Project context
Preligens (now Safran.ai) develops computer-vision algorithms to detect and classify military equipment in satellite imagery.
I redesigned the Tagging App, the core platform used to produce high-quality labeled datasets. It was used daily by 100+ annotators across CEDIA (internal) and Ingedata (external partner).
THE PROBLEM
Inefficient workflows undermining data quality
The existing tool had become a bottleneck as data volumes and AI ambitions grew.
Fragmented workflows: annotators switched between 5 tools (App, Trello, Google Earth, Excel, PDF).
No traceability: dataset versions were overwritten after quality checks.
Low efficiency: frequent crashes, no autosave, and manual QC created frustration and delays.
We spent more time switching tools than actually annotating. - Annotator, Ingedata
SOLUTION
Building a unified and intelligent tagging platform
I built a unified, role-based platform connecting annotators, QC leads, and data scientists in one workspace.
01. UX/UI Audit
All roles access the same secure interface with dedicated permissions.
→ Real-time batch tracking replaces Excel and Trello.
02. Streamlined annotation
A three-panel layout centralizes map, image settings, and classification.
→ Autosave, keyboard shortcuts, and dynamic ontology forms reduce repetitive steps.
03. Integrated quality control
QC happens directly in the same interface, with visual cues, validation tags, and traceable corrections.
Impact
USER FEEDBACK
What users said
OUTCOMES
Scaling high-quality dataset production for AI model training
The redesign transformed a fragmented toolchain into a centralized data-production platform, improving transparency, traceability, and motivation.
The new system became a critical enabler for faster model retraining and consistent dataset quality across Preligens’ AI pipeline.
