A multi-location marketing platform already automated the busywork of running campaigns - scheduling posts, generating landing pages, tracking leads. The next step was making the platform smarter: instead of just executing what a user scheduled, it needed to recommend what to do next, generate content on its own, and act on channels like Google Business Profile without someone manually logging in every day.

Technology Stack

PythonLLM / GenAIReactJSASP.NET / C#SQL Server

The Challenge

Each business location on the platform generates its own stream of engagement data - post performance, review sentiment, ad response rates - across social media, local ad campaigns, direct mail, and Google Business Profile. Reviewing that data manually and deciding what to post, boost, or respond to next doesn't scale past a handful of locations, let alone thousands.

What We Built

How It Works

Engagement and campaign data from every connected channel feeds into a set of models that score content performance and flag opportunities - a review that needs a response, a post worth boosting, a campaign that's underperforming its budget. Generative AI drafts the actual content (posts, review replies, campaign copy) grounded in the business's own brand voice and past assets, and a human retains a one-click approval step rather than the system posting fully unsupervised.

The Outcome

The platform moved from a tool that executes what users schedule to one that actively recommends and drafts what to do next across every channel it manages. Marketing teams running the platform now spend their time approving and refining AI-drafted work instead of producing it from scratch, and profiles that previously went unmanaged for weeks - especially Google Business Profile - get consistent, timely attention automatically.