A live workspace where 24/7 agents monitor Ads data, learn from attribution drift, optimize channel mix automatically, and manage larger budgets with financial and mathematical models.
Start by switching attribution models, then alter the attribution window and audience quality. The workspace will recompute paths, business metrics, and autonomous optimization actions as one connected system.
Compare Shapley, Markov, and Last Click under the same budget mix.
Disable a channel to see how journey composition and confidence respond.
Check the optimization engine to see which channel is under-credited, over-funded, or ready for expansion.
The workspace keeps three reading layers—controls, path reconstruction, and decision output. On notebook-width canvases they reorganize into a calmer top-to-bottom sequence before expanding sideways on very wide screens.
Stable model and mix tuning.
Journeys and credit shifts with progress feedback.
Metrics, autonomous actions, and budget governance in one glance.
24/7 agents monitor Ads data, rebuild journeys, and trigger budget actions through one control lane, one computation lane, and one decision lane so the product reads like real operating software.
24/7 agents keep watching spend, path drift, conversion quality, and marginal return instead of waiting for weekly reporting cycles.
Shapley keeps updating with a 7 days window and a confidence score of 75/100, so the engine improves as account signals change.
The engine is currently pushing budget from Google Ads toward Organic, replacing manual channel-by-channel adjustments with faster portfolio actions.
Budget decisions stay tied to attributed revenue, blended ROAS, CAC, and marginal ROI so larger budgets can be governed with financial discipline instead of intuition alone.
Shorter journeys still exist in the mix, so identity stitching quality remains the main confidence constraint.
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