Your models are only as good as your training data. Here's how we ensure auto-labels actually accelerate your annotation pipeline — not slow it down.
Most annotation platforms promise massive productivity gains from auto-labeling. Import your model predictions, let the system pre-annotate everything, and watch your costs drop.
In practice, it rarely works that way.
Here's what actually happens: annotators spend more time micro-adjusting mediocre auto-labels than they would creating annotations from scratch. The productivity gain evaporates. Sometimes it becomes a productivity loss.
We know this because we've measured it across dozens of autonomy programs. And it's why we built our auto-label workflow differently.
Kognic doesn't treat auto-labels as finished annotations that humans review. Instead, we use them as intelligent prompts that guide our automation features.
The difference matters.
Traditional approach: Import predictions → annotators fix errors → hope for the best
Kognic's approach: Import predictions → identify highest-impact frames → use auto-labels to guide object creation → apply one-click fixes → interpolate across sequences
This means even an imperfect model generates real value. Your auto-labels don't need to be perfect — they need to point our system in the right direction.
Here's what we've seen so far: up to 68% reduction in annotation time compared to manual methods, without compromising quality.
That's not a theoretical maximum. It's measured across real customer projects with production-quality requirements.
How we deliver it:
Smart frame selection — We identify where your auto-labels add the most value and where human annotation from scratch is faster. No wasted effort correcting labels that should be redone entirely.
Guided automation — Auto-labels trigger our platform's automation features (interpolation, auto-adjustments, geometry tools) rather than sitting as static predictions for humans to nudge.
Quality-first routing — Complex scenes, edge cases, and novel objects get full human attention. Straightforward frames get accelerated through the co-pilot. The routing is dynamic, not fixed.
Measurable outcomes — Every project gets annotation time tracking. You see exactly what the co-pilot saves versus manual baseline. No black boxes.
We're transparent about this: auto-labels aren't always the fastest path.
Auto-labels accelerate annotation when:
Manual annotation is faster when:
Our Solutions Engineering team evaluates this before your project starts. If auto-labels won't help, we'll tell you — and route your data through the most efficient manual pipeline instead.
The real question isn't whether auto-labels save time. It's whether they save time at the quality level your perception system requires.
Here's how the math works:
Without co-pilot: 100 frames × full manual annotation time = baseline cost
With co-pilot: Same 100 frames, but 60-70% of annotation effort is automated through guided workflows. Human effort concentrates on the 30-40% that requires judgment.
The result: you get the same quality ground truth, faster, at lower cost per annotation. And because humans focus on the hard parts, edge case coverage actually improves.
Autonomous driving development doesn't wait. Your annotation pipeline needs to keep up with your data engine — not become the bottleneck.
The auto-label co-pilot is one piece of how we maintain velocity:
API-first integration — Import predictions in OpenLabel format via our API. We support 3D cuboids, 3D lanes, 3D/2D points, bounding boxes, polygons, and semantic segmentation. Our Solutions Engineering team handles custom infrastructure and format mapping.
Sparse annotation through interpolation — You don't need predictions on every frame. Provide key frames and our platform interpolates between them automatically — in pixel coordinates for 2D, and in frame-local or world coordinates for 3D. This alone can cut annotation effort dramatically for sequence data.
Continuous improvement — As your models improve, the co-pilot gets more effective. Better predictions mean more automation, which means lower cost per annotation over time. Lock confident predictions with kognic_locked_geometries to skip human review on objects you trust.
Scale without compromise — The same quality standards apply whether you're annotating 1,000 frames or 1,000,000. The co-pilot scales the efficiency, not the error rate.
Getting your models into the co-pilot pipeline is straightforward:
1. Upload your scenes — Push sensor data (camera, LiDAR, radar) via our API. Multi-sensor sequences with calibration data work out of the box.
2. Submit pre-annotations Send your model predictions in OpenLabel format against the uploaded scenes. Supported geometries include 3D cuboids, 3D lanes and lines, 2D bounding boxes, polygons, points, and semantic segmentation masks.
3. Create annotated inputs Our platform evaluates your predictions, routes them through the co-pilot workflow, and delivers production-quality annotations back via API.
The entire pipeline is programmatic.
We don't ask you to take our word for it. Every new customer gets a pilot project where we measure:
The data speaks for itself.
Ready to see what your models can unlock? Talk to our team
Kognic has delivered 100+ million annotations across 70+ autonomy programs. Our platform combines human expertise with intelligent automation to help machines understand the world — faster.