Karachi, Pakistan/
← International

AI-Assisted Pipeline Health

Client
Logicose, internal
Role
Designer and builder, daily user
Type
Sales operations
Status
Live, used daily

( Overview )

Forecasts at Logicose missed money already owed, agents could not tell which lead to work first, and churn was eating into growth. I designed an AI-assisted workflow that turns recorded sales calls into a forecast review. I built it myself with GenAI, and my team uses it every day.

It is live. I use it with my team daily, and it feeds a portfolio report for senior leadership.

I also designed the CRM account-planning and pipeline-forecasting structure underneath it, adapted from Salesforce.

What I did
  • CRM account planning
  • Pipeline forecasting
  • Workflow design
  • Call transcription
  • LLM summaries
  • Forecast evaluation
  • Churn reduction
  • Built with GenAI

( The problem )

The forecast left out money that was already owed, and leads were worked in the wrong order.

Forecasting missed balance payments and contract payments. Lead scoring was weak, so agents did not give their time and effort to the right lead first. Pipeline planning was weak and skewed.

Without the whole picture in front of the team, selling got pushy, and churn followed.

( How it fits together )
  1. In

    • Recorded sales calls
    • CRM account plans
  2. The workflow

    • Call transcription
    • LLM summaries
    • Forecast evaluation
  3. Out

    • Forecast review for the team
    • Portfolio report for senior leadership
( The workflow, live )
The forecast view: money expected each month from contract and balance payments, invoices and the weighted pipeline(opens full size in a new tab)
Forecast: money expected each month from contracts, balance payments, invoices and the weighted pipeline. Demo seed data.
The full dashboard: pipeline, performance, lead quality and risk(opens full size in a new tab)
The daily dashboard, end to end. Demo seed data.
The deals table with stage and lead qualification scores(opens full size in a new tab)
Deals: every lead with its stage and qualification score. Demo seed data.
A sales profile: lifetime sales, top industries and services(opens full size in a new tab)
A rep's sales record. Demo seed data.
( What it fixes )05
01

Balance payments

Missed in forecasts before. The review now covers them.

02

Contract payments

Missed in forecasts before. The review now covers them.

03

Lead scoring

Agents can see which lead deserves their time first.

04

Pipeline planning

Planning no longer rests on a weak, skewed view of the pipeline.

05

Churn

With the whole picture in front of us, selling is less pushy and clients' ROI stays protected, which solves the churn problem.

( Process )

How it came together

I designed it, built it with GenAI, and it went live in the team's daily routine.

  1. 01 / Structure

    Account planning

    A CRM account-planning and forecasting structure, adapted from Salesforce.

  2. 02 / Workflow

    AI-assisted design

    Call transcription, LLM summaries and forecast evaluation, designed as one flow.

  3. 03 / Build

    Built with GenAI

    I built the CRM and the workflow myself with generative AI tools, no development team.

  4. 04 / Live

    Daily use

    My team uses it every day, and it feeds the leadership portfolio report.

( By the numbers )

  • 5

    Problems it was built to solve, from missed payments to churn

( Outcome )

It is live and used daily with my team. With the whole picture in front of us, selling is less pushy, client ROI is protected and churn is solved. It also feeds a portfolio report that senior leadership reads.

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