Case Study · AI Agent · Social Media
✍️ Bea Lejano · 📅 Jan 2026 · ⏱ 7 min read
Posting consistently on social media is hard enough. But posting without knowing whether any of it is actually working — that’s a different kind of frustrating.
A Philippine herbal brand with a B2B-focused Facebook presence was in exactly that position. They were posting regularly, but had no structured way to evaluate whether their content was reaching and resonating with their Ideal Customer Profile — manufacturing partners, research collaborators, and institutional buyers. The marketing team was making content decisions based on gut feel, not data.
What they needed wasn’t a social media manager. They needed a system that could think like one — analyzing their posts, scoring engagement against benchmarks, evaluating ICP alignment, and delivering actionable recommendations without anyone having to run a manual audit. So I built one.
Layered AI agents working in sequence
Weekly scraping, scoring, and insight generation
Manual audits or report compilation required
Insights waiting for the team every Monday morning
The Challenge: Data Without Insight
The brand wasn’t struggling to post. They were posting consistently. The problem was they couldn’t answer three critical questions about their content:
Manual audits were theoretically possible but not practical. A proper content review takes hours — scraping posts, calculating engagement rates, comparing against benchmarks, evaluating ICP alignment, and writing up recommendations. Nobody had time to do this weekly. And doing it monthly meant acting on data that was already too old to be useful.
💡 The real problem: it wasn’t a content strategy problem. It was a data infrastructure problem. The brand needed a system that could do the analysis automatically — so the team could spend their time acting on insights, not generating them.
The Solution: A Multi-Layered AI Agent System
I built a fully automated social media intelligence system — two AI agents working in sequence, each with a distinct role, scheduled to run weekly without any manual trigger. By Monday morning, the team has a complete analysis of the previous week’s content waiting for them.
Here’s how the system works end-to-end:
Data Collection — Apify Facebook Scraper
The system starts by scraping the brand’s Facebook page using custom Apify actors — pulling post captions, engagement metrics (likes, shares, comments), media links, and images. Custom actors were built to capture multiple images per post simultaneously, giving the AI richer visual context for analysis than a standard single-image scrape. All data flows directly into Google Sheets as structured rows — no manual export, no copy-pasting.
Engagement Scoring — Google Apps Script
Once the data is in Google Sheets, a custom Apps Script calculates engagement rates for each post and scores them against predefined industry benchmarks. Posts that exceed benchmarks are flagged as high performers. Posts that fall below are flagged for review. This replaces manual calculation — and because it’s formula-driven, it updates consistently every week without drift or human error. Image processing is also handled here via Apps Script rather than through Zapier, reducing automation task usage and keeping costs lower.
Agent 1: ICP Alignment Analysis (OpenAI)
The first AI agent reviews each post through the lens of the brand’s Ideal Customer Profile. It’s trained on the brand’s voice, positioning, and target audience — so it evaluates posts the way a strategist embedded in the business would. For each post, it answers: “Is this speaking to the right person? Does the messaging, visual, and angle align with what a manufacturing partner or research institution would respond to?” This produces an ICP alignment score and a brief qualitative note per post.
Agent 2: Weekly Content Strategy Summary (OpenAI)
The second agent zooms out. It takes the full week’s post data — engagement scores, ICP alignment notes, image analyses, and benchmark comparisons — and generates a human-style strategic summary. This isn’t a data dump. It reads like a weekly briefing from a content strategist: what worked, what didn’t, what patterns are emerging, and what to focus on in the coming week. Specific, actionable, written in plain language. This second workflow runs 6 days after the first — staggered deliberately to prevent overlapping executions and ensure the summary draws on a complete week of data.
Delivery — Google Sheets + Zapier Orchestration
Everything is delivered back to a master Google Sheet — structured, side-by-side, easy to scan. The team sees post content next to engagement score, next to ICP alignment rating, next to the AI’s strategic note. The weekly summary sits at the top. Zapier orchestrates the entire sequence — triggering Apify, moving data between platforms, scheduling the two OpenAI agent runs — all hands-free.
The Tech Stack
Five tools, two agent layers, one fully automated weekly intelligence system:
Data collection layer — Facebook scraping with custom multi-image actors
Central data layer — structured storage, engagement scoring, side-by-side output
Computation layer — engagement rate calculation, benchmark scoring, image processing
AI agent layer — ICP alignment analysis and weekly strategic summary generation
Orchestration layer — schedules, triggers, and connects all platforms in sequence
Image storage — post visuals saved and referenced for AI analysis
What the Team Has Now
Every Monday morning, without anyone pressing a button, the marketing team opens their Google Sheet and finds a complete content intelligence report for the previous week.
💬 The shift this creates: the marketing team went from making content decisions based on gut feel to making them based on weekly AI-generated evidence. That’s not a small change — it’s the difference between a content calendar and a content strategy.
What Makes This Different From Standard Social Media Analytics
Most social media analytics tools give you data. This system gives you interpretation.
Is This Right for Your Business?
This type of AI agent system makes sense when:
The system can be adapted for Instagram, LinkedIn, or other platforms depending on data availability. The core architecture — scrape, score, analyse with AI, summarise strategically — applies across channels.
Want an AI System That Thinks Like Your Content Strategist?
If your team is posting without a clear picture of what’s working — and for whom — let’s talk about what an AI-powered content intelligence system would look like for your specific audience and channels.
Work with Me →
Or send a message at [email protected] to start with a few questions first.
About Bea Lejano
Bea is the founder of Digital Freedom with Bea, an AI and automation systems consultancy based in Metro Manila. With 10+ years of corporate operations experience, she builds custom AI agent systems, automation workflows, and operational infrastructure for Philippine businesses using OpenAI, Zapier, Airtable, and the Google Workspace stack. www.digitalfreedomwithbea.com