Your Complete AI Post
Training Operating System
LH2 brings together verified domain experts and purpose-built automation, delivering training data with the quality and scale enterprise AI demands
Platform Capabilities
The annotation infrastructure behind production-grade AI training data
Multi-modal Annotation
Structured annotation pipelines across text, image, audio, video, and code with modality-specific tooling and quality controls.
Configurable Annotation Workflows
Fully configurable task flows, review layers, and acceptance criteria to match your data specification exactly.
Built-in Quality Tools
Linters, compilers, and LLM validators embedded at every annotation step to catch errors before they propagate.
Expert Routing & Skill-Based Matching
Task assignment driven by domain expertise, skill taxonomy, and contributor performance history.
Real-Time Quality Dashboards
Granular visibility into task throughput and dataset quality along with project progress and any identified risks.
API Access for Pipeline Integration
RESTful API access to programmatically trigger jobs, pull annotations, and integrate directly into your training infrastructure.
LH2 Expert Network
500+
Active experts
38%
PhD-level or equiv.
20+
Domains covered
94.2
Avg. quality score
Senior Radiologist
15+ yrs clinical practice · MD, Radiology
Medical imaging
Clinical NLP
Principal Software Engineer
10+ yrs production eng. · Ex-FAANG
Code review
Code-gen eval
Manufacturing Engineer
15+ years of supply chain experience
Quality control
Process optimization
How experts are assessed
Credential Verification - Degrees, publications, and professional affiliations independently verified before onboarding
Domain Assessment - Structured test tasks conducted at start of every live project to establish a verified baseline quality score
Continuous Evaluation - Inter-annotator monitoring on every active project with quality enforced at the task level
Performance Routing - Every task assigned based on domain score, skill taxonomy, and historical accuracy
Experts by domain
Medical & Life Sciences
Software Development
Engineering
Finance
Quality Framework
Gold Labels
Pre-validated reference annotations embedded into live task queues to benchmark contributor accuracy. Every contributor is continuously measured against gold standard responses to ensure quality is enforced at the source.
Inter-Annotator Agreement
Every task is independently annotated by multiple contributors, with agreement scores calculated to surface inconsistency before it compounds. High-disagreement tasks are automatically flagged for expert review.
Random Sampling
Statistical sampling protocols applied across every batch to validate dataset quality without full manual review.
Fraud Detection
Behavioral and pattern-based signals monitored across every session to identify and remove bad-faith contributors in real time. Combines automated anomaly detection with expert review to protect dataset integrity.
Integration
Pipeline Integration
End to end integration across your existing ML stack from data ingestion to training-ready delivery, without rebuilding your pipeline. RESTful API and webhook triggers to let your team programmatically manage jobs, pull annotations, and automate handoffs.
Formats & Delivery
All datasets delivered in your required format - JSON, JSONL, CSV, COCO, CoNLL, With full lineage, compliance metadata, and version control included. Supports both on-demand delivery and scheduled batch exports.
