Automated tender processing

Machine learning algorithm for faster and better tender processing.

The challenge

Simplifying the complex process of (public) tenders in the HVAC construction space.

In the construction industry, tenders (= invitation to tender for a project or part of a project) play a key role in the procurement process for major construction projects. Especially - but not exclusively - for projects coming from public institutions, the tendering process is characterized by complex documentation, inconsistent standards and long waiting times for all parties involved.

Since both Viessmann and its partners (system installers) are constantly confronted with the issues of the tender process, wattx has been looking for a solution that would improve and ease the situation for these stakeholders.

Our solution

A ML-empowered platform that automatically matches relevant products to "Leistungsverzeichnis" positions.

After analysing the whole tender process, the required documents, involved stakeholders, and existing online platforms that are used for public tenders, we concluded that the greatest value for Viessmann in the current environment has to come from a faster and less manual handling of tender requests - specifically the process of analysing and editing the hundred-page long “Leistungsverzeichnisse”. Due to the lack of standardisation in the document structure as well as the product descriptions, it is a task that has traditionally not been easy to automate. Based on our analysis, our long experience in applied machine learning, and recent advances in the technology made it now possible for a smart program to semi-automate this task. Consequently, we built a first prototype of the product to showcase its capabilities.

The outcome

In case you want to chat about this project, please reach out to max.s@wattx.io

Viessmann was convinced of our initial analysis as well as our prototype, and gave us the greenlight to start developing an MVP that will enable the whole organization to improve their internal processes dramatically.

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