AI Data Mapping for B2B Commercial Operations

A B2B wholesale company was receiving product and commercial data from many different suppliers, each with its own file format, naming conventions, and level of completeness.

The challenge

Before the project, supplier data had to be reviewed and transformed manually before the commercial team could use it with customers.

Every supplier sent information in a different structure. Product details, commercial attributes, availability signals, and operational fields had to be cleaned, normalized, and reconciled by hand before they could become usable offer data.

That manual work slowed down sales operations. The commercial team could not reliably generate offers until the data had been checked, corrected, and made consistent across sources.

What DPulses built

DPulses built an AI data mapping layer that works like an intelligent funnel across supplier files.

The system ingests files in different formats, maps supplier-specific fields into a consistent structure, and creates a clean master data layer that downstream commercial processes can trust.

• Ingestion of supplier files across multiple formats
• AI-assisted field mapping and normalization
• Creation of a clean and consistent master data layer
• Reconciliation of product and commercial attributes
• Structured outputs ready for offer generation
• A foundation for future logistics reconciliation workflows

Instead of forcing suppliers into one rigid template first, the system absorbs format diversity and turns it into usable operational data.

Results

The clean data layer gave the commercial team a faster and more reliable base for generating customer offers.

Sales operations no longer depend on manually rebuilding supplier data before it can be exposed commercially. The team can work from a consistent master data foundation, reducing friction between supplier intake and customer-facing sales activity.

• Clean master data layer
• Faster commercial offer generation
• Next logistics reconciliation layer

What's next

The same data mapping foundation is being extended into logistics operations.

The next step is to reconcile what was purchased with what physically arrives, saving time for the logistics team and giving the commercial team visibility even before the inbound goods are physically received.

That means commercial teams can start selling earlier, while logistics teams get a cleaner way to connect supplier information, purchased goods, and actual inbound stock.