# Unitlab AI

## Home

- [Welcome to Unitlab AI](https://docs.unitlab.ai/readme.md): Unitlab AI is an enterprise multimodal data platform for AI teams to curate, annotate, manage, version, and prepare training data at scale.

## Documentation

- [Unitlab Product Documentation](https://docs.unitlab.ai/documentation/start-here.md): Operate Unitlab from source ingestion through governed multimodal annotation and reproducible delivery.
- [Welcome to Unitlab](https://docs.unitlab.ai/documentation/get-started/welcome-to-unitlab.md): Understand Unitlab’s role as an enterprise multimodal data-production platform.
- [Platform navigation](https://docs.unitlab.ai/documentation/get-started/platform-navigation.md): Learn the workspace-level and project-level navigation model.
- [The Unitlab object model](https://docs.unitlab.ai/documentation/get-started/the-unitlab-object-model.md): Distinguish assets, groups, datasets, projects, tasks, and releases.
- [End-to-end quickstart](https://docs.unitlab.ai/documentation/get-started/end-to-end-quickstart.md): Move a representative sample from ingestion to a validated release.
- [Production-readiness review](https://docs.unitlab.ai/documentation/get-started/production-readiness-review.md): Make an evidence-backed go/no-go decision before production scale.
- [Data overview](https://docs.unitlab.ai/documentation/data/data-overview.md): Understand Data Space as the durable source and curation layer.
- [Data upload](https://docs.unitlab.ai/documentation/data/data-upload.md): Upload current local files and monitor processing to a usable state.
- [Cloud providers](https://docs.unitlab.ai/documentation/data/cloud-providers.md): Connect approved cloud storage and import the exact source prefix.
- [Data folders](https://docs.unitlab.ai/documentation/data/data-folders.md): Organize durable source resources without confusing folders with versions.
- [Data assets](https://docs.unitlab.ai/documentation/data/data-assets.md): Inspect, search, select, and lifecycle individual source resources.
- [Data curation](https://docs.unitlab.ai/documentation/data/data-curation.md): Build precise cohorts with filters, metadata, tags, collections, and frame granularity.
- [Data Groups and layouts](https://docs.unitlab.ai/documentation/data/data-groups-and-layouts.md): Preserve cross-view and cross-modal context as one unit of work.
- [Embeddings, similarity, and outliers](https://docs.unitlab.ai/documentation/data/embeddings-similarity-and-outliers.md): Investigate distribution, nearest neighbors, duplicates, and outliers.
- [Datasets overview](https://docs.unitlab.ai/documentation/datasets/datasets-overview.md): Understand datasets as reusable, versioned membership definitions.
- [Create a dataset](https://docs.unitlab.ai/documentation/datasets/create-a-dataset.md): Create a named reusable cohort from validated assets or Data Groups.
- [Dataset versions and history](https://docs.unitlab.ai/documentation/datasets/dataset-versions-and-history.md): Publish immutable snapshots and understand version-first behavior.
- [Attach datasets to projects](https://docs.unitlab.ai/documentation/datasets/attach-datasets-to-projects.md): Connect the intended published membership to a project and inspect created work.
- [Dataset lifecycle and recovery](https://docs.unitlab.ai/documentation/datasets/dataset-lifecycle-and-recovery.md): Duplicate, restore, detach, or retire datasets without losing provenance.
- [Projects overview](https://docs.unitlab.ai/documentation/projects/projects-overview.md): Understand projects as the operating system for annotation production.
- [Create and configure a project](https://docs.unitlab.ai/documentation/projects/create-and-configure-a-project.md): Establish ownership, operating intent, and initial controls.
- [Project data](https://docs.unitlab.ai/documentation/projects/project-data.md): Attach, inspect, filter, and reconcile the work population.
- [Project Instructions](https://docs.unitlab.ai/documentation/projects/project-instructions.md): Write the operational labeling policy visible inside work.
- [Project data QA](https://docs.unitlab.ai/documentation/projects/project-data-qa.md): Inspect project work as a cohort with the project Embedding view, status filters, and Display View controls.
- [Project settings and lifecycle](https://docs.unitlab.ai/documentation/projects/project-settings-and-lifecycle.md): Rename, inspect, retire, or delete a project with explicit impact controls.
- [Annotation Workbench](https://docs.unitlab.ai/documentation/annotations/annotation-workbench.md): Understand the shared shell, item state, controls, and navigation.
