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MedicalThis page
Medicine packaging, pharmacy shelves, and labels, captured in real pharmacies.
Medical data, built to spec
The health data captured from the pharmacies, homes, and markets for models.
Why medical models fail?
Medicines are packaged, labelled, and shelved differently in every market a model runs in.
Matches the pharmacies, packaging, and populations it saw in training.
Built for the markets the model serves.
What single-market health data misses
Rwazi captures each of these to your brief, in real pharmacies, homes, and stores, so your model meets them in training before it ever ships.
Real medical samples, on request.
A pack is captured for your task and markets, then delivered as files carrying capture metadata and a consistent naming convention, dropped into your cloud.
Medicine packaging, shot in real pharmacies: whole strips, cut strips, cartons, tubes, and sachets.
Request accessPharmacy shelves and counters, metro chemist through rural medical store.
Request accessHealth and wellness purchases, with what surrounds them at home.
Request accessMedicine labels across scripts for OCR and extraction.
Request accessWhat we capture, to spec.
Two ways to capture.
We work both ends of the spectrum. You pick the conditions your model needs.
Real-world capture
For models that run in real pharmacies and homes. Real light, real shelves, and real packs.
Controlled capture
For models that need precision. A clean background and fixed framing, shot to a tight brief.
Medical data, 190+ countries.
Most health data comes from a few mature markets, so models lose accuracy the moment they run somewhere new. Rwazi captures medical data across 190+ countries, in the pharmacies, homes, and stores where your model will run.
- 190+ countries
- 5+ label scripts
- pharmacy, home, and retail
- camera-original provenance
Where Rwazi medical data differs.
Built for health AI models.
Counterfeit and shelf verification
Published estimates of counterfeit medicine.
Medicine packaging was captured by market and annotated to the printed label.
Medical datasets by task.
Rwazi builds medical datasets for machine learning scoped to the task.
From spec to cloud
Run it as a one-off project or a recurring refresh, weekly or monthly.
How Rwazi medical data compares
Compare the same medicine captured in four different ways. Here is how that stacks up.
Scroll sideways to compare →
Rwazi captures medical data from the real world.
Our 5M+ contributor network captures in real pharmacies, homes, and stores across 190+ countries.
Every file earns its place
Each file is reviewed by people, checked against those criteria, and logged with where it came from: who captured it, in which location, and when. We report what passed before the dataset reaches you.
Tell us your scope or book a live demo with us
Questions teams ask before buying.
What are medical datasets for machine learning?+
Medical datasets for machine learning are sets of real-world health data used to train medical AI models. A medical AI dataset carries the file plus the metadata that makes it trainable. Rwazi captures both to your spec across 190+ countries, camera-original and tagged at the source.
What medical data can you collect?+
Medicine packaging, pharmacy shelves and counters, medicine labels across scripts, health and wellness purchases, and health activity. Capture runs in real pharmacies, retail, and homes, in real-world or controlled conditions, to the brief you set.
How does Rwazi collect medical data for AI?+
Real contributors capture brief in pharmacies, retail, and homes, under explicit consent: medicine packaging, pharmacy shelves and counters, medicine labels, health and wellness purchases, and health activity. Every file is camera-original, with location and timestamp attached. Capture stays in retail and home settings.
How do you build a medical dataset for machine learning?+
Start with the medicines, markets, pharmacy tiers, and conditions your model must handle, then capture real files for that brief and annotate them to the printed label. Rwazi builds the set to your pass-or-reject spec and delivers it ready to train.
Which markets do you cover?+
190+ countries, with India live. Capture is scoped to your brief, from metro chemists and pharmacy chains through rural medical stores. Label scripts are set by that brief, including English, Devanagari, Tamil, Telugu, and Bengali.
What annotation can you add?+
Annotation is an add-on layer, transcribed from the printed label: product name, generic name, strength, dosage form, manufacturer, pack type, and label language, plus batch and expiry location, legibility, and capture condition. Bounding boxes and OCR extraction are available on the same add-on layer.
How fast can you deliver?+
Smaller curated sets can land within days; larger or recurring builds run over weeks. Choose a one-off build or a weekly or monthly top-up.
How is it priced?+
We quote per project. The drivers are volume, SKU count, markets, pharmacy tiers, exclusive versus licensed, and any annotation add-ons. Send your brief and we will price it.
How do you handle consent and ownership?+
Every contributor is captured with explicit consent, sourced through Rwazi. You license the set or take it outright, and provenance travels with each file.
Where can I buy real-world medical datasets?+
Tell us the model and the health data it needs, and Rwazi scopes a bespoke medical dataset, captured to spec across 190+ countries and licensed or owned outright.