I build the data, API, automation, and AI infrastructure behind distributed media operations.
I wire together .com analytics, social platforms, paid media accounts, CMS workflows, and AI models so media teams can understand performance, fulfill sponsor obligations, and take action without living in exports.
See CapabilitiesMeridian Media Group
Performance Dashboards
Posts
1,284
+9.2% vs prior period
Impressions
42M
+14.7% vs prior period
Engagements
3.8M
-2.1% vs prior period
Impressions by Platform
A distributed media operating layer, shown in pieces
Meridian Media Group is a fictional distributed media company with a .com property, original series, organic social, sponsored content, and paid promotion across major platforms. These demos use synthetic data and stubbed credentials, but the workflows mirror real builds.
Data Pipelines
Meridian publishes on its .com, YouTube, Facebook, Instagram, X, TikTok, and Snapchat, then supports the content with paid media across the same ecosystem. The core build is reliable API ingestion into dashboards, scheduled reports, and operational destinations like Slack, Teams, Monday.com, or Airtable.
Connected Accounts
Source systems available for ingestion, reporting, automation, and AI enrichment.
Organic social accounts
8 accountsSocial ad accounts
4 accountsGoogle Analytics
2 accountsCMS / metadata locations
4 accountsContent Scoring (AI Powered)
Once the platform data is flowing, assets can be re-ingested and sent through Gemini or other models to generate consistent metadata: topic, format, hook, sponsor presence, talent, tone, audience, and creative attributes. That metadata becomes the foundation for scoring content groups and finding trends that are invisible in platform dashboards.
Content Scoring
Scores blend platform performance, AI metadata, format, audience fit, sponsor context, and engagement quality.
Chef Challenge: 60-second pantry reset
Sponsor-led TikTok cutdown with creator host, product integration, and recipe payoff.
Full Episode: Inside the new creator kitchen
YouTube episode tied to the original series launch with talent-led intro and sponsor mid-roll.
Carousel: Five ways viewers are cooking differently
Instagram education carousel built from episode themes and AI-generated topic metadata.
Article recap: What the episode revealed
.com article package connected to GA4 sessions, scroll depth, social referrals, and sponsor tags.
Static sponsor reminder
Late-flight Facebook image post with low creative variation and narrow audience signal.
Enhanced Sentiment Analysis
Comments are interpreted alongside the original asset, caption, sponsor context, and platform. That lets the model separate a true negative reaction from sarcasm, format fatigue, sponsor-fit concerns, long-form intent, or audience demand for a recurring series.
Enriched Comments
Filterable fields generated from the comment, original asset, caption, and platform context.
"I like this host, but this felt way more like an ad than a normal episode."
Soften sponsor reads and preserve the host's normal editorial voice.
"Please make this a recurring show. The first three minutes were actually useful."
Consider a recurring series and preserve the concise opening structure.
"Love getting the same clip for the fourth time today, really fresh stuff."
Reduce repeat exposure and rotate platform-specific creative variants.
"Can you drop the full interview link? The TikTok cut is too short."
Add a full-interview link and test pinned comments for long-form conversion.
Automated Action Workflows
Because performance data is collected automatically, workflows can watch for spend, impression, delivery, pacing, or engagement conditions and act directly on the asset or campaign: change budgets, update statuses, extend end dates, send approvals, or update CMS records.
Rules Engine
Metric thresholds trigger predefined actions with governed execution modes.
Impressions milestone
Sponsor A launch reel
Campaign fatigue control
Meta Ads · Sponsored Clips
Sponsor underdelivery
Sponsor B flight
Approval queue
1 pendingSet Meta Ads · Sponsored Clips status to Disabled after frequency crossed 4.5.
Audit log
9:42 AM · Slack sent: "Reached 100K Impressions"
Yesterday · Status change queued: Disabled
May 22 · Budget change +12% TikTok / Meta
Assets, CMSs and MRSS Feeds
Media assets can be pulled from social platforms, stored in Google Cloud Storage, enriched with AI and CMS metadata from tools like Monday.com, Airtable, and Google Sheets, then exposed through REST APIs or MRSS feeds. The feed stub is live at /api/dam/feed/mrss.
Media asset ingestion
Photos and videos stored in GCS with custom metadata attached from AI models, CMSs, and platform performance data.
GET /assets
GET /assets/{id}
GET /feed/mrss
// optional query params
?type=video
?metadata.sponsor=HearthlyMetadata sources
Custom MCP Servers
Some client tools do not have first-class Claude or ChatGPT connectors, and some connectors can read but not write. Custom MCP servers make those APIs usable inside AI workflows: create files, update CMS items, change campaign records, pull reporting context, or trigger internal systems.
MCP Workflow
Mock AI workflow using client-specific read/write tools.
Which sponsored posts are underdelivering, and can you create a Google Doc recap plus open Monday.com follow-up items?
Tool registry
Execution trace
I found two underdelivering sponsor flights, created the recap doc, and opened Monday.com follow-ups with affected assets, pacing gaps, recommended actions, and owners.
Integrations
server.tool("create_google_doc", {
title: z.string(),
summary: z.string(),
rows: z.array(reportRowSchema)
});Previous Works
Use the placeholder password 'media' for this design pass. In production, this becomes an environment-backed password gate with session storage.
Request or enter access
Veteran data and API engineering for modern media teams.
I specialize in the unglamorous but high-leverage layer between media platforms, warehouse tables, dashboards, AI models, and operating workflows. That means OAuth, pagination, quotas, schemas, backfills, data quality, reporting methodology, and the practical API work needed to make distributed performance measurable.
Engagements usually start with mapping the source systems and business questions, then move into scoped pipelines, dashboards, automations, AI enrichment, and documentation that lets non-technical teams trust and use the system.