High-quality training data for AI teams.
For over a year we've delivered annotation projects for AI and machine-learning teams—across computer vision, NLP, audio, LiDAR, and generative AI. Our work is confidential and NDA-bound, so we don't name clients, but our pipelines, quality standards, and tooling are ready today.
We're ready to take on new annotation projects.
1+ yr
delivering annotation projects
11
annotation capabilities
99%+
QA accuracy target
NDA
confidential, secure delivery
End-to-end annotation capabilities
From bounding boxes to LLM evaluation, we cover the full spectrum of training-data needs.
Image Annotation
Precise labeling of images for classification, detection, and recognition models across any domain.
Video Annotation
Frame-by-frame object tracking and event labeling for motion, behavior, and activity models.
Bounding Box Annotation
Tight 2D boxes for object detection—single or multi-class, at scale, with consistent quality.
Semantic Segmentation
Pixel-level masks that teach models to understand every region of a scene.
LiDAR Annotation
3D point-cloud labeling and cuboids for perception in autonomous and robotics systems.
NLP / Text Annotation
Entity tagging, intent, sentiment, and classification for language-understanding models.
Audio Transcription
Accurate, timestamped transcripts across languages, accents, and noisy environments.
Audio Annotation
Sound event, speaker, and emotion labeling for speech and audio ML pipelines.
LLM Data Annotation & Evaluation
Prompt–response rating, RLHF, red-teaming, and output evaluation for generative AI.
Data Collection & Curation
Sourcing, cleaning, and structuring datasets tailored to your model's needs.
Data Quality Assurance
Multi-pass review and consensus checks to keep every label accurate and consistent.
See the work
Representative examples of the annotation types we deliver across computer-vision projects.
Bounding Box Annotation
Tight 2D boxes on every vehicle and pedestrian for object detection.
Semantic Segmentation
Pixel-level masks separating road, vehicles, people, and greenery.
LiDAR Annotation
3D cuboids on a point cloud for autonomous-driving perception.
Training data for every AI domain
We understand the labeling nuances that matter in each field.
Automotive & Autonomous Driving
Robotics
Healthcare AI
Retail & E-commerce
Agriculture AI
Logistics & Supply Chain
Generative AI / LLM
Computer Vision
From pilot to production
A clear, low-risk path—starting with a small paid pilot so you can validate quality before scaling.
01
Scope & Pilot
We review your data, define labeling guidelines, and run a small paid pilot to lock in quality.
02
Tooling & Guidelines
We set up the right platform and a clear rubric so every annotator labels consistently.
03
Annotate & QA
Trained annotators label at scale while multi-pass QA keeps accuracy on target.
04
Deliver & Scale
You receive clean, structured data on schedule—then we ramp capacity as you grow.
Quality, security, and scale you can trust
Human-in-the-loop QA
Every dataset passes multi-pass review and consensus checks before it reaches you.
Secure & NDA-bound
Confidential handling of your data with strict access controls and signed NDAs.
Your tools or ours
We work in SageMaker Ground Truth, CVAT, V7, Supervisely, or your in-house platform.
Scales pilot to production
Start with a small pilot and ramp annotator capacity as your model needs grow.
Domain-experienced
Hands-on across automotive, healthcare, robotics, retail, agriculture, and more.
Clear communication
Transparent guidelines, regular updates, and fast turnaround on every batch.
Platforms & tools we work in
Ready when you are