Data Labeling & Annotation
High-fidelity training data, built for production AI systems
Solnix designs and manages annotation pipelines that produce the labeled datasets your models need to reach production quality, at enterprise scale, with rigorous quality controls.
Overview
The data foundation your AI models depend on
Most AI failures trace back to training data, insufficient coverage, inconsistent labeling, or missing edge cases. Solnix builds the annotation infrastructure, QA workflows, and delivery pipelines that turn raw data into model-ready datasets your teams can trust.
What's included
Image & Video Annotation
Bounding box, polygon segmentation, semantic segmentation, keypoint detection, and frame-level tracking for computer vision models across any domain.
Text & NLP Annotation
Named entity recognition, sentiment labeling, intent classification, coreference resolution, and instruction-response pairs for LLM training and fine-tuning.
Audio & Speech Annotation
Transcription, speaker diarization, emotion labeling, acoustic event classification, and phoneme-level annotation for speech AI systems.
Sensor & LiDAR Annotation
3D bounding box annotation, point cloud segmentation, depth map labeling, and multi-sensor fusion for autonomous vehicle and robotics datasets.
Quality Assurance & Agreement Scoring
Multi-pass QA workflows, inter-annotator agreement scoring, consensus labeling, and automated outlier detection, every dataset meets your threshold before delivery.
Annotation Pipeline Architecture
We design the full annotation infrastructure, tooling, workforce orchestration, quality scoring, and delivery pipelines, integrated with your model training stack.
Developer experience
Simple API. Powerful results.
Integrate in minutes with our SDK. Full TypeScript support, comprehensive documentation, and live examples for every feature.
How it works
From setup to production
Task Design
We analyze your model architecture, data distribution, and edge case requirements to design an annotation taxonomy and labeling guide.
Pilot & Calibration
A pilot batch establishes inter-annotator agreement baselines and calibrates quality thresholds before full-scale annotation begins.
Production Annotation
Annotation runs at scale with multi-pass QA, automated outlier flagging, and daily quality dashboards delivered to your team.
Delivery & Integration
Datasets are delivered in your preferred format (COCO, YOLO, TFRecord, JSONL) and validated against your training pipeline before handoff.
FAQ
Common questions
Get started
Start building your training dataset today
Talk to an expert and get a tailored implementation plan within 48 hours.