AI Social Video Automation for Multi-Location Brands

Over the last few weeks I’ve been deep in a project with my friend Tyler of TribalCore.com that sits right at the intersection of the following:

• systems architecture & design
• automation of work through multiple systems, requiring multiple uniquely custom connections
• customized branding for each client that adheres to their unique style guide
• “Applied AI” is me leveraging the best of AI, bridging the gap between my knowledge, skills, and practical application.

I’m designing and building a micro-ecosystem.

A social video engine that can generate templated promotional videos for multiple business locations, using structured brand and location data pulled from a Command Center system, then prepare those videos for distribution to channels like Google Business Profile and, later, other social platforms.

It’s like whipping up some waffles for your kids on their sleepover with half a dozen friends, each fielding you a specific request. No syrup, no butter, no blueberries, keep it all, etc.

What makes this interesting is not just the video output.
It’s the workflow.

A lot of small and mid-sized companies know they should be publishing more location-specific content.

They know they should be showing up more often on Google Business Profile, Facebook, Instagram, and whatever else matters to their customers.

They know consistency matters. But operationally, most businesses are nowhere near set up to produce that kind of content at scale.

That gap is where the real opportunity is.

This project is a strong example of how AI-assisted collaboration can help close it.

Not by waving a magic wand and “letting AI do everything,” but by using AI as part of a tightly scoped production system:

• planning,
• schema design,
• template logic,
• asset handling,
• validation,
• rendering,
• and eventually distribution.

The result is a workflow that gives small companies something they usually cannot justify building by hand or by their budget: repeatable, branded, multi-location video output with far less friction.

The current build is based on AI video automation, TypeScript, and React.

On the front end of the workflow, we ingest brand JSON and location JSON from a central nervous system of a business.

That data includes things like logos, brand palette, typography, business details, and location-level media.

From there, we normalize the data, apply fallbacks where needed, and feed it into a rendering template that produces a polished video for each qualified location.

That sounds simple when summarized in one paragraph. It isn’t.

The hard part is not rendering one video.

The hard part is building the plumbing so a system can render many videos reliably, with the right brand assets, the right fallback behavior, the right naming conventions, and a path toward actual distribution.

That means defining what counts as “ready,” separating optional fields from required ones, handling differences between single-location and multi-location brands, resolving asset URLs, and keeping the pipeline stable enough that someone else can operate it without reverse engineering the whole thing.

I’ve spent most of my energy making the system maintain coherence no matter the situation.

We already have a working live render flow against actual Command Center data.

The engine can identify locations that are opted in, confirm asset readiness, fetch the relevant brand and location records, download the images/logos it needs, normalize those inputs into a stable video model, and render the queue into MP4 outputs with logs.

We also added global timing controls for scene duration, which matters more than it sounds.

Once you’re producing video from templates, pacing becomes part of the product.

A system like this needs to make timing adjustable without turning every creative change into a code archaeology exercise.

The current template direction is intentionally practical: a premium local-business promo with subtle event energy rather than something loud or novelty-driven.

In this first phase, we focused on a World Cup campaign concept, not because sports content is the end goal, but because it gave us a useful test case for event-based creative that can be rolled across multiple locations.

The structure is there now to support future observances, seasonal promotions, and campaign variants.

What I like about this build is that it reflects a broader truth about AI and automation in business right now.

The companies that benefit most are not necessarily the biggest ones. In many cases, the biggest upside is for smaller agencies, local service operators, and multi-location businesses that have real marketing needs but not enterprise budgets. These organizations usually do not need more dashboards.

They need fewer manual handoffs. They need systems that make content production less fragile. They need infrastructure that turns “we should do this more often” into something operationally realistic.

That’s what this kind of workflow can do.

Instead of manually briefing a designer for every location, exporting every variation by hand, renaming files one by one, and hoping the distribution side gets handled later, you can build a system where structured inputs drive branded outputs consistently.

Humans still direct the creativity.

Humans still decide what kind of campaign makes sense. Humans still review tone, pacing, and presentation.

But the repetitive work starts to collapse into a production pipeline instead of an ad hoc mess.

That’s the practical promise of AI collaboration when it’s done well.

It’s also why I think a lot of the public conversation around AI still misses the point.

The value is not only in generating text or images on command.

The value is in helping design and operate systems that connect strategy, content, data, and execution.

In this case, AI has been useful as a thinking partner, coding partner, and architecture accelerant, but only because the workflow itself is grounded in real business requirements.

The result is not “an AI video.” The result is a more capable content operation.

From my side, this project has been especially energizing because it pulls together several disciplines I care about: brand thinking, systems thinking, product logic, and implementation detail. It’s one thing to design something attractive. It’s another to make it repeatable, data-driven, and operationally useful. That second part is where many good ideas stall. The interesting work is in bridging the gap.

There’s still more to do. We’re moving from rendering toward the distribution layer now: event registration, output naming standards, public video repository handling, URL delivery back into the Command Center, and preparing the path to GBP and beyond. That layer matters because a rendered file sitting on a laptop is not a finished business process. A useful system needs to connect all the way through.

But even at this stage, the direction is clear.

This is the kind of work I want more of.

If you’re an agency, operator, founder, or local-business group trying to figure out how to use AI and automation in a way that is actually grounded, I’m increasingly interested in projects where the goal is not hype but leverage.

Especially where the problem involves brand systems, workflow design, structured content, templated output, creative automation, or building internal tools that reduce repetitive production work.

There is a large middle ground between “everything is manual” and “replace the team with AI.” That middle ground is where the smart work is happening. That’s where systems get tighter, output gets more consistent, and small organizations can start acting with a level of coordination that used to require much more overhead.

This Social Video Engine project is a good snapshot of that direction.

More soon as we take it further.