Managing social ad platforms with an AI operator: Meta and beyond

What platform differences actually matter

The differences that matter for an AI operator are the ones that affect what the model can read and draft — not the UI differences, which are irrelevant once you are working through an API.

The meaningful gaps are: Meta's campaign object hierarchy (Campaign → Ad Set → Ad) versus Google's (Campaign → Ad Group → Ad), where budget can be set (campaign-level on Meta versus campaign or ad group on Google), how audiences are defined (Meta's Lookalike vs. Google's Similar Audiences), and how conversion events are attributed.

• Meta — budget can be set at campaign level (CBO) or ad set level; Advantage+ campaigns reduce configurability; API exposes detailed audience and placement controls.
• Google Ads — budget always at campaign level; bid strategy at campaign level, bid overrides at ad group or keyword level; smart bidding requires clean conversion tracking.
• LinkedIn — campaign budget at campaign level; fewer audience signal types than Meta but strong job-title and company targeting; API is less mature.
• TikTok — Spark Ads (boosting organic posts) have different API semantics than standard paid ads; audience export from other platforms not natively supported.

How SpendSignoff normalizes across platforms

SpendSignoff stores a normalized representation of each campaign's state regardless of platform. A campaign_summary object has consistent fields — platform, campaign_id, daily_budget_usd, status, objective, performance_7d — whether the source is Google or Meta. The model reasons about this normalized layer.

When the model drafts a change, it translates back to the platform-native format: a budget change on Meta becomes a CBO campaign-level budget update if the campaign uses CBO, or an ad-set-level update if it does not. That translation happens in the server's policy core, not in the AI client's inference.

Cross-platform reporting: the unified view

The most requested capability for social ad management is a single view of performance across platforms. Logging into Meta Ads Manager, then Google Ads, then LinkedIn Campaign Manager to get a complete picture of weekly spend and ROAS is the kind of context-switching that takes thirty minutes and produces errors from copying numbers between tabs.

SpendSignoff's query_reporting tool returns normalized performance data across all connected platforms in a single response. Ask your AI client "what was my total spend and blended ROAS last week across all platforms?" and get a consolidated answer with per-platform breakdown.

Platform-specific gotchas the model knows about

Some platform quirks need explicit handling in the tool layer. Meta's API returns estimated reach rather than exact audience size for custom audiences, so the model qualifies any audience-size estimate as approximate. Google's Responsive Search Ads report performance at the ad level but not at the headline or description asset level via the standard API — asset-level data requires a separate report query. These are not dealbreakers, but they affect how the model phrases its analysis.