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Data Labeling

Human-in-the-loop annotation across every modality. Images, video, text, audio, and 3D point clouds with pixel-perfect accuracy.

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RLHF & Alignment

Preference data, safety evaluations, and instruction-following assessments from domain experts who understand model behavior.

Evaluation & Benchmarks

Custom evaluation frameworks with expert human judges. Go beyond automated metrics to measure real-world performance.

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Synthetic Data

AI-generated training data validated by human experts. Scale your datasets while maintaining quality and diversity.

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Data Curation

Dataset cleaning, deduplication, and quality control. Transform noisy data into clean, consistent training corpora.

API & Integrations

RESTful APIs, Python SDK, and native integrations with your ML pipeline. Programmatic access to all platform capabilities.

Data Labeling Platform

Annotation at any scale, for any modality

Our labeling platform combines intelligent tooling with expert human annotators to deliver high-quality labels across every data type your models need.

  • Image annotation: bounding boxes, polygons, semantic segmentation, keypoints
  • Video annotation: object tracking, temporal segmentation, action recognition
  • Text annotation: NER, sentiment, classification, relation extraction
  • Audio annotation: transcription, speaker diarization, emotion detection
  • 3D annotation: LiDAR point clouds, cuboids, lane markings
  • Multi-modal: cross-referencing labels across sensor modalities
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Annotation Types Supported
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Bounding Box
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Polygon
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Keypoint
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Segmentation
Active projects: 47 Labels today: 124,500
RLHF Workflow Configuration
Evaluation Dimensions
Helpfulness Safety Accuracy Conciseness
Annotator Requirements
PhD in relevant domain โ€ข 95%+ agreement score โ€ข 500+ completed tasks
Quality Controls
3x consensus โ€ข Rationale required โ€ข Inter-annotator agreement โ‰ฅ 0.85
RLHF & Alignment Data

Human feedback that makes AI systems trustworthy

Generate the preference data your alignment team needs. Our expert annotators provide nuanced, multi-dimensional feedback with detailed rationales.

  • Pairwise preference ranking with Likert-scale granularity
  • Multi-turn conversation evaluation and scoring
  • Red-teaming: adversarial prompt generation by security experts
  • Constitutional AI: principle-based evaluation frameworks
  • Reward model training data with calibrated confidence scores
  • Safety taxonomy labeling (toxicity, bias, misinformation)
Talk to an expert
Evaluation & Benchmarks

Measure what automated metrics can't

Standard benchmarks don't capture real-world performance. Our custom evaluation frameworks use expert human judges to assess the capabilities that matter most to your users.

  • Custom rubric design with your evaluation criteria
  • Blind A/B testing across model versions and competitors
  • Domain-expert judges (PhD researchers, licensed professionals)
  • Statistical rigor: confidence intervals, significance testing
  • Longitudinal tracking: monitor performance over time
  • Automated + human hybrid evaluation pipelines
Design your evaluation
Evaluation Results โ€” Model Comparison
Model A
92%
Model B
87%
Model C
79%
Baseline
64%
n=2,500 evaluations โ€ข 95% CI: ยฑ1.8% โ€ข p < 0.001
Synthetic Generation Config
domain: "medical_qa"
target_count: 100000
difficulty: ["easy", "medium", "hard"]
validation: "human_expert"
diversity_score: 0.85
quality_threshold: 0.95
Medical QA Pairs Expert Validated
Synthetic Data Generation

Scale your datasets without sacrificing quality

Our hybrid generation pipeline combines state-of-the-art AI models with rigorous human validation to produce training data that's diverse, accurate, and bias-aware.

  • Domain-specific generation: code, math, science, creative writing
  • Configurable difficulty levels and complexity distributions
  • Diversity-aware sampling to prevent mode collapse
  • Human expert validation with domain-specific quality criteria
  • Privacy-preserving synthetic alternatives to PII-containing data
  • Automated decontamination against benchmark datasets
Start generating
Data Curation & Quality

Clean data in, better models out

Garbage in, garbage out. Our curation pipeline identifies and resolves data quality issues before they corrupt your training runs โ€” saving compute, time, and money.

  • Near-duplicate detection using embedding similarity
  • Label consistency auditing with inter-annotator agreement metrics
  • Automated bias detection across demographic dimensions
  • Data quality scoring with actionable remediation reports
  • Dataset versioning with full lineage tracking
  • Compliance checking for licensing and attribution
Audit your data
Curation Pipeline Status
โœ… Deduplication Complete
โœ… Label Audit Complete
โณ Bias Detection Running (78%)
โฌš Quality Scoring Queued
โฌš Final Report Queued
Platform Capabilities

Built for enterprise AI teams

Security, scalability, and integrations designed for production ML workflows.

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Enterprise Security

SOC 2 Type II, HIPAA, GDPR compliant. End-to-end encryption, VPC peering, and dedicated infrastructure options.

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Scalable Infrastructure

Process millions of labels per day. Auto-scaling workforce allocation and intelligent task routing for optimal throughput.

Native Integrations

Connect with AWS S3, GCS, Azure Blob, Snowflake, Databricks, and your existing ML pipeline tools.

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Real-time Analytics

Live dashboards showing project progress, quality metrics, annotator performance, and cost tracking.

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AI-Assisted Labeling

Pre-labeling with your models or ours. Human annotators verify and correct, reducing time-to-delivery by 60%.

Dedicated Teams

Assign dedicated annotator cohorts trained on your specific guidelines. Consistent quality across long-running projects.

Ready to see the platform in action?

Schedule a personalized demo with our solutions team to explore how AnnotRift fits your data needs.

Request a demo