DATA COLLECTION & ANNOTATION

Training data you can trust.

We design consent-aware pipelines to source, label, and audit text, speech, image, and video datasets. Gold sets, IAA, and multi-pass QC make labels audit-ready.

Consent & privacy
Global vendor network
Gold-set audits
IAA metrics
Multi-pass QC
Audit trail
In-tenant options
GLOBAL SOURCE

Raw Data Ingest

Multi-Modal Stream

IMG
AUD
TXT
VID
LABELING CORE
CONSENT
// Annotation Specs
task = {
type: "bounding_box",
classes: ["car", "person"],
consensus: "gold_set",
min_iaa: "0.95"
}
IAA Score
96%
Audit Trail
Human-in-the-Loop

Training Ready

Bias-Checked Datasets

BIAS CHECKPASS
PII REDACT100%
JSONL
Parquet
COCO/YOLO

Why AI teams switch.

The difference between raw data and training-ready data.

The Old Way (Crowds)

  • ✕Inconsistent labeling rules across batches.
  • ✕Opaque workforce sourcing & ethics.
  • ✕No guarantee on inter-annotator agreement.
Saytica

The Saytica Way

  • Consent-first sourcingManaged pipelines, audited weekly.
  • Balanced coverageManaged pipelines, audited weekly.
  • Quality at scaleManaged pipelines, audited weekly.
  • TraceabilityManaged pipelines, audited weekly.
  • Flexible opsManaged pipelines, audited weekly.

What we collect.

1
Text (prompts, chats, intents, labels)
2
Speech (read & spontaneous, multi-speaker)
3
Images (people, objects, scenes)
4
Documents (OCR, forms, receipts)
5
Video (segments, actions, events)

Deliverables.

TYPE 1Labeled datasets (JSON/CSV/TSV/Parquet)
TYPE 2Annotated images (COCO, YOLO, Pascal VOC)
TYPE 3OCR outputs (PAGE/ALTO/hOCR)
TYPE 4Transcripts & diarization (RTTM, ELAN, TextGrid)
TYPE 5Evaluation sets & scorecards (XSTS-style)

Tasks We Support

Transcription & segmentationSpeaker diarization & role labelingIntent & slot tagging, NER, sentimentOCR box/line/word markup, table structure extractionKey-value extraction, form parsingBounding boxes / segmentation / keypoints / poseSafety & policy labeling, red-teaming promptsPairwise ranking, preference annotations, ranking datasetsMultilingual evaluation sets & adversarial (red-team) suites

Built-in governance.

Quality controls that scale with your data volume.

01

Gold sets & audits

Configurable sample %; automated scoring and human review for consistent quality control.

02

IAA

Cohen's κ, Krippendorff's α on overlapping batches to measure inter-annotator agreement.

03

Error taxonomy

Structured error classes (span, label, omission, policy) for systematic quality tracking.

04

Rework rules

Automatic vendor feedback with visual fixes and re-submission queue for continuous improvement.

05

Governance docs

Data cards, sample manifests, consent records, and changelogs delivered with every dataset.

Frequently Asked Questions

Ready to start a pilot?

Send your schema or a short brief and we'll return a sample plan, pilot price, and timeline.