AVAI

AVAI Data Agent

Complete MLOps for Autonomous Systems

One platform from raw sensor data to production model. Calibrate sensors, ingest and curate petabytes of drive data, annotate in 2D and 3D, train on GPU clusters, and deploy to edge — all in a single pipeline.

Calibrate
Ingest
Annotate
Review
Train & Deploy
5
Pipeline Stages
2D/3D
Annotation Modalities
PB-Scale
Data Capacity
End-to-End
MLOps Coverage

The Pipeline

Five Stages, One Platform

Each stage of the MLOps lifecycle is purpose-built for autonomous driving data. No more stitching together disconnected tools.

Stage 01

Calibrate

Sensor Alignment & Validation

Multi-sensor extrinsic and intrinsic calibration for camera, LiDAR, radar, and IMU. Automated target detection, reprojection error analysis, and calibration health monitoring across your entire fleet.

1
Camera intrinsic & extrinsic calibration
2
LiDAR-to-camera cross-calibration
3
Radar alignment verification
4
IMU/GNSS lever-arm measurement
5
Fleet-wide calibration tracking
6
Automated re-calibration alerts
Stage 02

Ingest

Data Pipeline & Curation

High-throughput data ingestion from vehicle to cloud. Automated data validation, deduplication, scene classification, and intelligent curation to surface the most valuable training samples from petabytes of raw drive data.

1
Multi-format sensor data ingestion
2
Automated quality validation checks
3
Scene classification & tagging
4
Intelligent data curation & sampling
5
Metadata extraction & indexing
6
Petabyte-scale cloud storage
Stage 03

Annotate

2D & 3D Labeling at Scale

Production-grade annotation for autonomous driving. 2D bounding boxes, 3D cuboids, semantic/instance segmentation, lane markings, and temporal tracking across camera and LiDAR data — with built-in QA workflows.

1
2D/3D bounding boxes & cuboids
2
Semantic & instance segmentation
3
Polyline lane marking annotation
4
Multi-frame temporal tracking
5
Cross-sensor fusion labeling
6
Multi-tier QA & review pipeline
Stage 04

Review

Quality Assurance & Validation

Rigorous multi-tier review pipelines ensure every annotation meets production quality standards before it enters your training data.

1
Multi-tier QA & review pipeline
2
Inter-annotator agreement scoring
3
Automated consistency checks
4
Edge-case flagging & escalation
5
Audit trail & annotation versioning
6
Consensus & adjudication workflows
Stage 05

Train & Deploy

Model Training, Optimization & Edge Deployment

Managed training infrastructure with experiment tracking, hyperparameter optimization, and distributed training across GPU clusters. Version your datasets, compare runs, and track model lineage from data to deployment.

1
Distributed GPU training orchestration
2
Experiment tracking & comparison
3
Hyperparameter optimization
4
Model optimization & quantization
5
TensorRT & ONNX export
6
OTA deployment to edge fleets
7
A/B testing & canary releases
8
Inference monitoring & drift detection
Train & Deploy

Platform Capabilities

Built for Autonomy

Purpose-built tools for the unique challenges of autonomous vehicle data — multi-modal sensors, massive scale, and safety-critical quality requirements.

Sensor Fusion Labeling

Sensor Fusion Labeling

Annotate synchronized camera, LiDAR, and radar data in a unified 3D workspace. Labels propagate across sensor modalities automatically.

Active Learning

Active Learning

AI-assisted annotation with model-in-the-loop. Pre-label with your existing models, then human annotators correct and refine — accelerating throughput by 3-5x.

Version Control & Lineage

Version Control & Lineage

Full traceability from raw drive to production model. Track every dataset version, annotation iteration, training run, and deployed artifact in one system.

Quality Assurance

Quality Assurance

Multi-tier review workflows with consensus scoring, inter-annotator agreement metrics, and automated validation rules that catch errors before they reach training.

Scalable Infrastructure

Scalable Infrastructure

Cloud-native architecture that scales from prototype datasets to production-scale petabyte pipelines. Pay for what you use with no infrastructure management overhead.

API & SDK Access

API & SDK Access

Programmatic access to every stage of the pipeline. Integrate with your existing CI/CD, trigger training from annotation completion, and automate model deployment workflows.

Compatibility

Export in Any Format

Native support for all major autonomous driving dataset formats. Custom export pipelines for proprietary formats.

KITTI

Benchmark standard

COCO

Detection & segmentation

nuScenes

Multi-modal 3D

Waymo Open

Large-scale AV

Argoverse

HD maps + tracking

CVAT XML

Open annotation

Pascal VOC

Classic detection

Custom

Your format

Get Started with AVAI Data Agent

Stop stitching together disconnected tools. AVAI Data Agent is the complete MLOps platform purpose-built for autonomous systems.