We collect, annotate, and validate
real-world data for the world's most demanding AI teams.
Image · Video · Audio · Text · Multimodal · Field Data
Foundation model labs, computer vision teams, robotics programs, and voice AI teams come to Ayta when off-the-shelf datasets run out of real-world coverage.
One operation. Every stage of the data pipeline.
Give us the specification — we build the operation around it: sourcing, field capture, annotation, quality, and delivery, run as one connected system instead of a patchwork of vendors.
Data Collection
Digital and in-the-field collection across image, video, audio, and text — sourced to your exact demographic, device, and environment spec.
Field Operations
When the data needs to exist in the real world, we deploy trained collectors on location to capture it — not just annotate what already exists.
Annotation & Labeling
Bounding boxes, segmentation, transcription, and classification — labeled by trained annotators, not an anonymous crowd.
Human & AI-Assisted QA
Automated checks plus human review at every stage, so quality is verified before it ever reaches your pipeline.
RLHF & Model Evaluation
Human preference ranking, response evaluation, and structured model assessment for teams fine-tuning generative systems.
Dataset Delivery
Clean handoff — raw assets, QA reports, metadata, and documentation, packaged the way your pipeline expects.
One partner. Every modality.
Built for the teams pushing AI into the real world.
From requirement to running operation.
Every engagement follows the same disciplined operating sequence — scoped tightly, piloted first, and built to scale without losing quality.
Scope
Volume, modality, geography, demographics, languages, quality, and timeline defined before anything starts.
Build
Recruit, screen, train, and deploy the right team for your requirement.
Produce
Collect, annotate, and validate against your spec.
QA
Audit, calibrate, rework, and approve — nothing ships without sign-off.
Deliver
Package, validate, transfer, and report on your schedule.
Scale
Expand geography, workforce, and throughput as your program grows.
Quality is a process, not a promise.
Every batch moves through the same five-stage quality system — from contributor validation to client acceptance — before it reaches you.
Contributor validation
Training, qualification, golden questions, and eligibility checks before anyone touches your data.
Automated checks
Duplicate detection, metadata validation, format checks, completeness, and outlier detection.
Human QA
Sampling, second-pass review, and expert review where the work requires it.
Calibration
Guideline training, inter-annotator agreement, dispute resolution, and continuous calibration.
Client acceptance
A feedback loop with rework and root-cause analysis before final delivery.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →