AI systems require continuous human feedback to achieve safety-critical performance. As autonomous vehicles and robotics scale, the bottleneck shifts from raw data collection to cost-efficient, high-quality annotation. At Kognic, we've developed guided workflows that dramatically accelerate how machines learn from human judgment—delivering the most annotated autonomy data for your budget.
In this post, we'll explore how our guided workflows—powered by the Auto-Label Co-Pilot and our new ActionAssist feature—enable you to integrate scalable human feedback into your data pipeline while maintaining the quality standards required for safe autonomy.
Guided workflows optimize the annotation process by routing human attention to where it matters most. Rather than manually labeling every frame from scratch, annotators work through a predetermined sequence that leverages automation wherever possible. This structured approach ensures consistency across team members and projects, accelerates throughput, and allows teams to handle larger datasets more effectively. It also simplifies onboarding—new team members can quickly contribute productively with minimal training, as the workflow itself guides them through best practices.
Our auto-label co-pilot is designed to maximize the value of your model predictions while maintaining the quality required for safety-critical systems. Here's how it works:
This approach has delivered remarkable results. In recent customer projects, our co-pilot reduced annotation time by up to 68% compared to manual methods. By using model predictions to guide Kognic's automation features, we achieve faster annotations without compromising the accuracy and auditability that safety cases demand. The result: you get more annotated data for your budget.
We're excited to introduce ActionAssist, our latest feature for boosting annotator productivity. ActionAssist provides intelligent suggestions for annotating different geometries more efficiently, helping users navigate best practices while enabling projects to scale their annotation workforce faster without lengthy training.
Here's how it works:
ActionAssist accelerates annotation speed through smart suggestions for object refinement and simplified navigation through complex sequences. The feature offers convenient keyboard shortcuts, making the workflow more efficient and user-friendly. These improvements help you deliver more high-quality annotated data, faster—maximizing your return on investment.
At Kognic, we're continuously evolving our platform to support the future of autonomy annotation. Our roadmap includes:
As annotation converges with curation—where human feedback shifts toward selecting high-value scenarios and validating model behavior—Kognic is building the platform to support this evolution. Stay tuned for developments that will further improve your annotation processes and help you integrate scalable, cost-efficient human feedback into your data pipeline. Sign up for our newsletter to keep up-to-date with our latest news.