What feed actually means here.
Data is where most AI projects quietly die. Labeled by strangers in marketplaces. Audited never. Scoped to whatever was easiest to find. Kairos labels in-house, by people who know your industry. Claims adjusters labeling claims data, lawyers labeling legal documents, recruiters labeling resumes.
Four ways we shape data.
Voice and Speech Data Collection
Multilingual audio collection at scale. Native speakers, controlled environments, validated transcripts.
Document Annotation
Domain-specific labeling. Legal, medical, financial, insurance. Annotators with the credentials to read them.
Custom Dataset Engineering
Bespoke datasets built to a spec. Synthetic generation paired with human curation where it matters.
Data Quality Audits
Independent audit of an existing dataset. Class imbalance, label drift, annotation agreement scores.
Scope → Label → Audit.
Scope
Define the labels, the edge cases, the success metrics. Pilot batch before scale.
Label
In-house team, double-passed on hard examples, calibrated against gold-standard sets weekly.
Audit
Inter-annotator agreement, error analysis, retraining loops. Quality is measured, not assumed.