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Real projects. Real results.
Three case studies across our core service lines. Every number in this section is real. Every outcome is documented. Names are anonymised where clients have requested confidentiality.
What good annotation actually looks like
Any annotation vendor can claim high accuracy. What separates a reliable vendor from an unreliable one is whether the process that produces that accuracy is visible, documented, and repeatable. These case studies show ours in action — from the guidelines review before the first image is labelled, to the quality report attached to the final delivery.
Standard of Excellence
Multi-stage human verification for every dataset.
Dedicated project managers for real-time syncing.
Transparent error-rate reporting (JSON/CSV).
8,000 Legal Sentences Annotated. Zero Rework on First Submission.
THE CLIENT
A legal discovery platform automating the extraction of clauses from commercial lease agreements.
THE CHALLENGE
NER for legal text requires identifying nested entities (e.g. a 'Date' inside a 'Termination Clause'). Generic workers often missed the context, leading to inaccurate contract summaries.
WHAT WE DID
WHY THEY CHOSE US
Our team included paralegals and JD-background annotators who understood the semantic nuance of indemnity and liability clauses.
WHAT THE CLIENT SAID
"Finally, a vendor that understands the difference between 'effective date' and 'commencement date' without us having to write a 50-page manual."
THE RESULTS
100%
Entity Coverage
98.2%
F1-Score Precision
2 Weeks
Project Completion
10,000 Polygon Annotations Delivered 3 Days Ahead of Schedule
THE CLIENT
A Series B startup building autonomous weeding robots for commercial vineyards.
THE CHALLENGE
The client had a backlog of 10,000 high-resolution images of vine trunks, weeds, and irrigation lines. Previous offshore attempts resulted in 'lazy' polygons that overlapped boundaries, causing the model to misclassify vine trunks as weeds.
WHAT WE DID
Deployed a 12-person team specialised in sub-pixel precision. We implemented a 'shared edge' protocol where polygons for adjacent objects were locked to prevent overlaps, ensuring 100% semantic separation.
WHAT THE CLIENT SAID
"MedAxis didn't just label images; they fixed our edge-case logic. The speed was impressive, but the lack of rework was the real win."
THE RESULTS
99.4%
IW Accuracy Score
72 Hours
Early Delivery
0%
Rejected Batches
How a Free Pilot Became a 4-Month, 80,000-Image Contract
THE CLIENT
Global FMCG brand training an in-store shelf-monitoring system.
THE CHALLENGE
The client needed to identify 400+ different SKUs from low-quality CCTV footage. Lighting was inconsistent and products were often partially obscured.
WHAT WE DID
Started with a 200-image free pilot to prove our ability to handle occlusion. Once approved, we scaled to 5,000 images per week using a double-blind QA process.
WHAT THE CLIENT SAID
"The free pilot allowed us to test MedAxis without any procurement friction. The quality report they sent with the pilot was better than our previous vendor's final deliverables."
THE RESULTS
80k+
Total Images Labelled
400+
Unique SKU Classes
0.05%
Verified Error Rate
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MedAxis Data
Precision Data Annotation · Lagos, Nigeria
The partner for AI teams needing high-precision, managed data annotation.
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