- [Image Annotation](https://docs.unitlab.ai/documentation/annotations/image-annotation.md): Create boxes, polygons, masks, cuboids, landmarks, properties, and relations on still images with AI-assisted precision.
- [Video Annotation](https://docs.unitlab.ai/documentation/annotations/video-annotation.md): Create frame-accurate object tracks, temporal events, dynamic properties, and synchronized video annotations at scale.
- [Text Annotation](https://docs.unitlab.ai/documentation/annotations/text-annotation.md): Create entities, nested spans, classifications, relations, and structured properties for NLP and LLM datasets.
- [Document & PDF Annotation](https://docs.unitlab.ai/documentation/annotations/document-and-pdf-annotation.md): Annotate native PDF text, images, tables, regions, pages, relations, and document properties without losing document context.
- [Audio Annotation](https://docs.unitlab.ai/documentation/annotations/audio-annotation.md): Label temporal events, speakers, transcripts, classifications, properties, and relations on synchronized waveform and spectrogram views.
- [Medical Annotation](https://docs.unitlab.ai/documentation/annotations/medical-annotation.md): Annotate DICOM and medical imaging with synchronized multiplanar views, clinical ontologies, timelines, tracking, and AI-assisted segmentation.
- [Pathology Annotation](https://docs.unitlab.ai/documentation/annotations/pathology-annotation.md): Annotate whole-slide images with deep zoom, tissue and cellular labels, pathology ontologies, AI assistance, and expert review.
- [Geospatial Annotation](https://docs.unitlab.ai/documentation/annotations/geospatial-annotation.md): Annotate satellite, aerial, and large-raster imagery with deep zoom, spatial coordinates, structured ontologies, and AI-assisted segmentation.
- [AI-assisted Annotation](https://docs.unitlab.ai/documentation/annotations/ai-assisted-annotation.md): Use interactive and batch assists inside human-controlled quality gates.
- [Multimodal overview](https://docs.unitlab.ai/documentation/multimodal-annotations/multimodal-overview.md): Understand grouped context, layouts, and one-active-editor behavior.
- [Multiview Workbench](https://docs.unitlab.ai/documentation/multimodal-annotations/multiview-workbench.md): Operate Current file, Multiple files, and Grouped Workbench modes.
- [Multi-camera video](https://docs.unitlab.ai/documentation/multimodal-annotations/multi-camera-video.md): Annotate synchronized camera views of the same event as one governed work unit.
- [Media and document layouts](https://docs.unitlab.ai/documentation/multimodal-annotations/media-and-document-layouts.md): Combine image, video, audio, text, PDF, and structured evidence in one custom Data Group layout.
- [Medical study and clinical document](https://docs.unitlab.ai/documentation/multimodal-annotations/medical-study-and-clinical-document.md): Review synchronized medical imaging and clinical documentation as one governed case.
- [Ontologies overview](https://docs.unitlab.ai/documentation/ontologies/ontologies-overview.md): Navigate workspace and project ontologies, understand Live state, and inspect the complete semantic contract.
- [Classes and annotation types](https://docs.unitlab.ai/documentation/ontologies/classes-and-annotation-types.md): Create reusable concepts and choose geometry from the downstream task backward.
- [Properties, relations, and Item Properties](https://docs.unitlab.ai/documentation/ontologies/properties-relations-and-item-properties.md): Create typed schema content from the Ontology builder or directly from Annotation Workbench.
- [Validation and conditional logic](https://docs.unitlab.ai/documentation/ontologies/validation-and-conditional-logic.md): Configure Required state, type-aware validation, help text, and nested conditional branches.
- [Draft, Live, and version history](https://docs.unitlab.ai/documentation/ontologies/draft-live-and-version-history.md): Publish, inspect, restore, and compare ontology versions without silently mutating production history.
- [Test an ontology in Workbench](https://docs.unitlab.ai/documentation/ontologies/test-an-ontology-in-workbench.md): Attach or import the project ontology, make the intended copy Live, and exercise it in native annotation editors.
- [Workflows overview](https://docs.unitlab.ai/documentation/workflows/workflows-overview.md): Understand stages, routes, ownership, and valid actions.
- [Stages and routes](https://docs.unitlab.ai/documentation/workflows/stages-and-routes.md): Build the production path from project intake to completion.
