Last updated: 2026-07-27
Zentio unifies three shop-floor data sources, ERP, MES, and machine feeds, into one AI-native production planning layer that generates factory shift and production schedules in real time. Instead of digitizing existing spreadsheet workflows like legacy planning suites, it rebuilds scheduling logic around AI agents that re-optimize automatically the moment a machine fails or a shipment is delayed.
About Zentio
Zentio is a Berlin-based production intelligence platform founded in 2025 by Julian Rose, Immo Polewka, and Christophe Kafrouni. It combines mathematical optimization with machine-learning agents to replace the spreadsheets and ERP workarounds that still govern most factory floors. The company raised EUR 1.4 million in pre-seed funding led by HTGF (High-Tech Grunderfonds) and SIVentures in December 2025 to accelerate its mission of rebuilding production planning from scratch around AI. The platform targets three pain points that plague discrete and process manufacturers: low OTIF (On-Time In-Full) rates from rigid planning, hours spent manually rebuilding schedules, and unplanned downtime from poor visibility into machine and workforce constraints. Rather than digitizing legacy planning workflows, Zentio rebuilds the decision-making layer around AI agents that continuously learn from each customer's own production data, aiming to give factory planners a system that adapts instead of one that requires constant manual correction.
Pricing
Custom pricing only. No published tiers. Implementation includes a free Proof of Value (2-4 weeks), a paid Pilot Project (1-2 months), and a full rollout phase. Contact sales for quotes.
Key Features
- Autonomous Production Scheduling: Generates optimized production sequences and shift schedules automatically from real-time demand signals, machine data, and operational constraints, replacing manual spreadsheet planning.
- Real-Time Disruption Re-Optimization: When equipment failures, material delays, or worker absences occur, the system autonomously recalculates schedules before disruptions cascade into missed delivery deadlines or idle machines.
- Skill Matrix Integration: Embeds the full employee qualification matrix directly into the scheduling engine so every shift is automatically staffed with workers who hold the exact certifications and skills required for each task and machine.
- Self-Learning Data Flywheel: AI agents continuously analyze consolidated shop-floor data across production runs, building a feedback loop that improves scheduling accuracy and disruption response over time.
- Scenario Simulation: Lets planners stress-test scheduling decisions by simulating alternative scenarios weeks in advance, so capacity bottlenecks and resource gaps are identified before they affect live production.
- Unified Operational Data Layer: Centralizes fragmented data from ERP systems, MES tools, and shop-floor machines into a single structured layer that feeds all planning and optimization calculations in real time.
- GDPR-Compliant Cloud Deployment: Hosted and operated in Germany under strict data protection standards, with no surprise fees for integration or technical support during deployment phases.
Pros
- Rebuilds planning around AI from scratch rather than adding a layer on top of legacy ERP, avoiding the configuration bloat common in SAP APO or Siemens Opcenter deployments.
- Qualification-aware scheduling reduces HR errors by automatically matching worker certifications to machine requirements, eliminating a manual cross-check step that takes planners hours per shift cycle.
- Short time-to-value: the Proof of Value phase runs in 2-4 weeks using existing production data, letting factories see optimization results before committing to a full rollout.
- GDPR-compliant and hosted in Germany, which matters for European manufacturers subject to strict data residency requirements that US-based alternatives cannot easily satisfy.
Cons
- No published pricing means budget planning is impossible without engaging sales, which creates friction for mid-market manufacturers evaluating multiple vendors simultaneously.
- As an early-stage startup with limited funding to date, the product lacks the enterprise track record that procurement teams at large manufacturers typically require.
- No mobile app or desktop client confirmed, limiting floor supervisors who need plan visibility away from a fixed workstation or browser.
- Integration depth with specific ERP and MES vendors is not documented publicly, so buyers cannot verify compatibility with their existing stack before starting a pilot.
Frequently Asked Questions
How much do you pay for Zentio?
Zentio keeps pricing off its website entirely; every contract is scoped to plant size, integration complexity, and deployment mode. The rollout path runs from a Proof of Value phase through a paid Pilot Project and into a full organizational rollout, with exact figures only available by contacting the sales team directly.
Does Zentio have a free plan?
Zentio has no self-serve free plan to sign up for. What it offers instead is a complimentary Proof of Value engagement, run against a customer's own production data, before any contract or payment is discussed.
Which tools compete with Zentio in 2026?
The primary competitors are Siemens Opcenter, SAP APO, and AspenTech, which are established enterprise planning platforms with deep ERP integration but are built around digitizing legacy processes rather than replacing them with AI. For manufacturers wanting AI-augmented planning inside existing ERP infrastructure, SAP IBP is worth evaluating. For smaller operations, Katana MRP or Fishbowl offer lighter scheduling tools at lower cost. Zentio's stated advantage over these alternatives is its AI-first architecture that rebuilds planning logic from scratch rather than wrapping it around old ERP workflows.
Zentio or Siemens Opcenter: which should you pick?
Siemens Opcenter is an established enterprise suite that digitizes existing ERP and paper-based planning processes, with deep integration built over years of enterprise deployments. Zentio instead rebuilds the scheduling logic around AI agents from a blank slate, with no legacy configuration layer to maintain, but as an early-stage, VC-backed startup it lacks Opcenter's deployment track record. Opcenter suits large manufacturers already standardized on Siemens infrastructure; Zentio suits teams willing to trade that track record for a faster, AI-native Proof of Value.
How long does it take to get going with Zentio?
Most engagements show measurable results within 2 to 4 weeks, the length of the Proof of Value phase where Zentio runs on a customer's own production data at no cost. Teams that see results in that window move into a paid Pilot Project lasting 1 to 2 months inside one designated production area, then a full rollout with ongoing support; every relationship starts with a sales conversation rather than instant access.
HokAI guides covering Zentio
- What Parcel Perform Is Actually For: Parcel Perform's AI Commerce Visibility launch shows who the platform really targets: enterprise brands with complex carriers, not small DTC shops on a budget.
- Best AI Agents for Business Ops in 2026: Buy the Job, Not the Platform: AI agents for business ops actually span five separate jobs, not one: calendar, inbox, project tracking, invoicing and vertical automation for 2026 buyers.