High-quality, human-verified data annotation and labelling services for businesses building computer vision, NLP, and multimodal AI models. We label at scale — without sacrificing accuracy.
Annotation accuracy guaranteed across all project types
Data points annotated across image, text, video, and audio
Turnaround on pilot batches for new clients
Privacy Act 1988 & APPs compliant — on-shore processing available
Bounding boxes, polygons, semantic segmentation, keypoint labelling, and instance segmentation for computer vision models used in autonomous vehicles, retail, agriculture, and healthcare imaging.
Named entity recognition, sentiment labelling, intent classification, relation extraction, and question-answer pair creation for LLM training and NLP pipelines.
Transcription, speaker diarisation, emotion tagging, and phoneme labelling for speech recognition, voice assistant, and conversational AI models. We support Australian English accents and multilingual datasets.
Radiology scans, pathology slides, and clinical document annotation for diagnostic AI — built to meet TGA software as a medical device (SaMD) requirements and My Health Record data standards.
Satellite and drone imagery labelling for crop detection, land classification, and precision agriculture — supporting CSIRO, state agriculture departments, and agritech startups across Australia's farming regions.
Object detection datasets for autonomous vehicles, warehouse robotics, and fleet management systems — supporting teams building for Australian road conditions, port operations, and last-mile delivery networks.
Secure, on-shore annotation for federal and state government AI projects — compliant with the ASD Essential Eight, ISM, and PROTECTED data handling requirements under the Australian Government Security Framework.
Data annotation is the process of labelling raw data — images, text, video, or audio — so that machine learning models can learn patterns from it. The quality of annotations directly determines how accurate and reliable the resulting AI model will be.
We deliver in any format your ML pipeline requires — COCO JSON, YOLO TXT, Pascal VOC XML, CSV, JSONL, and more. We also integrate directly with annotation platforms such as Label Studio, Roboflow, and Scale AI.
We use a three-stage QA pipeline: automated validation scripts catch structural errors, inter-annotator agreement (IAA) checks measure consistency, and senior human reviewers audit a statistically significant sample of every batch. We target 98%+ accuracy as a contractual baseline.
Yes. All data annotation projects are handled in compliance with the Privacy Act 1988 and the 13 Australian Privacy Principles (APPs). For regulated sectors we offer fully on-shore processing, signed data processing agreements, and can operate under APRA CPS 234 and ASD Essential Eight frameworks.
We can begin a pilot batch within 48 hours of receiving your dataset and annotation guidelines. Full-scale projects are typically ramped up within one week of scoping sign-off.
Tell us your dataset size, annotation type, and timeline — we'll send a quote within 24 hours.