Agentic Labeling
We build custom auto-labeling agents trained from scratch on your data. Within one month, 80% of your annotations are automated. Human experts shift to failure mining and corner cases — the safety-critical long tail where precision matters most and generic models fall short.
Every customer follows the same proven ramp — from 100% human labeling to 90% AI agent-automated in three months.
Weeks 1–4
Our managed workforce of 15,000+ human experts labels ~100,000 objects on your sensor data — LiDAR cuboids, camera bounding boxes, polylines, segmentation masks. This is the seed data for your custom auto-labeling agent. Quality: 99%+ first-pass yield.
Week 6
We train a custom auto-labeling agent from scratch on your labeled data and deploy it within two weeks. Not a generic foundation model — a lightweight, task-specific agent optimized for your exact taxonomy. 80% of new labels are now auto-generated. Human experts verify the 20% where the agent has low confidence.
Month 3+
Every correction feeds back into the next agent version. Accuracy climbs. Cost per label drops. Your human experts become corner-case specialists — finding the scenarios where autonomous systems fail. This is the data that makes the difference between 99% and 99.99%.
| Generic Models (YOLO, SAM) | Avala Custom Auto-Labeling Agents | |
|---|---|---|
Accuracy | ~50% on specialized tasks | 80–90%+ on your specific task |
Inference Cost | $3–5K/month per project | Lightweight — many run on-device |
Training Data | Generic — not your taxonomy | Trained from scratch on your data |
Iteration Speed | Corrections slower than labeling from scratch | Iterate weekly with new corner cases |
We maintain a catalog of Physical AI models — from YOLO to NVIDIA Alpamayo — and build custom auto-labeling agents on top of the best foundations for your use case.
Custom auto-labeling agents trained from scratch on your data. 80-95% accuracy. Many run on-device with zero inference cost. The backbone of the 80/20 pipeline.
Real-time 2D detection from Ultralytics. We fine-tune YOLO on your taxonomy for fast bounding box auto-labeling.
Meta Segment Anything Model. Interactive segmentation masks for polygon and semantic pre-annotation.
10B-param VLA model. End-to-end trajectory prediction with reasoning traces from multi-camera input.
Object-centric segmentation variant. Pre-annotation for cuboid and 3D bounding box tasks.
Human-focused variant for pose estimation, keypoint detection, and skeleton auto-labeling.
World foundation models. Synthetic data generation, scene augmentation, and neural reconstruction.
Avala Inference Cloud
Run auto-labeling agents at production scale without managing GPUs, cold starts, or infrastructure. The managed inference layer for Physical AI.
No GPU management, no cold starts, no infrastructure overhead.
SageMaker and dedicated inference for heavy workloads.
Browser-side WASM execution for lightweight models — zero inference cost.
Auto-scaling for production volumes up to 1M+ frames per week.
Globally distributed, optimized for latency and throughput.
The closed-loop system behind the best autonomous driving programs. AI agents auto-label → human experts verify → retrain → repeat. Every iteration makes the agent smarter and your data cheaper.
Start with a pilot. See your custom auto-labeling agent hit 80% accuracy on your data in the first month — or we will tell you honestly if it is not the right fit.
