About Synthmark

Synthmark exists to make computer vision annotation feel like production software rather than a prototype. Too many ML teams lose time fighting tools that hide state, complicate review, or produce exports that need hand-fixing before they reach training.

We built Synthmark around three principles: visible state, clean review paths, and interface density that holds up over long annotation sessions.

Most annotation platforms are designed for sales demos, not for teams that spend 40 hours a week labeling, reviewing, and exporting datasets. They prioritize AI-assisted features over fundamentals like keyboard shortcuts, reliable undo history, and batch assignment controls.

We talked to CV teams and heard the same complaints everywhere: tools that crashed during long sessions, exports that required manual cleanup, reviewer feedback buried in email threads, and no clear ownership when work moved between labelers and QA leads.

Synthmark treats annotation as operational work that needs clear process, not a creative workflow that trades structure for flexibility.

• Predictable tools: Polygon, box, and keypoint tools share one editor with consistent shortcuts. No mode surprises mid-session.
• Built-in review: Assignment queues, label version comparison, and acceptance criteria stay visible inside the project — not in a separate spreadsheet.
• Clean exports: COCO, YOLO, VOC, and custom JSON outputs that plug into training pipelines without hand cleanup.
• Team coordination: Role-based access, project activity logs, and annotator performance views make handoffs explicit.

We work with computer vision teams at early-stage startups and applied research groups building production models. Common use cases include autonomous vehicle perception, satellite and aerial imagery, medical imaging, and retail inventory datasets.

Our typical user is a team of 2–20 people where at least one person owns annotation quality and needs the tooling to reflect that ownership.