Deals · Enterprise AI / ConstructionTech
conmeet raises €6M Seed to run the full back-office of a trades business from a single AI platform
A German startup building an AI-native operating system that unifies project management, procurement, scheduling, site operations, documentation, finance, and invoicing for mid-sized trades and construction businesses under a single shared data model.
“AI is now creating entirely new possibilities: processes can not only be represented digitally but also automated intelligently and increasingly executed autonomously. Because conmeet is built on a shared data foundation and an architecture designed for AI from the ground up, we can integrate these capabilities directly into companies' day-to-day operations.”
conmeet (Borken, Germany) has raised €6 million in an oversubscribed Seed round co-led by Reimann Investors Venture Capital and Smedvig Ventures, with May Ventures — which led the company's pre-seed approximately six months ago — reinvesting. The company was founded in 2023 by Benedikt Kisner (CEO), Leandro Ananias, and Lennart Eckerlein, and targets mid-sized trades and construction businesses with 10 to 500 employees across Germany, Austria, and Switzerland.
The problem: disconnected data, not missing software
The construction industry is not poorly served by software. It is poorly served by software that talks to each other. A typical mid-sized contractor or trade business in DACH runs separate tools for CRM, project management, scheduling, procurement, site documentation, finance, and invoicing. Each handover between departments — from the sales team to the project team to accounts — requires re-entering data that already exists somewhere else. The result is duplicated effort, error-prone manual reconciliation, and a fundamental absence of any single view of a project or a business at a given moment.
conmeet's design choice is to start with the data model, not the feature set. Rather than adding a layer of AI on top of existing fragmentation, the platform unifies all operational functions — from the first customer contact through procurement, project execution, and final invoice — on a shared data foundation. Every user in a conmeet-deploying business, from site managers to commercial directors, reads from and writes to the same source of truth.
AI as workflow automation, not as a feature
The platform's AI layer is built into this architecture rather than bolted onto it. Because the data is unified, the system has context that fragmented toolsets cannot provide: it knows what was quoted, what was ordered, what happened on site, and what has been invoiced, all in relation to each other and in real time. That context is the prerequisite for automating recurring decisions — scheduling adjustments when a supplier delays, procurement triggers when materials fall below threshold, invoice drafting from sign-off documentation.
"AI is now creating entirely new possibilities: processes can not only be represented digitally but also automated intelligently and increasingly executed autonomously," said Benedikt Kisner, co-founder and CEO. "Because conmeet is built on a shared data foundation and an architecture designed for AI from the ground up, we can integrate these capabilities directly into companies' day-to-day operations."
Samuel Schuler, Managing Director at Reimann Investors Venture Capital, cited the team's combination of trades-sector domain expertise and software capability as decisive: "conmeet addresses these problems with a central system and a team that combines unique domain expertise across the trades, company building and software development."
The Seed closes six months after the pre-seed and is described as oversubscribed — an early indication that the company has found customers willing to anchor the round based on what they have seen. The capital funds DACH expansion, AI capability development, and team growth. The first 18 months will test whether the automation layer demonstrably reduces administrative overhead at deploying firms, and whether conmeet can convert pilots into multi-year commitments before larger enterprise incumbents begin shipping AI-native modules on their existing distribution networks.
Sources
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