis

    We worked closely with the WikiNet team in order to establish a structured development approach with a two-fold rationale: to meet a genuine need while taking into consideration the day-to-day realities of the various stakeholders who would be responsible for using and implementing the solutions.

  2. Technology strategy planning

    Once we’d gained a clear understanding of the business context and identified the issues that needed to be resolved, we determined which technology platform to use for our application and which features we needed to develop to enable Wikinet to fully seize the business opportunities available.

  3. Confirmation of feasibility

    We created a first proof of concept to demonstrate the application’s ability to extract the data provided in environmental site assessments, automatically interpret this data and then clearly and reliably predict the best remediation method.

  4. Development of a preliminary version (MVP)

    To attain its objective of quickly positioning itself as a key player in the industry as well as strengthening its agreements with major-league environmental concerns, WikiNet needed a robust application that could be built quickly and would create immediate value.

  5. Data optimization

    The system we set up for data acquisition, processing and visualization enhanced the computational tractability and context analysis, two of the application’s key strategic pillars.

  6. Sustained development, innovation and cooperation

    After successfully building an initial client base and securing important partnerships thanks to their innovative cognitive solutions, WikiNet and Arcbees continue to take concerted action as technological partners pioneering in the field of artificial intelligence.


Sustainable Development

The main challenge

Data security

One of the project’s greatest challenges was ensuring the tractability and security of data on contaminated soil disposal while avoiding distortions of any kind or false information within Trace.

Arcbees encrypted all data plus any changes to this data using a process similar to the one employed by the PGP signature program, widely recognized for its robustness.

This technique enabled us to develop tools for verifying data integrity using local digital signatures, which has simplified the validation and remediation work conducted by a number of external auditors.

Our expertise creates


  • Faster data analysis

    Using tools like natural language processing (NLP) allows WatRem to understand and aggregate all the relevant information provided in the environmental site assessment so that this data can be analysed quickly and efficiently.

  • Streamlined decision making

    Machine learning (ML) enables WatRem to determine the best remediation technology for each specific site, thus making it easier for experts to make insightful recommendations.

Outlook for the future

Make the planet greener

WikiNet adopted a global vision from day one. It now has an extensive track record and the robust technology to guarantee its position as an important player on the world stage and this explains why their solutions are generating interest from international corporations. Arcbees is proud to play a part in enhancing the credibility and robustness of the cognitive products being developed at WikiNet.

Our clients

We build trustworthy partnerships