WORK · SHIPPED SYSTEMS
What we have actually shipped.
Every system below runs, or ran, for a real customer. Each one links to the full write-up: what the problem was, what we built, and what it does now.
References on the record by name, at HeartSciences, Gigster, and the World Gold Council. No anonymous logo wall — every name here agreed to be here.
14 case studies
The AI Cardiologists Trust With Patients' Hearts
ML LABS built the AI-ECG platform HeartSciences runs today in hospitals across the US and UK, where a wrong number can hurt a patient. This is what earning that trust took.
READ THE CASE →How A Telecom Recovered 12x On Roaming
A top-10 global telecom was losing margin on international roaming. Engaged through Gigster, ML LABS built the routing platform that ran on roughly 1TB of traffic data a day and returned more than 12x in its first year.
READ THE CASE →An AI Result The Hospital Rejects Helps No One
An AI result the hospital's system rejects is a result no clinician ever reads. ML LABS built the two-way hospital messaging layer for HeartSciences: per-hospital setup, billing codes that flow, no duplicates.
READ THE CASE →Every Clinic Shared One Login Before It Reached Storage
The plan said use the standard cloud gateway. The review found that every clinic's identity collapsed into one shared login before the data reached storage, so ML LABS built the secure replacement instead.
READ THE CASE →Two Security Reviews Caught What One Would Miss
Two reviews ran side by side: an outside firm's penetration test and ML LABS' AI-assisted code review. Together they caught what either alone would have missed, including a login check that could fail with no one noticing.
READ THE CASE →One Set Of Rules Across Every Clinic Site
Multi-site automation breaks when one clinic's design is copied to all of them. ML LABS built the platform HeartSciences runs across its sites: shared rules in the center, room for each site's differences at the edge.
READ THE CASE →Finding Clinical Records In Plain Words
Clinical experts on HeartSciences' AI-ECG platform needed to find records by typing what they meant, not by stacking dozens of filters. ML LABS built the search behind it, mobile included.
READ THE CASE →Three Ways An ECG Reaches The Cloud
ECGs reach the cloud three ways: web uploads, clinic file shares, and EHR triggers. ML LABS built the path for HeartSciences that handles all three the same, so nothing is lost on the way.
READ THE CASE →Why No AI Result Gets Stuck Half-Finished
When a model call half-fails, the record ends up charged, incomplete, and stuck. ML LABS rebuilt HeartSciences' AI-ECG platform so every record reaches a definite end state you can check with one query.
READ THE CASE →Billing Automation That Matched The Experts First
A billing engine only earns its place by agreeing with the experts it replaces. ML LABS built the clinical billing automation HeartSciences runs, held to 98%+ agreement with those experts before a single claim moved.
READ THE CASE →Billing AI Contracts No Tool Can Price
Enterprise AI contracts price on rules that interact: per-unit rates, site minimums, annual caps, on-off toggles, and no ready-made tool gets the interactions right. ML LABS built the billing behind HeartSciences' AI-ECG platform, from first count to final invoice.
READ THE CASE →Reading Every ECG Machine's Data Correctly
Every ECG machine encodes its data differently, and none of the files look broken. ML LABS built the layer behind HeartSciences' AI-ECG platform that reads all of them correctly, whatever the machine.
READ THE CASE →Pricing A Property In Seconds, Within 10%
ML LABS built the valuation engine behind a PropTech platform: photos, satellite imagery, and location data turned into a price in seconds, within 10% of the closing price for 90% of properties. Knowing when the number was soft was the harder half.
READ THE CASE →Building The Hospital Connection Without Their Sandbox
Access to a hospital's test system is scarce, shared, and scheduled by someone else. ML LABS built HeartSciences a full simulator of it, so the work never waited on another team's calendar.
READ THE CASE →That is the work. Now the question is yours.
Every one of those started as one workflow somebody could not get a straight answer about. The scoping session is where that happens: a written plan, a budget range, and a go or a no-go.
Written plan · credited toward the next step · refunded if it gives you nothing you can use