Platform ROAS is lying. Scale on the truth.
Forecasts that sharpen with use. Scenario tests before you spend. Attribution that reconciles to your P&L instead of repeating what platforms self-report.
Four ways the dashboard job fails operators every week.
The work isn't the strategy. The work is fighting the data layer to find a strategy. These four failure modes show up across nearly every growth team we talk to.
Platform ROAS is inflated
Meta says 4.2x. Google says 3.8x. The P&L shows 2.1x blended. Self-reported ROAS overlaps and overcounts; the dashboard repeats whatever each platform claims.
Scaling hits a wall you can't see
Spend goes up 30 percent and ROAS drops 40. Without marginal-return curves, every scaling decision is a coin flip and every retreat costs another week of stabilized CPMs.
Testing new channels is expensive guessing
Twenty thousand dollars to find out whether TikTok works. Six weeks for the first read. No way to model whether the lift is incremental or just cannibalizing Meta.
Performance drops compound silently
A CPM spike on Tuesday becomes eight thousand dollars wasted by Friday. The Friday review is the first time anyone sees it.
Five hours every week pulling four exports into a sheet that nobody reads after Wednesday.
Reconcile, forecast, test, deliver. One workforce, four functions.
Parker debiases attribution. Felix sharpens forecasts. Sam tests scenarios before money moves. Dana keeps the data layer honest. The other three agents fill out the workforce.
Parker
Attribution
Reconciles platform self-attribution against your P&L. Surfaces non-incremental spend that platforms claim but didn't drive. Architecture target: identify and reallocate the share of spend that doesn't show up as P&L revenue.
True incremental ROAS, debiased per channel.
Felix
Forecasting
Forecasts revenue, AOV, and ROAS by channel. Tracks every prediction against actuals; tightens the model on every miss. Architecture target: climb from 78 percent accuracy in month one to 91 percent by month nine.
From 78 percent to 91 percent across the 9-month pilot structure.
Sam
Scenario Testing
Models budget shifts, channel entries, and creative refresh sequences before they cost money. Returns confidence intervals, not gut calls. Architecture target: scenario tests that complete in 30 seconds with hard CAC and margin constraints applied.
Test before you spend, not after.
Dana
Unified Data
Builds the unified data layer across Meta, Google, TikTok, Shopify, GA4, Klaviyo. Reconciles spend, revenue, and conversions every night. Architecture target: the rest of the workforce reads from one set of numbers, not four.
One source of truth across every platform.
The other three agents fill out the workforce. See all seven →.
Concrete deltas. Targeted by architecture.
Four metrics targeted by the 14-day pilot structure. Based on the architecture and operator conversations about where the dashboard job is broken.
Architecture target across the 9-month pilot structure. What compound learning is built to deliver.
Architecture target: Dex auto-generates and delivers the recaps to Slack and Sheets, formatted per recipient.
Architecture target: Sam runs scenarios in seconds. The output is a confidence interval and a recommendation, not a meeting.
Architecture target: catch CPM and CTR drops the same day they happen, before the budget compounds into real waste.
Questions growth teams ask
How is this different from Triple Whale or Northbeam?
Those tools focus on attribution alone. Cresva is a workforce: attribution plus forecasting plus scenario testing plus institutional memory plus reporting. Seven specialized agents share one memory and one context, so specialization does not fragment the answer.
What does Parker do that Meta's CAPI doesn't?
CAPI improves Meta's view of conversions; it does not debias them. Parker reconciles Meta's self-attribution against your P&L and against holdouts, so the number you see is the number that survived the calibration.
How accurate are the forecasts?
The architecture targets 78 percent accuracy in month one and 91 percent by month nine. Felix tracks every prediction against actuals and tightens the model on every miss. The exact number depends on the volatility of your category.
Can we test budget scenarios without spending?
Yes. Sam simulates budget shifts, channel entries, and refresh sequences using your historical data and Felix's elasticity curves. You see projected outcomes with confidence intervals before any money moves.
How long is setup?
Five minutes via OAuth: Meta, Google, TikTok, Shopify, GA4. First insights inside 48 hours. Full compounding effect rolls in across the 14-day pilot structure.
Growth teams not the right fit?
Ready when you are
Run a 14-day pilot, or see an agent in action.
The pilot connects all seven agents to your real accounts. The mechanism page walks the orchestration end to end.
Looking for a deep dive? See Felix forecasts →, Parker debiases → or Sam tests scenarios →.