The Challenge
An established platform business approached us as they were rebuilding around an AI-augmented operating model. Their existing system worked, but the architecture had been built for a world before machine learning was a first-class input, and bolting AI on top of it would have multiplied the existing fragility.
The Solution
We led the enterprise architecture work - what to keep, what to re-platform, where the AI integration layer should live, how the data pipeline needed to evolve to feed the models reliably. From there we moved into the technical lead role, guiding the build and making the architecture decisions in real time as the system took shape.Architecture decisions of this size are usually outsourced to people who never touch the system after they hand over the document. We stayed in the build.
The Outcome
The client got a modernized, completely stable platform running on a live AI system. By setting up reliable, automated data feeds and staying on to manage the build, we fixed the weaknesses of their old system. They now have a solid foundation built to scale machine learning models without the risk of breaking things.
A Clear Shift in User Experience
These numbers reflect the immediate impact our design brought to the brand’s digital presence.
System Downtime
Data Flow Accuracy
Increase in Operational Efficiency
Project Focus
- AI Architecture & Schema Design: Built a clean structural blueprint to handle machine learning inputs safely.
- Automated Data Pipelines: Set up secure data pipelines to feed clean information directly to the AI models.
- Real-Time Build Governance: Stayed embedded with the team to manage technical decisions and guide the live code.