- [Assignment and stage actions](https://docs.unitlab.ai/documentation/workflows/assignment-and-stage-actions.md): Control who can claim, start, save, submit, reject, or move work.
- [Review, rework, and escalation](https://docs.unitlab.ai/documentation/workflows/review-rework-and-escalation.md): Route quality decisions with explicit ownership and context.
- [Model stages](https://docs.unitlab.ai/documentation/workflows/model-stages.md): Integrate model inference as an observable, reviewable workflow responsibility.
- [Change a live workflow](https://docs.unitlab.ai/documentation/workflows/change-a-live-workflow.md): Update routing without orphaning or misrouting in-flight work.
- [Queues overview](https://docs.unitlab.ai/documentation/queues/queues-overview.md): Understand Task Queue, Batch Queue, assignment, priority, and observability.
- [Task Queue](https://docs.unitlab.ai/documentation/queues/task-queue.md): Assign, claim, prioritize, filter, and start individual work.
- [Batch Queue](https://docs.unitlab.ai/documentation/queues/batch-queue.md): Monitor grouped processing, status, failures, and recovery context.
- [Assignment and priority](https://docs.unitlab.ai/documentation/queues/assignment-and-priority.md): Choose a transparent allocation model for human work.
- [Queue failures and recovery](https://docs.unitlab.ai/documentation/queues/queue-failures-and-recovery.md): Diagnose missing actions, stalled work, invalid states, and partial processing.
- [Releases overview](https://docs.unitlab.ai/documentation/releases/releases-overview.md): Understand release content, provenance, formats, splits, and lifecycle.
- [Create a release](https://docs.unitlab.ai/documentation/releases/create-a-release.md): Freeze approved content with an explicit export contract.
- [Export formats and source inclusion](https://docs.unitlab.ai/documentation/releases/export-formats-and-source-inclusion.md): Choose a representation that preserves the required annotation contract.
- [Release splits and grouped data](https://docs.unitlab.ai/documentation/releases/release-splits-and-grouped-data.md): Create train, validation, and test membership without leakage.
- [Inspect, download, and validate](https://docs.unitlab.ai/documentation/releases/inspect-download-and-validate.md): Prove release integrity in the downstream consumer.
- [Collaboration overview](https://docs.unitlab.ai/documentation/collaboration/collaboration-overview.md): Understand how workspace and project responsibilities fit together.
- [Members and invitations](https://docs.unitlab.ai/documentation/collaboration/members-and-invitations.md): Onboard and remove people through an organization-owned identity process.
- [Role-based access](https://docs.unitlab.ai/documentation/collaboration/role-based-access.md): Use built-in roles to separate ownership, management, annotation, and review.
- [Permissions and custom roles](https://docs.unitlab.ai/documentation/collaboration/permissions-and-custom-roles.md): Configure permission groups for durable enterprise responsibilities.
- [Comments, Issues, and notifications](https://docs.unitlab.ai/documentation/collaboration/comments-issues-and-notifications.md): Preserve context, create ownership, and keep work moving.
- [Security overview](https://docs.unitlab.ai/documentation/security/security-overview.md): Apply least privilege and explicit ownership across the Unitlab operating model.
- [Authentication and account recovery](https://docs.unitlab.ai/documentation/security/authentication-and-account-recovery.md): Protect human accounts and recovery paths.
- [API keys and service identities](https://docs.unitlab.ai/documentation/security/api-keys-and-service-identities.md): Issue, store, rotate, and revoke automation credentials safely.
- [Cloud credential governance](https://docs.unitlab.ai/documentation/security/cloud-credential-governance.md): Control source-system identities, prefixes, rotation, and revocation.
- [Sensitive data and offboarding](https://docs.unitlab.ai/documentation/security/sensitive-data-and-offboarding.md): Remove access without orphaning regulated work or privileged dependencies.
- [Workspaces overview](https://docs.unitlab.ai/documentation/workspaces/workspaces-overview.md): Understand the top-level boundary for people, projects, data, models, and releases.
- [Create and switch workspaces](https://docs.unitlab.ai/documentation/workspaces/create-and-switch-workspaces.md): Establish the correct top-level boundary and avoid cross-workspace mistakes.
- [Workspace settings](https://docs.unitlab.ai/documentation/workspaces/workspace-settings.md): Manage shared configuration with impact awareness.
