The AI ingestion layer nobody sees, everybody depends on.
For a US health-technology company, we built the complete AI ingestion layer and the infrastructure behind it — turning messy health records, lab results, and indicators into clean, structured, AI-ready data, running in production.
AI features are only as good as the data underneath.
The platform wanted to build AI features its users could trust. The data those features needed arrived as health records, lab results, and health indicators in every format imaginable — different layouts, different units, different levels of quality, no two sources agreeing on how to describe the same thing.
You can't run intelligent features on data like that. Before any model could be trusted, someone had to turn the mess into clean, structured, AI-ready data — and turn it reliably, every day, at the volume a growing platform produces. That layer didn't exist yet.
The ingestion layer, and the infrastructure to run it.
Not a one-off script. A production data layer — schema, pipelines, orchestration, and observability — designed to feed the platform's AI features and grow with it.
One structure the AI can trust
We designed the canonical schema the platform's AI features build on — normalizing formats and units, reconciling how each source describes the same concept, so a value means the same thing no matter where it came from.
From raw record to clean data
Ingestion pipelines parse health records, lab results, and indicators, validate them against the schema, and resolve the quality issues that are normal in real health data — turning heterogeneous input into structured, AI-ready output.
Built to run in production
We wired the pipelines into an orchestration layer that schedules the work, handles failures and retries, and processes new data as it arrives — so ingestion is a system that runs on its own, not a job someone babysits.
You can see the data layer
Monitoring and observability across every pipeline: what ran, what passed validation, where data quality dropped. When something looks off, the team finds out before the AI features ever feel it downstream.
Embedded with the team, handed to the team.
Map the data
We worked through the real sources — every format, unit, and quality quirk in the health data — and defined what AI-ready had to mean for this platform.
Build the layer
Schema, pipelines, orchestration, and observability — built privacy-conscious from the start, with data handled carefully and access controlled at every step.
Run in production
We put the layer live feeding the platform's AI features, then hardened it against the messy edge cases that only show up at real volume.
Transfer ownership
The infrastructure, the schema decisions, and the operational know-how are the client's. Their team runs the data layer, and extends it as the platform grows.
A foundation the platform builds on.
The platform's AI features now stand on data they can trust — clean, structured, and consistent, produced by a layer that runs in production and scales as more data flows in. Nobody using the product sees the ingestion layer; every AI feature they touch depends on it.
It was built privacy-conscious throughout — data handled carefully, access controlled — and then handed over. The client's team runs the infrastructure, owns the schema, and extends the pipelines as the platform grows. We built the layer; they own it.
Your AI is only as good as your data layer.
Book a 30-minute call. We'll map the data your AI features actually need, and what a production ingestion layer would take to build — and to own.