Marketing Intelligence Pipeline for Adology.AI

James Donner at Adology.AI needed one place to see what marketing was doing — not another spreadsheet. I built the full pipeline: ingest, enrich, report, and ship it to production.

Client problem

When James Donner brought me in at Adology.AI, the product was solid but the ops side was painful. Campaign data lived in Meta, Google Ads, LinkedIn, and a handful of landing-page tools. Every week someone exported CSVs, pasted them into Google Sheets, and tried to answer basic questions: Which channel drove qualified leads? What did we spend last month by campaign? Where did conversion drop off?

Manual reporting was slow and often a few days stale — fine at seed stage, not when scaling ad spend and proving ROI.

James needed something his team could run every day without calling a developer.

Solution delivered

We started with discovery: map every data source, define the metrics that mattered for Adology, and lock scope before writing code.

I built a production pipeline:

• Ingestion workers pulling ad platform and form data on a schedule
• A central PostgreSQL store so nothing lived in spreadsheets
• Enrichment to normalize campaigns, UTM tags, and lead records
• A React reporting layer the team could filter without SQL
• Automated summary reports pushed to Slack

I owned backend, frontend, and deployment, with Loom walkthroughs and written docs for day-to-day use.

Integrations and scope

Meta Ads, Google Ads, LinkedIn Ads, landing-page tools, PostgreSQL, React reporting UI, Slack.

Verified outcome

The manual reporting loop was replaced with current numbers in one place.

James Donner, Owner at Adology.AI:
"Zohaib built our end-to-end marketing intelligence pipeline — from data ingestion and enrichment to automated reporting. He delivered production-ready full-stack work with clear communication throughout. Highly recommended."

The pipeline has been running in production.

Ongoing responsibilities

Post-launch: documentation and handoff so the team can operate day to day. Further feature work available as an ongoing development engagement.