RevOps AI Lab
Working examples of pipeline risk scoring, probabilistic forecasting, capacity planning, and seller execution workflows built to demonstrate operator-grade RevOps systems.
Overview
A quick introduction to the lab
This lab is a portfolio of applied RevOps tools and workflows I built to show how structured metrics, scenario logic, and grounded AI can support forecasting, planning, and seller execution. It's also an example of how I work with LLMs as a builder. I used AI collaboratively to turn concepts into functioning tools, workflows, and interfaces. The goal is not generic dashboarding, but to demonstrate practical, operator-grade systems that could support real revenue reviews and day-to-day decision-making.
Modules
Designed to reflect real RevOps workflows, from executive reviews to rep execution.
Pipeline Health Command Center
Risk index, funnel constraints, coverage adequacy, slippage pressure, and a deterministic AI quarter narrative.
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Forecast Simulation
Monte Carlo forecast ranges, remaining coverage math, and an AI-generated executive brief grounded in model outputs.
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Sales Capacity Planner
Model quotas, ramp timing, segment assumptions, and hiring scenarios against plan.
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Sales Enablement Assistant
Daily seller workspace for quota pacing, deal prioritization, and AI-assisted call prep.
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What This Demonstrates
What to look for in operator-grade RevOps + AI.
- • Explainable signals, not black-box dashboards
- • Forecasts that reflect uncertainty, not commit optimism
- • AI summaries grounded in structured metrics and scenario logic
- • Clear assumptions, confidence levels, and tradeoffs
- • Practical workflows that could support real operators and sellers
How AI Is Used
Grounded, practical, and intentionally constrained.
- • AI is grounded in structured model outputs and deterministic logic
- • Narrative summaries are paired with visible drivers and assumptions
- • Confidence and tradeoffs are surfaced where relevant
- • Synthetic datasets only — no proprietary customer or employer data