AVIA · Annotation
Annotation that learnsfrom your data
A labeling loop built on an embedding map: see every sample in one shared space, label a few, and let active learning, propagation, and quality checks do more with less of your time.
Look at your data
It starts with an embedding map
See every image and text in one shared space, and search by a phrase or a reference image.
- Embedded with CLIP, DINO, or SigLIP
- Kept in a vector index for fast retrieval
- Embeddings sharpen as the models train
Active learning
Label less with every round
Label a small set, train a fast model, and let it auto-label the rest.
- Uncertainty and diversity pick what to label next
- The hand-labeled share shrinks each round
- Built for large sets — past 100k samples
AI annotation
Annotate by example then propagate
Label one reference and propagate it across the whole set.
- Or name a category in text; the system labels it to spot-check
- Single-image prompts: point, box, text, reference image, or reference box
Quality check
Catch bad data before it costs you
Outlier detection and image checks flag bad samples before they reach training.
- Embedding outliers surface at the sparse edges
- Flags too dark, blurry, over- or under-exposed
- Catches near-duplicate frames
How it works
One loopend to end
Embed everything into one space, cold-start a model on a small labeled set, auto-label and propagate across the rest, then curate with quality checks — and feed what you learn back in.
Embed
Images and text map into one shared vector space you can search.
Cold-start & train
Label a small set and train a fast model to kick off the loop.
Auto-label & propagate
The model returns to label, and references propagate across the set.
QA & curate
Outlier and image-quality checks gate what becomes training data.
Capabilities
Everythingthe loop runs on
Multimodal embeddings
CLIP, DINO, and SigLIP map images and text into one shared space.
Model-assisted pre-labeling
Built-in detectors, segmenters, and counters draft labels for you.
Embedding search & retrieval
Find samples by text or by a reference image across a vector index.
Uncertainty & diversity sampling
Budgeted selection surfaces the most informative samples to label next.
Image quality checks
Flag blur, brightness, exposure, and near-duplicate frames automatically.
Bounded active-learning loop
Each iteration trains, scans, auto-labels, and re-enters the loop.
In practice
Where teamsput it to work
Robotics & industrial inspection
Label perception and defect data at scale and close the loop from field errors.
Retail & e-commerce catalogs
Tag and dedupe huge product catalogs by example and by text.
Documents & multimodal
Annotate across documents, images, and frames in one shared space.
By the numbers
Built for scaletuned for less labeling
100k+
Datasets it's built for
↓ / round
Share you label by hand
5+
Image-quality dimensions
BYO
Storage, models & keys
Built to integrate
Runs in your environment
BYOS
Keep data in your own storage; AVIA reads only what it needs.
BYOM
Bring your own models and endpoints for embedding and pre-labeling.
BYOK
Your keys, your access controls, full audit trail.
FAQ
Annotationanswered
Images and text are embedded with CLIP, DINO, or SigLIP into one shared vector space, so you can search and match across both.
See your datathen label what matters
Watch AVIA Annotation turn an embedding map into a labeling loop that needs less of you each round.