Turn Data Into Perspective.
Build visual, executable workflows to explore your data, gain perspective and act quickly. Then continuously refine and automate them as the problem evolves.
Build for today's question. Evolve for tomorrow's.
GENIE
The workflow is the analysis.
PDGenie turns analytical logic into a visible workflow. Each node performs an operation, produces an output and passes that output forward. Build visually, inspect directly, and use Python when you need deeper flexibility.
Visual enough to understand
See sources, transformations, calculations, models and outputs as one connected analytical process.
Programmable enough to adapt
Nodes can represent executable Python functions, so visual simplicity does not remove code-level flexibility.
Structured enough to automate
Once an analysis works, save it, reuse it, schedule it and evolve it instead of rebuilding the process elsewhere.
| Product | Location | Sales | Stock | Weeks Cover | Exception |
|---|---|---|---|---|---|
| 1234567 | 2000 | 125 | 93 | 3.2 | Yes |
| 2345678 | 3000 | 87 | 42 | 2.1 | No |
| 3456789 | 4000 | 211 | 301 | 6.7 | Yes |
Don't just see the answer.
See how you got there.
Intermediate outputs are part of the analytical experience. Select a node and review its DataFrame, chart, object, code, log or error. Change the logic and run again.
Execute the workflow or selected path.
Review what each step actually produced.
See how logic and data create the result.
Adjust nodes, parameters or Python logic.
Iterate until the workflow answers the question.
Explore. Understand. Act.
Learn. Adapt. Repeat.
Business problems evolve. PDGenie is designed so the analytical process can evolve with them instead of becoming a frozen report.
AI should be powerful.
And intentional.
AI can be an explicit step in the workflow. Select, filter, aggregate or mask information before it reaches a model, so users remain in control of what is sent and how the response is used.
Approved input
Local when necessary.
Cloud when useful.
PDGenie is being designed so architecture can follow business requirements. Sensitive workloads can stay close to company systems while cloud services can support collaboration, management and shared capabilities where appropriate.
Local / private execution
Keep selected processing close to internal databases, files and approved models.
Cloud capabilities
Use cloud infrastructure for collaboration, workflow management, scheduling and shared assets where it makes sense.
Build for today's question.
Evolve for tomorrow's.
Imagine an inventory risk appears Monday morning. The analysis can start small and gain sophistication as the team learns what actually matters.
Connect inventory and identify the affected products.
Add open purchase orders and supplier lead time.
Add forecast risk and exception rules.
Refine the metric as the business situation changes.
Schedule the workflow, monitor it, then archive it when the problem is resolved.
Add capability.
Don't rebuild everything.
PDGenie is intended to work alongside existing databases, ERP systems, spreadsheets, APIs and BI platforms. The workflow can produce files, alerts, reports or datasets for downstream systems.
Databases · ERP · Excel · APIs · Files
Transform · Analyse · Forecast · AI · Automate
Reports · Excel · Alerts · Existing BI · Business processes
“A useful analysis shouldn't disappear inside a spreadsheet or one-off script. It should become visible, reusable and executable knowledge.”
Turn Data Into Perspective.
Build visual workflows for today's questions, then continuously refine and automate them as tomorrow's problems emerge.