Before a customer call, a salesperson needs a simple answer: which orders need attention, and why? When order details and delivery updates sit apart, even a routine follow-up can mean another message to operations.
This proposed workflow connects three pieces of work: data engineering prepares the records, analytics shows the priorities, and AI helps people explore the information behind them. The diagram above shows how those pieces fit together.
Data Engineering: make the records usable
SAP HANA holds the business records. In this architecture, Azure Data Factory copies the agreed order and delivery data on a schedule. The pipeline brings related records together and applies the rules the business has agreed.
For instance, an order can be marked for follow-up when its promised date has passed and delivery has not been confirmed. Missing dates and unmatched records need their own checks; they should not quietly disappear from the report.
Azure Synapse Analytics stores the prepared data separately for reporting. Customer identifiers, order statuses and refresh times give the downstream tools a consistent starting point.
Analytics: show what needs attention
Power BI turns the prepared records into a sales follow-up view. A salesperson can review overdue or partially delivered orders, see the customer involved, and check when the information was refreshed.
The report should answer a business question, not simply display more charts. Here, the question is which customer conversations need preparation today. Measures also need clear definitions: the full value of a flagged order is not automatically overdue revenue.
AI: explain the records behind the report
A company assistant adds another way to explore the same information. Someone might ask, “Why does this order need attention?” The intended answer explains the recorded status and points back to the supporting order or delivery record.
Azure AI Search retrieves relevant approved information; Azure OpenAI uses that context to compose an answer. This approach is called retrieval-augmented generation, or RAG. It connects an answer to retrieved material, but does not guarantee that every answer is correct.
If there is no confirmed delivery date, the assistant should say so. It should not invent a promise to the customer. Access checks, source references and answer testing are part of the implementation.
Follow one order through the workflow
Consider a fictional order for Northline Supply. Its promised delivery date has passed, but the records contain no delivery confirmation. The pipeline applies the agreed follow-up rule, and the report brings the order to the salesperson’s attention.
The assistant can explain why the order was flagged and identify the supporting record. Operations still needs to confirm the next delivery update before sales makes a commitment. Each tool supports a different part of that decision.
Start with one decision your team needs to make
A useful first scope is one team, a defined set of records and a small number of questions. Before building, agree on:
- The decision: which follow-ups should the report help people prioritise?
- The information: which records can be read, by whom, and how often should they refresh?
- The checks: what makes a status reliable, and who reviews missing or conflicting data?
SAP access, licensing, system capacity and the existing reporting environment also shape the design. This page describes a proposed architecture with fictional data; it does not represent a client deployment or measured results.
Varveta can scope the data pipeline, reporting and assistant around those decisions.