In the competitive world of autonomy development, annotation costs can quickly spiral. But what if you could cut your budget in half while maintaining (or even improving) the quality of your ground truth data? At Kognic, we've helped global leaders in autonomous vehicles and robotics achieve exactly that. Here are five proven strategies to maximize your annotation ROI.
Traditional auto-labeling approaches often disappoint. Teams expect cost savings as their models improve, but the reality is different: fixing poor auto-labels frequently takes longer than annotating from scratch.
Kognic takes a fundamentally different approach. Our Co-pilot doesn't just provide a starting point. It uses auto-label information to guide annotators through an optimized workflow with advanced automation. Even when auto-labels aren't perfect, this guided approach delivers significantly faster results.
Real-World Impact: Zenseact achieved 48% cost savings by integrating their auto-label model into Kognic's platform.
Quality assurance shouldn't be an afterthought. It should be built into every step of your annotation pipeline. Kognic's QA-first approach combines automated validation with structured review workflows to identify and resolve issues before they cascade through your dataset.
Our platform includes:
By catching quality issues early, you eliminate the costly rework that often doubles annotation expenses. Our customers consistently report that Kognic's QA infrastructure prevents errors that would otherwise require entire batches to be redone.
Here's where Kognic's approach gets truly unique: we pass savings from high-quality auto-labels directly back to you. Our performance-based pricing model means you pay less when your auto-labels are accurate and require minimal correction.
This creates a virtuous cycle: as your models improve, your annotation costs decrease automatically. You're not just getting faster annotations: you're getting a pricing structure that rewards your investment in better perception models.
How It Works: When auto-labels require fewer corrections, annotators work more efficiently. We track this efficiency gain and reflect it in your pricing, ensuring you benefit directly from your model improvements.
Rework is the silent budget killer in annotation projects. When annotators must redo work due to unclear guidelines, inconsistent standards, or workflow inefficiencies, costs multiply quickly.
Kognic eliminates rework through:
Our customers have achieved up to 9.5x productivity improvements by leveraging these intelligent workflows. One customer reduced their required workforce from 95 to 10 users while exceeding quality benchmarks. All without changing their annotation tools, just by optimizing how they were used.
Generic annotation tools force annotators to work harder, not smarter. Kognic's platform is purpose-built for autonomy data, with every feature designed to maximize throughput without compromising accuracy.
State-of-the-art visualization, controls, and shortcuts enable annotators to focus on high-value work. Smart features like snap-to-object, auto-follow, and bulk edits turn complex tasks into simple operations.
At Kognic, our customer promise is simple: Get the most annotated autonomy data for your budget. We're not just another annotation vendor. We're the price leader in autonomy data annotation, delivering more annotated data per dollar than anyone else in the market.
Global autonomy leaders, including OEMs and robo-truck developers, have trusted Kognic to deliver fleet-scale, high-quality datasets over multiple consecutive years. This track record showcases our scalability, reliability, and operational stability.
The autonomy industry is evolving rapidly, and annotation approaches must evolve with it. As we move toward smarter human-in-the-loop systems and curation-first workflows, having a platform that delivers maximum productivity today while preparing you for tomorrow's challenges is essential.