Dataset
sample ready
Labels
QA passed
Delivery
your format
Rights-ready
Human-reviewed
Multi-domain data
Structured metadata
Custom delivery
Services
One team for collection, production, annotation, and delivery
A dataset is not just a folder of files. We plan the data spec, create or source the content, label it, review it, and deliver it in a structure your ML team can use.
01
Real-world sourcing
Data Collection
Scenarios, demographics, locations, devices, lighting, and edge cases matched to your model task.
02
Controlled capture
Data Production
Controlled data creation for visual, audio, text, sensor, and domain-specific AI training needs.
03
Human-reviewed labels
Annotation & QA
Bounding boxes, segmentation, classification, captions, keypoints, and metadata with quality checks.
04
Rights-aware packages
Dataset Licensing
Ready-to-license or custom-packaged datasets with usage clarity and structured delivery.
Data types
Not limited to one modality or one industry
Photo and video are strong use cases for us, but the workflow is built around the model requirement: modality, domain rules, annotation schema, and delivery format.
Visual Data
Images, video, frames, object scenes, action capture, and visual annotations.
Audio & Speech
Voice, environmental sound, conversations, sound events, and transcripts.
Text & Documents
Classification sets, prompts, transcripts, structured text, and document data.
Sensor & Structured Data
Metadata, tabular data, device logs, scenario attributes, and structured records.
Domain-Specific Data
Healthcare, agriculture, retail, safety, industrial, and niche workflows.
Sample data
Make the offer tangible before the first call
These are example packages. Each project starts with a smaller reviewable sample so your team can approve labels, metadata, and delivery format before scale.
Action labels / scene metadata / consent-backed capture
Human Actions Video
Multi-angle short clips for action recognition, safety models, and multimodal video understanding.
class
bottle
bbox
0.94
Object labels / bounding boxes / environment tags
Product & Object Images
Controlled and real-world product photos for detection, shelf analysis, and visual search.
Voice clips / sound labels / transcripts / event metadata
Speech & Audio Events
Speech, environmental sound, and event datasets for audio classification and multimodal models.
Brief-driven / specialist workflows / custom schema
Custom Domain Dataset
Healthcare, retail, industrial, safety, or niche data produced around your model requirements.
Workflow
From model requirement to usable dataset
The process is intentionally visible. You see the spec, sample, labels, QA pass, and delivery structure before the dataset becomes expensive to scale.
Brief
Define the model task, target classes, edge cases, constraints, and success criteria.
Data Spec
Turn requirements into collection rules, metadata schema, and annotation guidelines.
Production
Collect, capture, or source the required photo, video, audio, or domain-specific data.
Rights
Prepare consent, usage clarity, release handling, and sensitive-data constraints.
Annotation
Label, classify, segment, caption, and enrich the data according to your ML pipeline.
QA
Review samples, fix inconsistencies, and validate labels, metadata, and structure.
Delivery
Export in your preferred format and hand over a clean, documented dataset package.
Use cases
Data workflows for teams building real AI products
The same production process adapts to computer vision, speech, text, sensor, multimodal, and specialist domain requirements.
Computer Vision
Images and video for detection, classification, segmentation, and visual search models.
Multimodal AI
Cross-modal datasets with captions, audio, transcripts, metadata, and context.
Speech & Audio AI
Voice, sound event, conversation, and transcript datasets for speech and audio models.
NLP & Document AI
Text, document, prompt, classification, extraction, and transcript datasets.
Robotics & Sensor AI
Task scenes, sensor context, device logs, environment states, and edge cases.
Domain AI
Healthcare, agriculture, retail, safety, industrial, and specialist workflows with domain review.
Why Kvet.io
Custom data should feel engineered, not improvised.
We keep the creative production and ML delivery sides connected, so the final dataset is useful for training instead of just visually attractive.
Production-first data team
We plan capture conditions and dataset structure before files are produced.
Custom data workflows, not generic scraping
We build the data around your model task, modality, domain rules, and delivery format.
Fast sample iteration
Start with a small reviewable sample, refine the spec, then scale the dataset.
Quality system
Built for model training, not just file delivery
The output has to survive engineering review: clear rights, consistent labels, usable metadata, and the export format your training pipeline expects.
7-step
dataset workflow
6+
AI use-case groups
Custom
schemas and exports