Custom data collection
On-the-ground gathering of images, audio, text and field data, anywhere in the world.
Datasets
The right data, sourced where it actually lives.
Models are only as good as their data. Omdena collects, annotates and validates custom datasets through a global network across 60+ countries, so you train on data that reflects the real world your model will run in.
60+
Countries for data sourcing
Global
Contributor network
QUALITY VERIFIED
Spec · Collection · Annotation · Validation — every dataset is checked against an agreed quality bar before delivery.
What we deliver
Data for the model you're actually building.
Annotation & labeling
Bounding boxes, segmentation, classification, transcription and entity labeling at scale.
Domain datasets
Specialized data for health, agriculture, climate, finance and other verticals.
Low-resource languages
Text and speech data for languages and dialects the big datasets ignore.
Image & video
Curated visual data for detection, segmentation and recognition tasks.
Audio & speech
Recorded, transcribed and labeled audio across accents and environments.
Synthetic & augmented
Generated and augmented data to fill gaps and balance rare classes.
Eval & benchmark sets
Held-out, gold-labeled sets that tell you honestly how a model performs.
How a dataset is built
A pipeline you can audit, end to end.
From a precise spec to a validated, documented deliverable — every record traceable, every label checked, every edge case accounted for. You get a datasheet, not a mystery dump.
FORMATS & MODALITIES
ImageVideoTextAudio & speechGeospatialTabularMultilingualSensor / IoT
DATA PIPELINE · spec → collect → label → validate
01
Define the spec
Schema, label guidelines, coverage, quality bar
02
Collect & source
Global network gathers representative data
03
Annotate & label
Multi-annotator labeling with adjudication
04
Validate & deliver
QA, agreement scoring, datasheet, handover
When teams need custom data
Where off-the-shelf data falls short.
Training a new model
Build the dataset a model needs when nothing suitable exists off the shelf.
Fine-tuning LLMs
Instruction, preference and domain data to specialize a foundation model.
Benchmarks & evals
Trustworthy gold sets to measure accuracy, safety and regressions.
Low-resource coverage
Reach languages, regions and edge cases public data leaves out.
Vision in the field
Collect imagery in the exact conditions a deployed model will face.
Filling data gaps
Augment an existing dataset to balance classes or cover blind spots.
A simplified process, powered by Umaku
Explore the platform From data spec to verified delivery.
01
Spec
Umaku captures the schema, label guidelines, coverage and quality bar as structured scope.
02
Collect
A vetted global network sources representative data against the spec, with provenance tracked.
03
Review
Four AI agents review every commit — scope, quality, DevOps, bugs — with evidence.
04
Validate
Annotations are QA'd and scored for agreement; gaps are flagged and re-worked.
05
Deliver & document
A clean, versioned dataset plus a datasheet on provenance, schema and known limits.
Why it matters
Better data beats a bigger model.
60+
Countries to source representative, real-world data, including low-resource regions.
Datasheet
Every delivery documented — provenance, schema, label guidelines and known gaps.
Verified
Quality checks and inter-annotator agreement before a dataset is signed off.
Often, yes. A network across 60+ countries lets us collect data on the ground — local languages, regional imagery, field conditions and rare scenarios — that simply doesn't exist in public datasets. We scope what's realistic up front before any work begins.
We agree clear label guidelines, use multiple annotators with adjudication, and measure inter-annotator agreement. The pipeline runs on Umaku, so quality checks and reviews are logged with evidence, and the dataset isn't delivered until it clears the agreed bar.
Consent, anonymization and clear licensing are part of the spec from day one. You receive a datasheet documenting provenance and usage rights, so the data is safe to train on and defensible if anyone asks where it came from.
We can. A dataset engagement plugs straight into AI Development or AI Research & Development — the same team that knows your data can train, evaluate and ship the model, with no handoff lost in translation.
Related case studies
All
Environment · Edge AIFrom AI Ultra-Early Fire Detection to €10.5M of Funding
How Dryad Networks used edge AI to detect forest fires at their earliest stage.
Read case study
Humanitarian Response · MappingAn AI-Assisted Mapping Tool with Humanitarian OpenStreetMap
A collaborative mapping workflow that supports humanitarian teams with faster geospatial data.
Read case study
Insurance · Computer VisionMyCover.ai: Elevating Vehicle Insurance with AI
An AI claims-evaluation workflow that dramatically reduced processing time and errors.
Read case studyNeed data that doesn't exist yet?
Tell us the spec and where the data lives — we'll scope collection, annotation and validation, usually within one business day.
Request a dataset