Sam simulates channel shifts, spend changes, and new strategies in seconds. Every scenario priced against Felix's forecasts before a dollar moves.
A new channel mix takes $30K to $50K to validate. Most brands can only afford two or three tests per quarter, so most strategies never get tested at all.
Shift budget on Monday, wait two to three weeks for signal, analyze, decide. By the time the answer arrives, the market has already moved.
A team can realistically test one budget change at a time. Combinatorial questions like Meta up, Google down, TikTok added stay in the spreadsheet, never in the wild.
What happens to Google when Meta shifts? What's the TikTok halo on organic? Channel interactions never show up in a single-axis test.
Each capability maps to a specific job: simulate the budget, find the elastic channel, model the risk, score the recommendation, and stay aware of the rest of the workforce.
Monte Carlo simulation across channel splits, spend curves, and timing shifts. One question becomes fifty priced outcomes.
Example. 70/30 Meta/Google asked; 65/35, 60/40, and TikTok-15 also tested
Sam identifies which channel actually responds to spend changes and which one is already saturated.
Example. TikTok elastic to +40%, Google Brand saturated past +8%
Every strategy gets simulated before commitment. Upside, downside, and break-even priced before any budget moves.
Example. Doubling BFCM spend: +34% revenue, but CPMs up 2.4x, net margin down
Top five scenarios ranked by expected outcome, each with a confidence band and the assumptions behind the prediction.
Example. Shift 20% Meta to TikTok: CAC down 12% by week 6 at 87% confidence
Reads Felix's forecasts, Maya's CAC ceilings, Parker's incrementality. No silo, no manual reconciliation.
Example. +30% Meta shift requested; Felix priced it in 4 seconds with bands
One workweek, hour by hour. Specific numbers vary by account.
Strategist asks: what if we shift 20% of Meta to TikTok for Q2? Maya supplies budget caps and prior TikTok outcomes; the question gets framed in seconds.
Sam fans out 52 variants around the question. 65/35, 60/40, 70/30 with TikTok at 10, 15, 20 percent, plus three timing curves on each.
Felix prices every variant. Each scenario gets a revenue range, a CAC range, and a confidence percentage. Eleven seconds, end to end.
Top five surfaced, side by side. Best CAC at 87 percent confidence: 65/35 Meta/TikTok with a 2-week ramp. Worst case priced too: revenue dip of 6 percent in week one.
Sam ships the recommendation to Slack with the assumption set, the confidence band, and a link to the four other ranked scenarios.
Strategist approves the 65/35 plan. Dex builds the brief; Felix watches the actuals against Sam's prediction starting next cycle.
Competitor doubles spend overnight. Sam simulates 20 defensive counter-moves, ranks the top three, sends to Slack within the hour.
Sam runs on Monte Carlo simulation, channel-level elasticity curves, and shared memory across the workforce. Every scenario gets priced against live forecasts, not stale benchmarks.
Felix's nightly forecasts and historical campaign outcomes from the workforce memory. Sam never simulates against stale numbers or industry averages.
Felix forecasts + reconciled campaign history
Slack for ranked recommendations and confidence bands, Slides for the strategy review deck. Format adapted to the surface and the audience.
Top five scenarios delivered within seconds of the ask
Felix prices every scenario with the current forecast model. Maya supplies CAC ceilings, prior outcomes, and budget constraints so Sam never tests a plan that violates the brand's rules.
Shared memory, not message-passing
Monte Carlo simulation across channel-level response curves. Elasticity discovery refits nightly per account; confidence bands derived from backtested forecast accuracy.
Per-account elasticity, refit nightly
Ready when you are
The pilot connects your accounts to all seven agents. Bring a what-if question; Sam returns ranked, priced answers in under a minute.
Looking for something specific? Felix forecasts the outcome →, Maya holds the constraints → or Parker validates real lift →.
