Image, video, text, audio, and specialized data, labeled to the standard that decides whether a model ships or stalls.
ISO 27001 Certified · GDPR Aligned
Every ceiling a model hits traces back to its data. The label it learned from, the edge case no one caught, the standard that drifted halfway through the set. Annotation is where that ceiling is set, and raising it takes precision at every label.
We produce the labeled data that models reach their limit on, across every modality, to a standard you can measure.
One annotation capability across the data types your model learns from, held to a single standard of precision.
The labels that teach a model to see, from object boundaries to dense scene understanding and motion across frames.
For autonomous and perception systems, the precision requirement on object boundaries is defined by what happens when the label is wrong, not by annotation cost. We produce bounding boxes, polygon masks, and semantic segmentation to the geometric tolerance your architecture requires, with systematic coverage of occlusion, small objects, and scene transitions that models trained on undifferentiated volume consistently underperform on.
Capabilities: Bounding boxes, polygon and semantic segmentation, instance segmentation, keypoints, 3D point cloud and LiDAR, video object tracking, and OCR.
The labels that teach a model to read meaning, the entities, intent, and sentiment it has to resolve.
Named entity recognition and intent classification accuracy degrades where the label taxonomy does not reflect actual domain usage. We build taxonomies calibrated to your domain with your subject matter experts before calibrating annotators against them, so the label space is correct before production begins rather than corrected after the first model evaluation.
Capabilities: Classification and taxonomy, named entity recognition, intent and dialogue labeling, sentiment and aspect labeling, and semantic relationships.
Multilingual coverage is available, with dedicated depth on our African Languages practice. Explore African Languages.
The labels that teach a model to hear, precise to the timestamp.
Transcription accuracy varies by speaker diarization quality, acoustic channel conditions, and domain vocabulary coverage. We produce verbatim and normalized transcription to the specification your ASR pipeline requires, with speaker attribution at the turn level, phonetic annotation for low-resource language training, and audio event labels at the granularity your model architecture consumes.
Capabilities: Verbatim and normalized transcription, speaker diarization, phonetic and ASR annotation, and audio event and scene classification.
The labels that need expert judgment, for the domains where a wrong one is expensive.
Annotation in domains such as medical imaging, legal documents, financial instruments, and safety-critical environments requires annotators whose judgment can be validated by a subject matter expert. We source domain specialists through our academic partnerships, calibrate them against your domain experts, and build the taxonomies that encode institutional knowledge into a label structure your pipeline can train on.
Capabilities: Custom taxonomies and guidelines, multi-modal data combining text, image, audio, and sensor inputs, and edge-case and safety-critical annotation, calibrated with your experts and the specialists we access through academic partnership.
The standard shows in how the data is produced and how the quality is proven.
Every delivery carries agreement scores, audit sampling, and a quality report, so the standard is verified. Agreement scores are tracked per annotator, per label type, and per batch, so quality issues are localized to their source rather than averaged away. Every delivery includes the score distribution alongside the dataset.
The bottleneck on most annotation projects is not volume, it is the availability of annotators who can produce defensible labels on hard cases. We match specialists to your work through our academic partnerships, hold them on the project, and accumulate context across batches so the expertise is there when the hard cases arrive and quality compounds rather than resetting.
Schema is agreed during scoping. Delivery is in JSONL, Parquet, or your format of choice, with the quality report, agreement metrics, and label documentation packaged alongside the dataset, ready to train on.
Step 01
We scope the labels, the guidelines, and the quality thresholds with your team, and surface the edge cases before production. Every taxonomy decision made in scoping is documented and versioned so annotators make consistent calls as the project scales, and edge cases surfaced early do not become quality failures later.
Step 02
We calibrate annotators against your guidelines and set agreement targets before the first batch ships. We run calibration checks at the start of each new batch and when the task type shifts, so the standard holds across the project.
Step 03
We produce against documented standards with live quality monitoring, sharpening the guidelines as new cases surface.
Step 04
We review in stages, automated checks, senior audit sampling, and inter-annotator agreement, and deliver in your format with a full quality report that covers agreement scores, audit sample results, edge case flags, and any guideline changes made during production.
Image and video, text and language, audio and speech, and specialized multi-modal data. A single project can span several modalities under one structure, with consistent standards across all of them.
Calibration before production and measurement throughout. Annotators are aligned on your guidelines, agreement targets are set before the first batch, and quality is monitored with automated checks, senior audits, and inter-annotator agreement scoring as the work scales.
Disagreements on close calls are resolved through adjudication by a senior annotator or domain specialist, with the adjudicated label and the rationale documented alongside the disagreement. Systematic disagreements signal a taxonomy problem, not an annotator problem, and we escalate those to a guideline revision rather than overriding labels in volume.
Yes. For work that turns on expert judgment, we calibrate with your subject matter experts and draw on specialists through our academic partnerships, building custom taxonomies and guidelines to your domain.
Your preferred format and schema, with the labeled dataset, a quality report, agreement metrics, and full documentation of the standards applied, defined during scoping so the data integrates with your pipeline.
You do. The labeled data and its rights are yours, produced under NDA and handled under our ISO 27001 certified information security management system.
With a scoping conversation and a complimentary annotated sample, so you can evaluate the quality before committing to production volume.
Scope an annotation project with our team, or request a sample to see the standard before you commit.
