Celonis and Databricks are preferred delivery platforms, options rather than prerequisites. Datanovel can also deliver on your existing approved platform, including Microsoft Fabric. The choice follows your procurement priorities, available capabilities, access and operating needs; the initial data review establishes how the agreed scope can be delivered in your environment.
Celonis: connect process data, identify where work stops and bring evidence, business input and follow-up actions into the improvement cycle. For example, an invoice-blocking pattern could lead to a targeted review of missing PO references, an owner decision and tracking of whether the issue is resolved. Delivery can span data extraction and SQL transformations, Knowledge Models, PQL, views and Action Flows, supporting the define, measure, analyse, improve and control stages of DMAIC.
Databricks: explore procurement data, prepare it and prototype rules, models and AI-assisted analysis for early business review. For example, a working prototype could extract service periods from PO descriptions for a buyer to check against the supporting document. SQL, data pipelines and AI functions help turn a requirement into something the business can see, test and refine before wider implementation.
Prediction models are trained on your procurement data and run on your approved compute. They work alongside business rules and evidence matching to detect and assess PO Value Signals™ in already-created POs. A missing reference or an agreed-price exception may be established directly; other PO Value Signals™ depend on learned patterns.
The initial data review checks the data, history, access, compute and timing needed for each PO Value Signal™ before implementation is agreed.
Where LLM support is useful, the model is selected with you and used within permitted AI services and security controls. A retrieval layer can ground messages in approved procurement context, rather than generic model assumptions.