The setting

Marlow Freight is a freight brokerage that matches shippers with carriers. You are the sales operations analyst supporting a team of eight account executives, keeping the pipeline, routing, forecast, and reporting honest.

Every mission on this path happens at the same company, so context carries over the way it does in a real job: the data you cleaned in mission two is the data the finance lead questions in mission four.

The missions

1. pipeline checkstarter

Check the Marlow Freight pipeline for the Monday forecast call

The Monday forecast call runs off the pipeline export, and right now that export is not trustworthy. There are deals with close dates in the past still marked open, a probability that does not match the stage, at least one duplicate, and a closed deal sitting in the open number. If you present it raw, the forecast is...

You deliver: A pipeline health check listing the problem deals, the outsized deal, and a cleaned weighted pipeline number.

Scored on: Duplicate and closed deal, Slipped close dates, Stage-probability mismatch, Clean weighted number.

Working from: pipeline.csv, stage_probability_guide.md.

2. routing rulestarter

Write the lead routing rules for Marlow Freight's two new reps

Marlow just hired two more account executives, and inbound leads are being assigned by whoever grabs them first. That leaves some regions and freight types uncovered and overloads the fast responders. You are writing the routing rules that assign every inbound lead to the right rep by territory, specialty, and...

You deliver: A routing rules document assigning each inbound lead by region, specialty, and capacity, including a fix for the uncovered region.

Scored on: Region and specialty match, Capacity respected, Solves the Southwest gap, Usable rules.

Working from: rep_coverage.md, inbound_leads.csv.

3. forecast reviewcore

Review Marlow Freight's Q3 forecast for sandbagging and happy ears

The Q3 forecast rolls up next week and the commit number looks shaky. Some deals are marked commit with no recent activity or a close date already gone, which is happy ears. Others sit in best case that clearly meet the commit bar, which is sandbagging. Your job is to challenge the categories against the rules before...

You deliver: A forecast review flagging miscategorized deals, the corrected category for each, and an adjusted commit total.

Scored on: Catches happy ears, Catches the sandbag, Too-early deal, Adjusted commit total.

Working from: forecast.csv, forecast_rules.md.

4. CRM reportcore

Build the weekly activity report from Marlow's messy CRM export

The VP wants a weekly rep-activity report every Monday, but the raw CRM export is dirty: the same call logged twice, an activity dated in the future, activity types spelled four different ways, and one rep who barely shows up. If you count it raw, the report rewards the person who double-logs and punishes the honest...

You deliver: A weekly activity report with cleaned per-rep counts, the data quality flags, and a note on the low-activity rep.

Scored on: Removes bad rows, Normalizes types, Accurate per-rep counts, Flags the low rep.

Working from: activity_export.csv, reporting_notes.md.

5. process fixstretch

Fix the broken quote-to-book handoff at Marlow Freight

Deals keep dying between quote and booking. The reps blame ops, ops blames the reps, and meanwhile shippers walk to competitors while Marlow scrambles to confirm a carrier. Leadership wants the handoff redesigned so quotes stop rotting in a gap nobody owns. You have the current process and a log of where deals stalled...

You deliver: A process fix proposal naming the bottleneck, quantifying the losses, and redesigning the handoff with owners, an SLA, and a concrete change.

Scored on: Names the bottleneck, Quantifies the loss, Owner and SLA, Concrete change.

Working from: process_current.md, handoff_issues.csv.

How the scoring works

Each deliverable is graded against the rubric written for that mission. Separately, every mission on every path is graded on how you used AI, against the same four criteria:

  • Understood the task. The learner framed the goal for the assistant clearly instead of pasting the brief and hoping.
  • Grounded in the material. The learner directed the assistant into the provided files and based the work on them, not on invented facts.
  • Verified the output. The learner checked claims, numbers, or coverage against the source material before submitting.
  • Iterated with judgment. The learner refined weak parts of the draft with specific follow-ups rather than accepting the first answer.

Both scores, with the work behind them, go on your proof profile. That is what makes a claim like "I can use AI for sales operations" something an employer can check.