Data annotation that builds ML training sets you can trust.
TL;DR. TechSure labels text, images, video, and audio for ML training. Model pre-labels are corrected by named annotators, QA'd by senior reviewers, and exported in your format. Across our pilot clients in the last 12 months, inter-annotator agreement is 0.91 Cohen kappa on text and 0.89 IoU on bounding boxes.
What does TechSure's data annotation service actually label?
We label text, images, video, and audio for ML training and evaluation. Inputs are your raw data and your label schema. Outputs are the labeled dataset in your format, with confidence scores, a held-out QA pass, and the inter-annotator agreement on every batch.
What we label, in plain terms
- Image classification, object detection, segmentation, polygon and keypoint annotation
- Video frame labeling, object tracking, action recognition, temporal segmentation
- Text classification, named entity recognition, sentiment, intent, slot filling
- Audio transcription, speaker diarization, sound event labeling, phoneme tagging
- Multi-modal: image plus text, video plus audio, document plus table
A live demo of this discipline's workflow mounts here. Static metrics shown until then.
How does the annotation pipeline run from raw data to labeled dataset?
Five stages, instrumented end to end. Every label has confidence, every batch has QA, every dataset has a version.
The five-stage pipeline
- Schema design. We align on the label set, the taxonomy, the edge cases, and the QA criteria. The schema is signed off before we start labeling.
- Gold set. We label 200 to 500 examples together, in a working session. The gold set becomes the ground truth for QA and the model pre-training.
- Annotate. Named annotators label in batches. Pre-annotations from a model speed the work; humans correct and add edge cases.
- QA. A second annotator reviews every batch. Disagreements escalate to a senior reviewer with the gold set as the reference.
- Deliver. The labeled dataset is exported in your format (COCO, Pascal VOC, YOLO, JSONL, CSV) with a manifest and a version tag.
How is the engagement structured, and what does it cost?
Statement-of-work, with a fixed monthly retainer sized to the dataset volume, the label class count, and the QA depth. We do not bill per label. The retainer covers the annotators, the senior reviewers, the engineers, the dashboard, and the named account owner.
What the monthly price covers
- Named annotators trained on your label schema and gold set
- Senior reviewers for QA on ambiguous or high-stakes batches
- A TechSure engineer on the pod, owning the model pre-annotations and the export pipelines
- Engagement dashboard with live kappa, IoU, and label distribution
- Monthly review with the named account owner and a TechSure partner
FAQ
Common questions on Data Annotation
What does TechSure's data annotation service actually label?
We label text, images, video, and audio for ML training and evaluation. Inputs are your raw data and your label schema. Outputs are the labeled dataset in your format, with confidence scores and a held-out QA pass.
How accurate is the annotation?
Across our pilot clients in the last 12 months, inter-annotator agreement is 0.91 Cohen kappa on text and 0.89 IoU on bounding boxes. We publish your actual numbers on the engagement dashboard, broken down by label class.
Can you use a model pre-label and let humans correct?
Yes. That is the default. The model pre-labels, the human corrects, and the corrections are fed back to improve the model. We work with your existing model or train a baseline for the gold set session.
What about sensitive data, like medical images?
We sign BAAs for HIPAA scopes. Sensitive queues are routed to a named subset of annotators with background-check clearance. The training environment is on isolated infrastructure, not shared with other clients.
What format do you deliver in?
We deliver in your format. COCO JSON, Pascal VOC XML, YOLO TXT for image. JSONL with span offsets for text. RTTM for audio. Custom JSON schema for downstream training. Every dataset has a version tag and a manifest.
Have a workflow that needs a team behind it?
Send a brief. We come back in 24 hours with a scope, a price band, and a named engineer.
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