Production-readiness review
Make an evidence-backed go/no-go decision before production scale.
Production readiness is a review of connected controls, not a feature checklist. A project is ready only when access, data, policy, workflow, quality, automation, and delivery are all owned and exercised.
The operating model
Image annotation
Bounding boxes, cuboids, polygons, masks, brush and eraser, keypoints, skeletons, lines, properties, relations, Magic Touch, Detect all objects, Find Similar, and Magic Crop
Video annotation
Frame and timeline navigation, persistent tracks, single- and multi-object Auto-Tracking, full bidirectional/forward/backward tracking, object and mask interpolation, dynamic class properties, and dynamic Item Properties
Audio annotation
Waveform and spectrogram views, temporal events, segmentation, transcription-oriented workflows, and playback controls
Text annotation
Entities, relations, whole-item properties, configurable text windows, and source-text editing
PDF annotation
Native multipage navigation, selectable PDF text, copied text values, bounding boxes from text or embedded image regions, page-aware history, and UUEF export
Medical annotation
Grouped DICOM volumes, synchronized axial/sagittal/coronal and 3D views, clinical ontologies, and DICOM, NIfTI, and NRRD ingestion
Multiview Workbench
Current File and Multiple Files modes, 1×1 through 4×4 grids, grouped custom layouts, resizable panels, and family-aware tools per tile
Multimodal data
Data Groups, filename-based auto-grouping, custom layouts, mixed datasets, mixed releases, and grouped project work items
AI assistance
Magic Touch, Detect all objects, Find Similar, Magic Crop, AI captioning, Auto-Tracking, interpolation, batch auto-annotation, and workflow Model stages
Ontologies
Classes, class attributes, typed class properties, Item Properties, relations, annotation-side schema creation, required/dynamic fields, validation, unlimited conditional depth, Logic Map, Live switching, snapshots, and version history
Workflows
Project, Annotate, Review, Model, Archive, and Complete stages; approve/reject routes; specialist review; assignment; self-assignment; manager override; and task lifecycle controls
Queues
Stage-based Task Queues, upload-oriented Batch Queues, assignment filters, priorities, claiming, submission, approval, rejection, and rework
Quality operations
Cohort filters, Grid/List/Embedding inspection, annotation-aware Display View controls, project instructions, item-level comments, owned issues, human review, notifications, project statistics, and member statistics
Data curation
Assets, folders, cloud folders, collections, activity history, metadata, advanced filters, Video/Frames browsing, filename-based auto-grouping, custom multimodal layouts, embeddings, natural-language search, duplicate detection, and outlier detection
Datasets
Working drafts, immutable published versions, version history, restore, duplicate, exact-version project attachment, detachment, and restoration
Releases
Versioned annotation snapshots, public/private visibility, read-only viewing, cloning, train/validation/test splits, UUEF, Standard Bundle, family-specific formats, and stable tokenized URLs
AI models
Public and private model catalogs, external-model registration, validation, input/output mapping, class mapping, and workflow integration
Accounts and workspaces
Email or Google authentication, email verification, password recovery, TOTP two-factor authentication, onboarding, workspace switching, settings, and quotas
Enterprise access
Members, invitations, built-in and custom roles, granular permissions, and API-key management
Automation
Python SDK and CLI access across projects, assets, datasets, ontologies, workflows, queues, embeddings, cloud storage, and releases
25. Core product value
One operating model across modalities
Image, video, audio, text, medical, PDF, and grouped multimodal cases share projects, ontologies, workflows, queues, review, and release concepts. Teams can standardize governance once and apply it across different forms of training data.
Context-preserving annotation
Unitlab does more than accept multiple file formats. It groups related files into one work item and lets teams control how those sources appear together. Supported patterns include two- and four-camera views, synchronized medical views, document with audio, and video with PDF and audio.
Reusable domain knowledge
Ontologies turn domain rules into reusable annotation structures. They support spatial objects, classifications, events, entities, relations, required properties, dynamic video properties, validation rules, and unlimited conditional nesting. Live versions, snapshots, history, Logic Map, and deleted-item restoration keep those structures manageable over time.
Human and model work in one workflow
Project, Annotate, Review, Model, Archive, and Complete stages form forward and correction routes. Teams can add a second Review stage for specialist evaluation, configure eligible participants, and return rejected work to the appropriate annotation stage. Model output remains part of the same assignment, review, and release process as human-created annotations.
Reproducible data handoffs
Datasets separate working changes from immutable published versions. Projects attach exact versions, and releases package annotation snapshots, source context, metadata, and data splits for downstream use. A model experiment can therefore reference the precise data, ontology, and release used to produce it.
Integration with existing data operations
Cloud storage connections, public and private AI models, API keys, the Python SDK, the CLI, custom metadata, embeddings, and vector search allow Unitlab to fit into an existing AI data workflow while preserving the platform’s project, review, and release controls.
Use this in production
Record a named approver and evidence for every control area.
Exercise rejection, invalid, failed, skipped, and escalation paths.
Validate service identities, retries, model versions, and Batch Queue observability.
Define the trigger for the next review: schema change, workflow change, new modality, new source, or material model update.
A material configuration change reopens the relevant part of the readiness review.
Continue with Unitlab: AI training-data annotation · multimodal data curation