The training datayou legally can'tget.
You have a few hundred real records. Your model needs thousands. We generate the rest from the data you already hold. It arrives compliant, validated, and benchmarked against your own baseline before you pay for any of it.
HealthBay AI & Scaler AI Labs
The wall
Your best data is the data you're not allowed to use.
In regulated work, the model was never the bottleneck. The records good enough to train on are the ones legal will not let out of the building.
So teams train on a few hundred de-identified samples and watch the model underperform. Or they wait months for a data-sharing agreement that arrives with the useful fields stripped out. Or they buy a public dataset that looks nothing like their own coding, their own templates, or their own patient mix.
Tools that tidy up the data you already have don't solve this. Neither do generic generators. They produce records that look plausible but carry none of the relationships that make a real one useful: which codes appear together, how conditions cluster, the way a note actually gets written on a Tuesday afternoon.
We start from a small, messy, locked sample. We model what's actually inside it. Then we build you the volume you were missing.
What you get
Three deliverables. One of them is the point.
Every engagement ends the same way: a dataset, a privacy scorecard, and a number that says whether your model got better.
Synthetic records, built to your schema
Built from a sample of your own data, matched to your formats and your coding system. Not a public dataset with your logo on it.
A privacy and compliance report
We measure how close every generated record sits to every real one, flag any personal or health information, and check for copied text. This runs on every batch and comes to you with the data. Your compliance team gets a document, not a promise.
Proof your model improved
We train your model on the synthetic data and score it against your real-data baseline, on your task, using real records you held back.
If the benchmark doesn't move, you don't pay.
We sell a measured improvement, not data by the gigabyte. If the number comes back flat, you keep the scorecard and the finding, and we've both learned something cheaply.
The pipeline
Four steps. The first one is the one everybody skips.
Generating text is close to free now. Any competent engineer can prompt a model into a thousand fake clinical notes over a weekend. What they can't do is give those notes the structure that makes a real dataset worth training on.
Understand
We map the relationships inside your sample before generating anything: which codes appear together, how conditions cluster, the order events happen in, and how a real record is actually written.This is where the improvement comes from.
Generate
Synthetic records built to those relationships, at the volume you need. Structure first, then scale, not the other way round.
Validate
Privacy and compliance checks on every record, before anything reaches you. Anything that sits too close to a real record doesn't ship.
Prove it worked
Train, benchmark, compare against your real-data baseline. The number is the deliverable. Everything above it explains why the number is bigger.
Who's building it
Three people who have shipped training data for a living.
Between us: data engines at Scale AI and Labelbox, and production healthcare AI on claims and medical coding today. We ran into this problem at work before we decided to go solve it.
Sayed Zahur Zaidi
Co-founder & CEO
Founding AI engineer at HealthBay AI, working on claims and medical coding automation. Previously Scale AI and Labelbox.
Dev Khera
Co-founder & CTO
ML infrastructure at Scaler AI Labs. Scalable ETL and data processing, multi-agent orchestration, production ML backends.
Garvit Sachdeva
Co-founder & Infrastructure
ML pipelines at Scaler AI Labs. Deployment pipelines, evaluation tooling, context-aware retrieval.