Inside adextract: how our AI agents scan millions of ads across platforms

Performance marketers spend hours each week jumping between ad libraries. Google Ads Transparency Center. Meta Ad Library. TikTok Creative Center. LinkedIn Ad Library. Four separate platforms, four different UIs, and zero way to compare competitors side by side. adextract was built to fix this. It is a competitive ad intelligence platform that uses AI agents to search every major ad library from a single interface. This post is a product deep dive: what adextract does, how the AI agents work under the hood, and what you can do with it today.

What adextract does

adextract is a competitive ad intelligence platform built for AI-native performance marketers and agencies. It searches the ad libraries of Google, Meta, TikTok, and LinkedIn and returns structured data about what your competitors are running: which creatives, what copy, which formats, and when they launched.

The platform has three core capabilities: search, read, and compare. Search lets you query by competitor name, industry, or ad format across all four libraries at once. Read returns the full creative asset, copy, landing page URL, and run dates for any specific ad. Compare puts two or more competitors side by side so you can spot differences in positioning, spend strategy, and creative approach.

Unlike traditional ad spy tools that require you to log into a dashboard and run manual searches, adextract is designed to be driven by AI agents. You connect it once to your AI workspace and the agent becomes your competitive intelligence analyst. This architecture choice matters because it changes who does the work: instead of you running searches and building reports, an AI agent does it on your behalf.

The ad library landscape in 2026

Every major ad platform now has a public ad library. Google's Ad Transparency Center launched in 2023 and covers Search, Display, YouTube, and Discovery ads. Meta's Ad Library has been publicly accessible since 2019 and covers Facebook and Instagram. TikTok launched its Creative Center with competitor search in 2022. LinkedIn's Ad Library went live in 2024.

But these libraries were built for transparency and compliance, not for competitive research. Each has different search semantics, different filter options, and different data export formats. A query that works on Meta will not work on TikTok. A filter available on Google is missing on LinkedIn. Cross-platform comparison requires manual tab switching and copy-paste workflows that break concentration.

adextract normalizes all four libraries into a single, consistent schema. A search for "DTC skincare video ads" returns results from every platform in the same format: brand name, ad type, creative asset, copy text, landing page, and run dates. You stop thinking about which library to check and start thinking about what the data means.

How the AI agents search across platforms

Under the hood, adextract runs a multi-agent architecture. When you ask your AI workspace "show me the top five competitors running video ads in the DTC skincare space this month," several things happen in parallel.

First, a query planning agent parses your request and breaks it into platform-specific sub-queries. "DTC skincare" maps to a set of known brands and industry keywords. "Video ads" maps to format filters on each platform. "This month" maps to date range parameters. The planning agent handles the translation layer so you do not need to learn each platform's search syntax.

Second, platform-specific search agents fan out to each ad library simultaneously. The Google agent queries the Ad Transparency Center. The Meta agent hits the Ad Library API. The TikTok agent searches the Creative Center. The LinkedIn agent queries LinkedIn's ad repository. Each agent speaks the native query language of its target platform and handles authentication, rate limiting, and pagination.

Third, a synthesis agent collects the results from all four platform agents, deduplicates overlapping entries, normalizes the data into a consistent schema, and returns a single structured response. The entire round trip takes under 10 seconds for most queries. This is the heart of what makes adextract different: you do not run four searches and stitch results together manually. The agents do it for you.

What the platform captures

Every ad result in adextract includes the full creative asset (image or video), the ad copy, the headline, the call to action, the landing page URL, the platform it ran on, the run dates, and the ad format. For video ads, the platform also captures the duration and the thumbnail frame.

Beyond individual ad data, adextract builds aggregate views: which formats a competitor uses most, how often they refresh creatives, whether they are running more video or static ads, and how their messaging shifts week over week. The platform detects trends automatically. If a competitor suddenly increases their ad volume or launches a new product line, the system flags it.

For performance marketers, this data answers practical questions: what is my competitor testing right now, which angles are they betting on, and how long are they running each creative before refreshing. For agencies, it answers: how do our clients compare to their competitive set, and what creative gaps can we exploit.

Connecting to your AI workspace via MCP

The fastest way to use adextract is through its MCP server. The Model Context Protocol (MCP) is an open standard that lets AI tools like Claude, ChatGPT, and Gemini connect to external data sources. adextract ships a production MCP server that exposes the full ad intelligence engine as a set of tools your AI can discover and call.

Setup takes under a minute. You get a server URL from your adextract dashboard, paste it into your AI client's MCP settings, and sign in with OAuth. Once connected, your AI workspace has direct access to competitive ad data across all four platforms. You ask questions in plain English and get structured results without switching tabs. We wrote about the MCP server launch and the thinking behind it in a separate product update

The MCP server is read-only by design. It pulls data from ad libraries and returns it to your AI. It does not modify campaigns, change budgets, or write back to ad platforms. This is intentional: competitive intelligence should inform decisions, not execute them automatically. You stay in control of every campaign decision.

Real workflows from question to insight

Here are four workflows that work today with adextract connected to your AI workspace.

Competitive landscape monitoring. Ask your AI to pull a weekly report on what ads your top three competitors are running. Which channels are they active on. Has their creative approach shifted. You get a structured briefing in one prompt instead of checking four ad libraries manually.

Creative analysis and inspiration. Describe a competitor's ad angle and ask your AI to find similar creatives in the adextract database. The MCP server queries by industry, format, and messaging pattern. You see what is working across your category without guessing.

Anomaly detection. Set up queries that flag when a competitor suddenly increases ad volume, launches a new product line, or changes their pricing messaging. The platform compares current data against historical baselines and surfaces the shift. You catch competitive moves early.

Campaign planning with live competitor context. Before launching a new campaign, research how competitors are positioning similar products. adextract returns active ad examples, estimated spend ranges, and messaging themes. Your brief is grounded in real market data, not assumptions.

These workflows work with any MCP-compatible AI client. If your team already uses AI agents for competitor research, the MCP server feeds them real-time data instead of stale exports. If you are building a multi-agent ad intelligence workflow, adextract plugs in as the data layer.

What we are building next

adextract is actively in development. Three things are on the near-term roadmap.

Scheduled competitive briefings. Configure your AI to pull a competitive report every Monday morning and post it to Slack or email. The MCP server handles the data, and Slack MCP or Gmail MCP handles the delivery. Zero manual steps.

Cross-platform ad spend estimates. We are building models that estimate competitor spend across Google, Meta, TikTok, and LinkedIn simultaneously. Your AI will be able to query spend data directly: "how much is Brand X spending on Meta this quarter compared to last."

Agentic campaign briefs. The current MCP server is read-only, but we are exploring approval-gated write paths. Your AI analyzes competitor data, drafts a campaign brief, and presents it for your approval. You stay in the loop but the busywork disappears.

If you are a performance marketer or agency team that spends hours each week manually checking ad libraries, adextract replaces that workflow with AI agents that do it for you. Setup takes under a minute, the free tier includes 50 searches, and the MCP server connects to any AI workspace you already use.

Try it at adextract.co. Connect your AI workspace and run your first competitor search in under 60 seconds.

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