AI-native planning platform for better business decisions
Model your business, simulate scenarios and align finance, operations and supply chain to decide with confidence
Solutions that add value to your business
A single model that combines statistical forecasts, market signals, deviation control, and commercial goals so that every projection reflects the reality of the business. Its AI agents detect variations in time, read patterns in real-time, and accelerate scenario analysis.
Faster, better-aligned demand decisions.
A single model that connects stock, replenishment, distribution, and suppliers based on service, cost, and working capital goals. Its AI agents anticipate shortages and surpluses, simulate scenarios, and optimize inventory with greater visibility and less tied-up capital.
Improve visibility and reduce tied-up capital.
A single model that connects capacity, production, and materials so that every operational decision responds to real constraints and financial goals. Simulate scenarios, measure their impact on service, costs, margins, and capacity utilization, and adjust plans when demand changes.
Its AI agents detect bottlenecks, prioritize orders, and streamline decision-making.
A single model that connects budgets, projections, costs, margins, and financial statements to see the financial impact of every operational decision. From management control and OPEX/CAPEX planning to consolidation and closing, the entire organization works with consistency and traceability. Its AI agents accelerate simulations, anticipate deviations, and evaluate scenarios for greater predictability and control.
More predictability. More control.
How does Agentic AI work in Pyplan?
Pyplan agents reason based on the company's Corporate World Model: a living representation of how the business operates.
Understand how the business works
Integrate data, business rules, and constraints across demand, supply, inventory, capacity, and finance into a single model.
Simulate before you decide
They evaluate the impact of each scenario on inventory, service levels, margins, and cash flow before executing.
They explain every recommendation
They show the data, logic, and assumptions behind every number, with trade-offs in plain sight. No black boxes.
The team decides
The AI proposes; planners validate and decide. Every actual result feeds back into the model, which learns with each cycle.
AI-native. Human-driven.
Model. Simulate. Decide.
Model
Design intelligent strategies based on real data. Anticipate scenarios, set clear goals, and align resources to maximize impact from the very first step.
Simulate
Evaluate scenarios in seconds and visualize the impact before committing resources. Test every decision under uncertainty and understand the trade-offs with total transparency.
Decide
Make decisions aligned across departments, with visible logic and clear consequences for service, cost, and profitability. AI and people working on the same model, with a single version of the truth.
Discover the supply chain and financial planning platform
Coty redefines its planning: from fragmented processes to decisions with real business impact
When financial complexity requires another level: EDP Brasil redefines its FP&A with Pyplan
From complexity to total control: Coca-Cola Andina redefines its planning with Pyplan
Resources
Nestlé breaks the traditional model and adopts planning that thinks, predicts and decides on its own
Nestlé transformed its planning with a digital flow of DRP, demand sensing and automation that increased accuracy, agility and product availability.
Pirelli is driving a new financial era with an agile, integrated model ready to scale
Pirelli transformed its financial planning by replacing manual processes with an integrated, agile model with instant visibility.
Coty redefines its planning: from fragmented processes to decisions with real business impact
Coty transformed its S&OP by integrating demand, supply and finance into a single model, improving decisions and efficiency.
Learn more about Pyplan
This section answers the most frequently asked questions about Pyplan, how it works, and its capabilities.
How is Pyplan's AI different from the AI we already tried that didn't work?
Most planning AI fails for the same reason: it is a black box bolted onto an existing system. Pyplan is AI-native; AI is integrated into every layer of the Corporate World Model, not added on top. More importantly, every recommendation includes the logic, assumptions, and business impact. If your planners cannot explain why the AI made a decision, they won't trust it. We make that impossible to ignore.
What exactly is the “Corporate World Model”?
It is the living representation of how your business operates: your data (Data Foundation), your coded and versioned business rules and constraints (Business Rules Layer), and the agents that reason over that model to simulate scenarios and recommend actions. This is not a new idea for Pyplan; it is the architecture upon which the platform was built from day one.
Will AI replace our planners?
No. Pyplan’s AI excels at what machines do best: large-scale monitoring, anomaly detection, running thousands of scenarios, and automating routine decisions. Planners retain what only humans do well: exercising judgment on edge cases, aligning different departments, and taking responsibility for the final decision. The result is a faster, better-informed team, not a smaller one.
How do I know I can trust the AI's recommendations?
Don't just take our word for it: every recommendation comes with the data that drove it, the logic applied to the Corporate World Model, and the projected business impact. Planners can challenge any assumption, override any input, and immediately see how that change ripples through the plan. Trust is earned through transparency, not marketing promises.
Does Pyplan use our data to train its AI models?
No. Your business data remains yours. Pyplan models are trained using planning logic and domain knowledge, not your proprietary operational data. Each client's data is kept separate and confidential, and is never used to train or improve our models.
What happens when the AI gets it wrong or the context changes?
That is why the human-in-the-loop approach is a design principle, not just a feature. Planners can override any AI input at any time and see the impact instantly. When conditions change—a tariff, a supply chain disruption, a spike in demand—your team adjusts the assumptions and the Augmented Loop corrects the Corporate World Model. The AI doesn't make the final decision: your team does.
How long does it take to implement and see results?
Pyplan is designed for a fast time-to-value. Because it is built on the open Python ecosystem and integrates with existing data sources, implementation is measured in weeks, not months. Clients like Nestlé and Coca-Cola were running AI-assisted planning cycles within their first quarter, with measurable automation rates and planner adoption from day one.
See Pyplan in action
