AI System for Preparing Trial Accounts for Sales Review

The problem

In day-to-day operations, permitted account, company and usage information arrives through different systems and files, preventing the team from reviewing the same case in one view.

As a result, the link to source evidence can be lost while a sales-context summary is prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook personal use or incomplete company information, while incorrect automation risks unfairly excluding an account without human review.

What we set out to improve

• Time taken for the sales-context summary draft to be ready for review
• Correct routing of records involving personal use or incomplete company information to the appropriate specialist queue
• Record of suggestions corrected or rejected by the sales operations specialist

How the system works

1. 01 Retrieve permitted account, company and usage information from authorised sources
2. 02 Verify the source system, record identifier and recency information
3. 03 Map the fields to the target process schema
4. 04 Prepare the sales-context summary draft
5. 05 Compare the draft with business rules and source records
6. 06 Check for an exception: personal use or incomplete company information
7. 07 Have the sales operations specialist approve, revise or reject the suggestion
8. 08 Write the input, suggestion, changes and final decision to the audit log

Methods we used

• Source-traceable data extraction for permitted account, company and usage information
• Structured output that limits the sales-context summary fields through business rules
• An exception gate that separates cases involving personal use or incomplete company information
• A queue that shows the sales operations specialist the source evidence and suggestion together
• Duplicate-processing controls and a reversal record to reduce the risk of unfair exclusion without human review

Where people stay involved

Data and security

Only fields required for the task are processed from permitted account, company and usage information. Source-system permissions are preserved, and personal or commercially sensitive data is minimised before being passed to the model. Every read, suggestion, human change and write to the target system is logged.

Who this suits

Frequently asked questions