Turning scattered social and API data into something a business can read

The requirement was straightforward to state and considerably harder to build: bring together information scattered across several external platforms and APIs, and make it legible to people who need to make decisions from it.

The data lived in different places, in different shapes, behind different APIs with different rules. Some of it needed collecting on a schedule. None of it arrived in a form that could be charted directly.

The client team was not technical, so requirements arrived as business questions rather than specifications.

The first task was translating those business questions into a technical shape — working out what data was actually needed to answer them, which sources could reliably provide it, and what the platforms' APIs would and would not permit.

Several third-party API platforms were researched and evaluated rather than assumed. Some sources were better served by an established provider; others needed collecting directly. Choosing correctly here determined how much of the system would be fighting rate limits later.

Data ingestion pipelines were built to collect from multiple sources including X and Facebook, with collection and processing automated so the platform stayed current without manual intervention.

API integrations with the major social platforms were developed in Laravel, designed for reliable and scalable data flow rather than one-off pulls — handling the failure modes external APIs reliably produce.

The frontend was delivered in ReactJS against the same backend, so the data processing and the way it was presented were designed together rather than handed across a boundary. That is a meaningful advantage of one person owning both.

The platform aggregates data from multiple external sources and presents it graphically, giving a non-technical team a single place to see information that previously had to be gathered by hand.

Owning the project end to end — design, backend, data processing, frontend and deployment — meant business requirements could be translated directly into technical decisions without a translation layer in between.