https://tagi5.ai/ - AI-Powered Annotation & Labeling Platform
by•
Tagi5 is an AI-powered data annotation platform by EnFuse Solutions Ltd. that unifies image, document, and video labeling in one intelligent workspace. With AI-assisted annotation, quality review workflows, OCR, masking, and flexible exports, it helps organizations create high-quality training data faster and at scale. Built for AI teams, Tagi5 combines platform, people, and process to streamline data sourcing, annotation, quality assurance, and delivery in a single solution.
Replies
Spent a few minutes clicking through the demo and the unified workspace for image, doc, and video labeling in one place feels genuinely practical, especially the OCR and masking flow. Auto-annotation suggestions looked solid on the sample I tried.
Curious how the AI-assisted annotation actually works in practice, do I need to pre-train it on my own labels or does it come with decent out-of-the-box suggestions for common tasks like object detection or text extraction?
Really like how Tagi5 brings image, doc, and video annotation into one place instead of juggling separate tools. One thing that would save our team a lot of time is a built-in consensus scoring feature that automatically flags labels where annotators disagree, so QA can focus on edge cases rather than spot-checking everything manually.
The unified workspace for image, doc, and video labeling actually feels cohesive rather than bolted together. OCR and masking worked smoothly on my test batch, and the AI-assisted suggestions cut my annotation time noticeably.
How does the AI-assisted annotation actually handle edge cases like occluded objects in video, and is there a way to flag low-confidence labels for human review before they hit the training pipeline?
Finally got to try this for a small labeling batch and the AI-assisted bounding boxes saved me a ton of clicks. Liked that document and image annotation live in the same workspace too, keeps context switching minimal.
How does the AI-assisted annotation actually handle edge cases like heavily occluded objects or overlapping labels, and is there a way to step in manually when it gets those wrong?