ARTICLY

The challenge many companies face in managing CO₂ emissions lies less in a lack of willingness to reduce them, but rather in the reliable capture and structuring of relevant data. The central bottleneck in today’s carbon accounting is not the calculation itself, but the availability and preparation of emissions-related information. Existing solutions often require extensive manual data entry, rely on complex enterprise systems, or are economically inaccessible for small and medium-sized businesses. At the same time, the majority of emissions originate in supply chains and operational processes that are documented but not systematically analyzed. The CarbonInsights system addresses this exact point by fundamentally shifting the entry point for CO₂ accounting. Rather than requiring companies to manually collect data, the system uses existing business documents as the primary data source. Invoices, delivery notes, and energy bills are processed automatically, allowing emissions-relevant activities to be extracted directly from real operational data. The system architecture combines document processing, structured data extraction, and automated classification. Artificial intelligence is strategically applied to interpret unstructured content, while the actual emissions calculation remains rule-based and transparent. This separation enables both scalability and transparency of results. The result is a structured CO₂ balance, organized by categories such as transport, energy, and materials. Beyond mere reporting, the system establishes a direct connection between emissions and the underlying operational structures. Based on this, prioritized optimization measures are derived that are tailored to the specific business model and identified emissions focus areas. This approach transforms CO₂ accounting from a purely retrospective reporting obligation into a forward-looking analytical tool. By integrating into existing document flows, the CarbonInsights system significantly reduces manual effort and makes sound emissions analysis accessible even for companies without specialized sustainability infrastructure. In a regulatory environment where emissions transparency is increasingly mandatory, this approach offers a practical way to gain reliable insights into your own CO₂ footprint and derive concrete measures for emissions reduction.

GovRadar is a cloud-based AI solution for automated creation of public procurement documents, developed in Germany for public administration and procurement offices. Efficient procurement is a lever for government capability. In practice, the bottleneck often occurs in procurement preparation: skilled worker shortages, lengthy coordination loops, and unclear performance specifications delay projects. In GovRadar’s digital workspace, specialist departments and procurement offices jointly create audit-compliant performance specifications. The process remains entirely digital – without media breaks. AI assistance supports research, text drafting, and quality assurance based on millions of German procurement documents from 16,000 public institutions. GovRadar is hosted and operated in Germany. The focus is on content creation rather than process management, immediately deployable and federally scalable. GovRadar is in use nationwide at more than 600 public contractors with over 4,000 active users (as of 02/2026), including municipalities, state authorities, and ministries.

Kuno Intelligence develops a European AI recorder with a companion app for people and teams who need to document many conversations. KUNO records meetings, phone calls, customer conversations, and on-site notes with visible control, transcribes them, and creates summaries, decisions, tasks, and searchable knowledge from them. The product combines custom-developed hardware with privacy-conscious software designed for Europe. Target audiences include consulting, sales, public IT, law, finance, healthcare, coaching, and operations teams. Kuno helps you be more present in conversations, complete follow-ups faster, and find scattered information again, without sensitive content unnecessarily ending up in insecure workflows.