The Future of
Humans
in AI
Data Annotation Has Fundamentally Changed
You can't patch label quality with product features anymore.
Model performance is capped by demo quality—and talent is now the real bottleneck.
Why Brix:
Our End-to-End Solution
Multi-Stage QA
Consensus labeling + AI-assisted reviews
Project-specific KPIs
Domain Expert Labelers
STEM, legal, medical, financial, etc.
Vetting via tests & credentials
Flexible & Fast Experimentation
Pilot in days, scale in hours
API & UI integrations
Real-Time Insights with WorkViz
Minute-by-minute progress dashboards
Automated QC alerts & analytics
Labeler Traceability & Direct Hire
Per-annotator ID, performance metrics
Option to directly recruit top performers
Brix 500M Talent
Language Competency
Native English (US/UK)
support
plus expertise in over 100 languages.
Coding & STEM Expertise
Brix Domain Expert Pool
Brix Domain Expert Pool
Experts & Linguists
Hand-picked, domain-specific talent
Managed Pipelines
End-to-end dataset generation
Ethical by Design
Privacy, fairness & transparency
Efficiency Gains
Up to 70% faster time-to-model
RLHF & Fine-Tuning
Full human-feedback loops
Case Study
Openaudio
TTS Labeling
Challenge
Robotic intonation & limited non-English support
Solution
200K utterances across 30+ languages by native experts
WorkViz tracked prosody, pause placement & error drift
Phonetics specialists ensured phoneme accuracy
Result
85% reduction in phoneme-error rate
MOS score from 3.2 → 4.7
Multilingual launch in under 1 month
Case Study
Luma AI
Video Model Labeling
Challenge
1M+ frames of fine-grained AR segmentation
Solution
50K frames/day, 20+ object classes per frame
WorkViz traceability & drift detection
Red-team edge-case stress tests (low light, occlusion
Result
40%± IoU on segmentation tasks
30% faster inference
25% increase in production uptime
Next Steps
Pilot Program
1-2 week PoC (5K annotations)
Quality Review
Sample label comparison
Scale & Integrate
API keys, SLAs & rollout plan
Contact: data@joinbrix.com