AI labs need domain experts, not anonymous gig workers. Think.Loop deploys university-verified STEM students for the specialized data work that frontier models demand.
Frontier models are starving for high-quality, domain-specific human data. Synthetic data alone leads to model collapse. Crowdsourced gig workers cannot close the expertise gap.
Think.Loop partners directly with universities to authenticate and deploy STEM and graduate students for complex AI data work. Every annotator is identity-verified, domain-matched, and produces a full provenance trail.
Raw or synthetic data from AI labs and enterprise clients
University-verified domain specialists with 3-tier quality assurance
Auditable, credentialed, compliance-ready data output
Every annotator verified through university enrollment systems. No anonymous gig workers.
Chemistry tasks go to chemistry students. Legal review to law students. Precision by design.
Complete audit trail from annotator credentials to final label. Built for regulatory compliance.
Cloud-native microservices architecture handling all data modalities at enterprise scale.
Rule-based checks, statistical outlier detection, ML-based quality prediction
Blind review, structured checklists, consensus mechanisms for disagreements
Senior domain experts, inter-rater agreement via Cohen's Kappa
RLHF, instruction tuning, red-teaming, and evaluation benchmarks from annotators who understand the models they are training.
Clinical data annotation, molecular structure validation, and medical DICOM labeling by pre-med and life science students.
Contract analysis, regulatory document classification, and legal NER from law students who read the fine print for a living.
Financial document extraction, risk assessment labeling, and market data validation by finance and economics students.
We handle the complexity of sourcing, verifying, and managing expert annotators so your team can focus on building.
Define data modality, domain requirements, quality thresholds, and volume. We design a custom annotation protocol for your use case.
Our system identifies university-verified specialists in your domain. Chemistry data gets chemistry students. Legal gets law students.
Automated checks, blind peer review, and senior expert validation. Cohen's Kappa inter-rater agreement on every batch.
Production-ready data with full audit trail. Every label traceable to a verified annotator credential. API or bulk delivery.
Think.Loop is built for organizations where data governance, regulatory compliance, and infrastructure interoperability are baseline requirements.
Auditable controls for security, availability, and confidentiality
Full data residency controls, right to erasure, and consent management
BAA support for healthcare and clinical data annotation projects
Programmatic access to submit, track, and retrieve annotation projects
AWS, Azure, and GCP connectors for seamless infrastructure integration
TensorFlow, PyTorch, and scikit-learn export formats out of the box
Request a pilot. Run a blind quality comparison. Let the data decide.
Request a Pilot