AI Data Foundations for Frontier Models.
Multilingual, multimodal, human-verified. Eighteen years of building the data corpora behind the AI systems people actually trust.
18 years improving Data. So you don't have to.
Data Collection.
Sourced, consented, deduplicated.
Multilingual Speech.
Native speakers across 700+ languages and dialects.
Transcription.
Audio to ground-truth text at lab-grade accuracy.
Labeling.
Text, image, video, geospatial, sensor data.
RLHF.
Preference data calibrated by domain experts.
Fine-tuning Datasets.
Task-specific corpora your model will retain.
Multimodal Annotation.
Linked audio + image + text + video.
Synthetic Data Validation.
Human calibration of generated data.
Localization.
Cultural and linguistic adaptation, not translation.
A process built for quality at frontier scale.
Specify.
Schema, edge cases, and acceptance criteria locked with your team.
Source.
Consented, vetted contributors matched by language and domain.
Annotate.
Tooling tuned to the task, with built-in adjudication.
Verify.
Blind double-pass, expert review, IAA scoring before delivery.
Every step is audit-logged. Every annotator is credentialed.
Quality you can measure.
Sigma's quality protocol is built on inter-annotator agreement (IAA) as a first-class metric. Every dataset runs through blind double-passes, with disagreements adjudicated by tier-three reviewers credentialed in the relevant domain. We publish IAA, coverage, and calibration scores with every delivery — so your evals start from ground truth, not from hope.
{
"dataset": "med-qa-en-v3",
"rows": 48_320,
"iaa": {
"cohen_kappa": 0.99,
"krippendorff": 0.991
},
"double_pass": "100%",
"expert_review": "tier-3",
"coverage": 0.99,
"audit_log": "verified"
}How teams use Sigma for data foundations.
Teaching AI to hear like humans: Transforming voice data into training signals
A global AI client needed to fix unnatural speech outputs, using high-fidelity human evaluation of complex voice data.
Read case studyUser intent labeling: Bridging the gap between AI logic and human goals
A leading AI team needs consistent human evaluation of user intent — across ambiguous, multilingual inputs at production scale.
Read case studyAI quality evaluation: Why automated benchmarking fails the competitive test
A frontier AI lab needs to benchmark model performance across tasks and versions — with human evaluation that reveals what automated tests miss.
Read case studyWhy AI feels like a foreigner: Multimodal localization and the human cultural layer
A leading AI team needed to evaluate model performance across text, audio, and video — and surface subtle failure modes at scale.
Read case studyBuilt by people who know the domain.
Secure Facilities in EU and UK. ISO 27001, SOC 2 Type II, GDPR. Zero Data Retention Policy.
- ISO 27001
- SOC 2 Type II
- GDPR
- EU AI Act Ready