Zentio Raises €1.4M To Build AI-Native Production Planning
Zentio has secured €1.4 million in pre-seed funding to accelerate an AI-native platform for production planning. The round was led by High-Tech Gründerfonds (HTGF) with support from SIVentures (formerly Smart Infrastructure Ventures).
The company says it's building production planning with AI at the core-rather than layering models onto legacy tools. The goal: help manufacturers improve resilience, efficiency, and competitiveness across European operations.
What "AI-native" means for product teams
- Architecture first: AI isn't an add-on. Data models, constraints, and optimization live in the core planning logic.
- Faster iteration: Expect shorter cycles to test planning scenarios (e.g., constraints, changeovers, and shift plans) without manual rework.
- Operational intelligence: Decisions are informed by a mix of historical data, live inputs, and simulation-not static rules alone.
- Agent-based workflows: Agentic automation can handle routine planning steps and escalate exceptions with context.
How Zentio says the platform works
Zentio combines operational data with mathematical optimization and agentic automation to support higher-quality planning decisions. Instead of generating a single "best" plan, the approach can evaluate trade-offs, propose scenarios, and coordinate actions across teams.
The company is investing the new capital to speed development of its agent-based system and widen access as manufacturers manage industrial transformation and growing operational complexity.
Why this matters now
- Volatile demand and supply: Teams need planning that flexes with shifting supplier lead times, quality swings, and workforce constraints.
- Margin pressure: Optimization that reduces changeovers, idle time, and scrap can move the needle quickly.
- Systems fatigue: Many plants still rely on spreadsheets and brittle integrations; an AI-first core can lower manual overhead.
Checklist for a pilot (product development lens)
- Data inputs: What sources feed the system (ERP, MES, QMS, maintenance, forecasts)? How fresh do they need to be?
- Objective functions: Agree on priorities upfront-throughput vs. on-time delivery vs. cost vs. CO₂ impact.
- Constraints model: Include changeovers, tooling, batch sizes, labor skills, maintenance windows, and quality gates.
- Decision rights: Which actions can agents automate, and which require human approval?
- Explainability: Can planners see why a plan changed and what assumptions drove it?
- Fallback plan: Define rollback paths to current processes if data quality drops or integrations fail.
- Integration path: Start read-only, then progress to write-backs into ERP/MES after validation.
- KPIs: Track schedule adherence, lead time, utilization, changeover minutes, scrap, and planner hours saved.
Founding team
- Julian Rose (CEO, co-founder): Background in process and logistics optimization with a sustainability focus, including CO₂-reduction programs and logistics improvements at Ford.
- Immo Polewka (CCO, co-founder): Experience in production-process optimization and industrial business development across chemical and pharmaceutical environments.
- Christophe Kafrouni (CTO, co-founder): Leads product and platform development; built complex cloud-native systems for AI production deployments, working with international research teams and shop-floor stakeholders.
Hiring and next steps
Zentio is hiring across product and go-to-market as it builds out the first generation of AI-native production planning software. If you're evaluating a pilot, assemble a cross-functional squad-planning, operations, data, and quality-to shorten time-to-value and reduce integration risk.
If you're building your team's AI skill stack, you may find these helpful: AI courses by job role.
Your membership also unlocks:
