AI Acquisition: connected analysis across 200 sales team members.

The problem

Sales performance is difficult to assess when call information and reviews are scattered. Managers need a consistent way to see what happened on calls and where the team may need support.

My contribution

I built systems to analyse performance across 200 sales team members, bringing together information from Teams, Slack, call logs and other third-party tools. Automatic call logging, AI-assisted reviews and scoring gave the team a connected view instead of isolated records. My broader engineering work for AI Acquisition included Python and C++ solutions and internal integrations.

The workflow

A call becomes a logged record, which provides the input for review and scoring. The output supports a person assessing team performance. The diagram is a simplified explanation of that workflow, rather than a production architecture or a screenshot of private customer data.

What the engineering needs to get right

Call data needs to remain connected to the right record. Review criteria need to be explicit enough for a manager to interpret a score. AI-generated assessments need a route for human review, especially where context is missing or a conversation is ambiguous. These are useful questions when scoping a similar implementation.

A view across the team

The result was the ability to analyse activity and performance across 200 sales team members using information from multiple tools. That scale and scope are reported from my work on the project. The public study focuses on the connected analysis capability, rather than claiming a measured increase in sales revenue.

A similar project for your team

Start with one call type and the information a reviewer needs. Agree the review criteria, access permissions and expected output before extending automation to more teams or workflows.