Billion X.

Key Skills

Software

Other

OpenCV AI Kit (OAK)

Data Annotation Tech

CrowdSource

Axiom AI

Top Subject Matter

Regulatory-grade evidence synthesis and policy analytics

Geospatial environmental hazard detection and regulatory-grade evidence synthesis

Manufacturing evidence extraction

Top Data Types

Document

Image

Text

Top Task Types

Function Calling

Object Detection

Segmentation

Point/Key Point

Classification

Polygon

Freelancer Overview

Technical Co-Founder, EviSyna - Evidence Synthesis AI (Ideation Stage). Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other, OpenCV AI Kit (OAK), and MMOCR. Education includes Master of Science, University of Lagos (2026) and Bachelor of Science, University of Lagos (2024). AI-training focus includes data types such as Document, Geospatial, and Tiled Imagery and labeling workflows including Function Calling, Object Detection, and Segmentation.

Labeling Experience

Deep Learning Research Assistant

OpenCV AI Kit (OAK)Object Detection

Built computer vision models to detect and localize environmental hazards from satellite imagery for regulatory-grade evidence analysis. Implemented scalable deep learning architectures to support real-time evidence synthesis and structured downstream analysis. Focused on producing detection outputs that can be used as evidence inputs for audit-ready reporting workflows. • Trained/optimized YOLO for hazard detection • Trained/optimized U-Net for segmentation in geospatial pipelines • Supported evidence synthesis from detection outputs • Contributed to scalable real-time regulatory-grade analysis

2025 - Present

Technical Co-Founder, EviSyna - Evidence Synthesis AI (Ideation Stage)

OtherDocumentFunction Calling

Led development of an evidence synthesis AI co-pilot that performs structured extraction from research/policy inputs for regulatory-grade outputs. Utilized PICO parsing and multi-database search expansion to transform unstructured documents into structured, auditable evidence. Incorporated human-in-the-loop validation to ensure label/field correctness before generating final results. • Performed PICO parsing and structured extraction from text • Expanded search across multiple databases • Maintained audit logs for traceability • Validated extracted evidence via human-in-the-loop review

2025 - Present

Data Scientist Intern, CEAT Ltd.

Segmentation

Developed computer vision solutions for automated evidence extraction and visual inspection in manufacturing settings. Implemented OCR pipelines to convert unstructured visual text into structured fields for regulatory reporting. Worked on deep learning-based anomaly detection and related causal inference components to support evidence-oriented analytics. • Used YOLO and custom RCNN for evidence extraction • Automated OCR using MMOCR for structured extraction • Supported visual inspection and reporting workflows • Built anomaly detection models tied to evidence generation

2023 - 2024

Education

Work History