Waste & material traceability solution for sustainable facilities
EPR CSRD reporting adds real obligations for waste producers, transporters, and sustainability teams. Extended Producer Responsibility (EPR) and the Corporate Sustainability Reporting Directive (CSRD) both demand detailed, accurate data. In fact, the hard part usually is not missing data. Instead, it is the time it takes to find, filter, and assemble the right data into a compliant report. AI assistants are starting to remove that bottleneck.

Traditional reporting tools need a user to know which filters, date ranges, and categories map to a given requirement. Then comes the export and reformat step. In addition, teams handling both EPR and CSRD often maintain two separate reporting logics. Sometimes these even live in different systems.
An embedded AI assistant changes the starting point. Instead, skip the manual filtered view. Simply ask for the report you need: a packaging waste breakdown by material type for an EPR submission, or an emissions and waste-diversion summary for a CSRD disclosure. The assistant pulls the data and presents it, ready for review.
However, none of this skips the human review step. Instead, it simply removes the friction between a regulatory question and a draft answer. In Evreka360 and WasteDashboard, Archimedes generates these reports on request. Compliance and sustainability teams still handle the review, adjustment, and final submission. This mirrors how Archimedes supports material traceability and municipal operations elsewhere on the platform. For the underlying regulatory context, see the European Commission’s overview of the extended producer responsibility framework.
Moving to AI-assisted EPR CSRD reporting does not require replacing the systems your team already relies on. Archimedes works inside Evreka360 and WasteDashboard, reading the weigh tickets, transport documents, processing certificates, and supplier contracts your teams already record. Instead of launching a separate compliance project, you simply ask a question in plain language and get an answer built from the actual underlying data.
For sustainability teams, this means less time spent manually stitching together spreadsheets for every reporting request, and more confidence that the figures being reported are genuinely traceable back to source records. Start small: ask the AI assistant about a specific material stream or reporting period, and expand the scope gradually as your team grows comfortable asking questions in plain language instead of hunting through filters.