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LAB 01 / Forecasting system

A forecast that explains itself.

What can close, why do we believe it, and what changes the answer?

My role

System architecture, operating logic, interface design, and deployment

Conviction forecastIllustrative data
Current call$1.92M80% of target
Booked$840k
Calibrated pipeline$690k
Inbound + partners$390k
Gap to plan$480k

Three late-stage paths can materially change the call.

The system

The interface replaces a single weighted-pipeline number with an inspectable forecast. Leaders can see the assumptions, pressure-test scenarios, and trace every adjustment back to a commercial signal.

Scenario modelingPipeline calibrationSource lineageExecutive narrative

How it works

  1. 01

    Normalize pipeline and historical conversion inputs

  2. 02

    Calibrate confidence by stage, age, source, and observed behavior

  3. 03

    Expose assumptions through editable scenarios

  4. 04

    Turn the model into a weekly operating decision

Working stack

CRM data / Python / Vercel / Model routing / Scheduled jobs

What it changes

An executive forecast surface that shows the number, the logic behind it, and the fastest path to close the gap.

All names, values, and account information shown here are anonymized or illustrative.

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