Recommendation API
Fraud Model v6
Data Pipeline
Inference Latency
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Our commitments
A few principles we don't bend on.
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Data layer
Versioned ingestion
Every dataset is hashed and traced to its source system.
Full data-to-prediction lineage
Rollback time on alert
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Rollout
Shadow → canary
New models run silently before serving real predictions.
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Gate
Reviewer sign-off
No override without a logged clinical reason.
Faster time to production
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Challenge
Models shipped like features, not clinical devices
Training a strong model took days. Getting it safely in front of a clinician took months — a manual export, a Slack thread, a spreadsheet of approvals, and no consistent lineage from data to prediction.
"We used to be afraid of our own release process. Now our data scientists ship a model and trust that nothing reaches a clinician until it's been checked, staged, and watched."
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VP of Engineering, Healthtech Startup
It’s a directory for discovering AI tools and a library of reusable prompts, both ranked by real community upvotes instead of paid placement.
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Compliance
Built for the sector's demands
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Full audit trail
Every version links to data, code, reviewer, timestamp.
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PHI stays in-boundary
Training and inference run inside the client's own VPC.
03
Drift monitoring
Predictions checked against baselines around the clock.
Shipping a model that has to be trusted?
We design deployment pipelines for teams where "it works on my machine" isn't good enough.
