Fast Data Labeling
We turn raw text, images, tables, time-series, and audio into training-ready labeled datasets in hours, not months. Foundation-model pre-labeling, active learning, and expert human verification — assembled into a single rapid pipeline.
From six months of manual labeling to a labeled dataset by Friday.
Modern training jobs starve while teams wait weeks for vendors to label what a foundation model can pre-label in seconds. Here is the gap on a 100,000-item job:
Each card below shows the live labeling pattern for that modality. Hover a card to focus its animation.
Entities, sentiment, intent, topics, contract clauses, medical codes, support categories — across 20+ languages.
Bounding boxes, segmentation masks, keypoints, classification, OCR — pre-drawn by detectors and verified by humans.
Row-level classification, anomaly flags, fraud labels, eligibility scoring, schema-aware reasoning across millions of rows.
Segment events, detect anomalies, classify activity windows in IoT, finance, biosignals, and machine telemetry.
Speaker turns, intent, transcription review, audio events — labeled and time-aligned for downstream training.
Models predict. Uncertainty is scored. Humans only see what they need to see. Every reviewed item flows back into the model — making the next batch easier.
We do not run a one-off labeling project. We hand you a re-runnable pipeline that turns any new batch of raw data into labeled training data on demand.
We codify your taxonomy, edge cases, and quality bar into a machine-readable spec. Disagreement rules, gold-standard examples, and confidence thresholds are decided up-front so the pipeline never drifts.
Stream from S3, GCS, Azure, BigQuery, Postgres, Kafka, or local mounts. Sensitive fields can be redacted, hashed, or tokenized before any model sees them — GDPR by construction.
Zero-shot, few-shot, or fine-tuned models generate first-pass labels at >1,000 items per second. Multiple models vote in parallel, producing both a label and a calibrated confidence score for every item.
Active learning ranks every prediction by model disagreement and uncertainty. Confident cases pass through; hard, ambiguous, or rare items bubble to a focused human review queue — typically less than 5% of the data.
Domain experts review only the slice that matters in a fast, focused UI: keyboard-first, hot-key driven, with reference examples and disagreement context. Reviewer agreement is measured continuously.
Multi-model consensus, gold-standard spot checks, and reviewer agreement merge into a single quality score. Final labels export as JSON, COCO, CSV, Parquet, or pushed back into your training pipeline.
Numbers below assume a 100,000-item labeling project with a moderately complex taxonomy. Your mileage will vary; we will model the actual numbers for your data before any work starts.
Pre-drawn bounding boxes, segmentation masks, keypoints, and classification — your annotators only touch the hard frames.
Entities, intents, sentiment, contract clauses, and ICD codes labeled across 20+ languages with consistent taxonomies.
Preference pairs, response ratings, safety judgments, and reasoning traces curated at the scale modern post-training needs.
Medical, financial, and legal labeling on-premise. No PHI, PII, or proprietary data leaves your infrastructure.
Build evaluation sets, benchmarks, and gold-standard datasets in days instead of months. Versioned and reproducible.
Plug a labeling layer into your existing stack — APIs, S3 watchers, webhooks, and Airflow / Dagster integrations.
We support text, documents, images, tabular records, time-series, audio, and RLHF-style preference data. The pipeline is adapted to your taxonomy, export format, and quality requirements.
Foundation models create first-pass labels, active learning routes uncertain items to reviewers, and agreement checks catch drift. Humans focus on the ambiguous slice instead of relabeling everything manually.
Yes. For sensitive datasets, the workflow can run in your VPC or on-premise environment so regulated or proprietary data does not need to leave your control.
You receive versioned labels in practical training formats such as JSON, CSV, COCO, or Parquet, plus the repeatable pipeline so new batches can be processed again without starting from scratch.
Next step
Bring 100 raw items — text, images, rows, signals, audio — and we will pre-label them live, walk through the uncertainty queue, and quote a real plan for the full dataset before the meeting ends.