Actual TeamLens pipeline interface, shown with fictional demo data
The business problem
Give the team review a clear next step.
A team leader needs to understand closed business, the current pipeline, progress toward goals, and which agents need attention. Answering those questions can involve exports, spreadsheets, and a separate list of follow-up tasks.
I built TeamLens around that recurring management workflow: bring records together, calculate a useful view, prepare a conversation, and keep track of the resulting actions.
How the system works
A repeatable path from records to action.
Each stage has a defined job. Structured data supports the review; the team leader decides what to do with it.
01
Import
Map transaction CSV columns, normalize the records, and check for duplicates.
02
Review
Bring closed business, pipeline, goals, and agent benchmarks into a management view.
03
Prepare
Use that business context to prepare coaching notes and suggested priorities.
04
Act
Track action items through pending and completed states, with completion times.
Inside the build
Decisions that make the workflow useful.
Calculate first, then explain
Financial summaries and agent benchmarks are calculated in TypeScript from stored records. AI receives that context to prepare narrative and suggestions, keeping the underlying calculations inspectable.
Make imports repeatable
The import process checks transaction fingerprints against existing records and the current batch. It reports imported and skipped rows, so a repeated export can be handled deliberately.
Connect the review to follow-up work
Prioritized action items have their own status and completion time. The workflow continues beyond a dashboard into work a team leader can review and track.
Start with the export, spreadsheet, or handoff your team already uses. We can define one useful output and the review step that makes it part of the working week.
