Systems Built inReal-World Environments

Kull's stack had grown across multiple vendors over time — a hosting provider for the frontend, a separate VPS for the backend, Cloudinary for media, and an additional CDN for static assets. Five different invoices, three different deploy pipelines, and incident response that meant pinging the right vendor for the right surface. The team was spending more energy on the seams between platforms than on the product itself.

We mapped every request path, every vendor relationship, and every recurring billing line before touching any infrastructure. From there we designed a single AWS architecture that could absorb the entire stack — compute, storage, media, and edge delivery — and staged the migration so that live traffic never noticed the move happening underneath it.

Cloud billing dropped 40% on the first full month post-migration. More importantly, the team now operates from one console, one bill, and one deploy pipeline. The operational drag of maintaining parallel platforms is gone, and the architecture has room to grow without compounding complexity.

EntryBridge's team was sourcing high-value real-estate leads manually from public Urban Toronto listings. Every morning, researchers spent hours copying details, cross-referencing properties, and ranking which to call first. The signal existed in the data, but it was buried in volume — and by the time the team had triaged it, the most time-sensitive opportunities had already moved.

We built a continuous scraper that ingests Urban Toronto listings as they appear, then layered an analysis engine on top that scores each opportunity against the team's actual qualification criteria — neighborhood, price band, listing trajectory, and a handful of contextual signals tuned with the team. The output isn't raw data; it's a sorted queue of opportunities ready to act on.

Research time collapsed from hours to minutes. Mornings now start with the leads worth calling, in priority order — not the leads researchers happened to find first. The pipeline runs by itself, and the team's attention shifted from data collection to the part that actually drives revenue: the conversation.