Build the ad dashboard the platform cannot give you
Connect ad spend to real sales, turn the result into a daily decision, and understand when AI-generated content actually needs a label.
Ben Sufiani, Gianna, Paul, Marcus, Alexa Scheffler · The article: Vibe Code Your Ad Reporting Stack
A useful dashboard ends in a decision
Paul and Marcus combine Meta campaign costs with the sales that reached Shopify, including the conversions browser tracking misses. Their dashboard calculates a blended return and adds an action beside each campaign.
The output is deliberately plain: scale, watch, or reduce. The dashboard earns its place by shortening the morning decision rather than by showing more charts.
“This column called action basically tells us what to do now – increase the spend or decrease the spend based on these two numbers.”
The full answer crosses four sources
Ben extends the pattern to a subscription product. Campaign IDs arrive in the first-party database, PostHog describes behavior, Stripe holds revenue, and Meta holds cost.
The agent can join those sources without pretending a cookie captured every sale. That is what lets a B2B product build the same operating view Paul and Marcus use for ecommerce.
“In B2B, you would usually have four sources: the ad engine, your database, your web analytics, and your revenue source like Stripe.”
The automation is trained through daily correction
The reporting loop was not correct on day one. For months, Paul asked for the data, rejected bad joins, and explained what the numbers should mean until the routine became trustworthy.
That history matters. The polished morning action is the result of repeated supervision, not a single prompt that happened to produce a convincing table.
“Every morning I would say the data is bad because of this, and it would fix itself. This is the result of two or three months of work.”
AI labeling depends on who did what
Alexa separates model providers from people using a finished model, and a stylized generated image from a realistic deepfake of a real person. The obligation changes with the role and the use case.
ReguLite turns that classification into a guided conversation backed by a curated knowledge base. It stores the reasoning so a company or lawyer can inspect how the recommendation was reached instead of trusting a confident chat answer.
“I was getting a wall of text. I just wanted to know: can I use it?”
Dive deeper
Read the full reporting method and see how Marcus and Paul supervise the system instead of operating it.
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