- [Storage and cloud settings](https://docs.unitlab.ai/documentation/workspaces/storage-and-cloud-settings.md): Govern reusable source connections and storage behavior at workspace scope.
- [Capacity, limits, and workspace operations](https://docs.unitlab.ai/documentation/workspaces/capacity-limits-and-workspace-operations.md): Monitor shared scale, queues, ownership, and periodic governance.

## API, SDK & CLI

- [API, SDK & CLI](https://docs.unitlab.ai/api-sdk-cli/api-sdk-and-cli.md): Build production Unitlab automations with the Python SDK, CLI, and authenticated HTTP API.
- [Developer overview](https://docs.unitlab.ai/api-sdk-cli/get-started/developer-overview.md): Choose the Python SDK, CLI, or authenticated HTTP interface for a Unitlab automation.
- [Install the Python SDK and CLI](https://docs.unitlab.ai/api-sdk-cli/get-started/install-the-python-sdk-and-cli.md): Install Unitlab 3.0.0 on Python 3.10 or newer and verify the SDK and CLI.
- [Authentication and configuration](https://docs.unitlab.ai/api-sdk-cli/get-started/authentication-and-configuration.md): Resolve API keys and the API base URL through explicit arguments, environment variables, or CLI configuration.
- [Python SDK quickstart](https://docs.unitlab.ai/api-sdk-cli/get-started/python-sdk-quickstart.md): Create a project, upload multimodal data, wait for processing, and inspect the resulting Data Units.
- [CLI quickstart](https://docs.unitlab.ai/api-sdk-cli/get-started/cli-quickstart.md): Configure the CLI, create a project, upload data, wait for processing, and emit JSON for automation.
- [UnitlabClient and resource namespaces](https://docs.unitlab.ai/api-sdk-cli/sdks/unitlabclient-and-resource-namespaces.md): Construct and close the client and navigate its public resource namespaces.
- [Projects](https://docs.unitlab.ai/api-sdk-cli/sdks/projects.md): List, create, retrieve, update, and soft-delete project resources.
- [Data Units](https://docs.unitlab.ai/api-sdk-cli/sdks/data-units.md): List and retrieve loose datasource or grouped project work units with stable filters.
- [Project uploads](https://docs.unitlab.ai/api-sdk-cli/sdks/project-uploads.md): Upload local multimodal files into one project Batch Queue and handle partial failure explicitly.
- [Batch Queues and processing](https://docs.unitlab.ai/api-sdk-cli/sdks/batch-queues-and-processing.md): Inspect, wait for, and diagnose asynchronous server processing.
- [Attach and detach project sources](https://docs.unitlab.ai/api-sdk-cli/sdks/attach-and-detach-project-sources.md): Preview source attachment, attach folders or dataset versions, and detach only after impact review.
- [Assets and custom metadata](https://docs.unitlab.ai/api-sdk-cli/sdks/assets-and-custom-metadata.md): Upload durable assets, select a folder, apply tags, and replace custom metadata intentionally.
- [Folders](https://docs.unitlab.ai/api-sdk-cli/sdks/folders.md): Create, traverse, and inspect local or cloud-backed folder resources.
- [Cloud storage](https://docs.unitlab.ai/api-sdk-cli/sdks/cloud-storage.md): List safe cloud-storage metadata, browse paginated entries, and import approved paths into a project.
- [Data Group automation](https://docs.unitlab.ai/api-sdk-cli/sdks/data-group-automation.md): Suggest, estimate, and create Data Groups from a folder with explicit grouping configuration.
- [Datasets](https://docs.unitlab.ai/api-sdk-cli/sdks/datasets.md): Create datasets from folders or assets and manage Unpublished changes.
- [Dataset versions](https://docs.unitlab.ai/api-sdk-cli/sdks/dataset-versions.md): Publish immutable dataset snapshots and inspect exact version membership.
- [Embedding spaces](https://docs.unitlab.ai/api-sdk-cli/sdks/embedding-spaces.md): Create a fixed-dimension embedding space and inspect readiness before loading vectors.
- [Upsert and search vectors](https://docs.unitlab.ai/api-sdk-cli/sdks/upsert-and-search-vectors.md): Upsert asset- or frame-level vectors in batches and run scoped nearest-neighbor search.
- [Build ontologies in Python](https://docs.unitlab.ai/api-sdk-cli/sdks/build-ontologies-in-python.md): Construct object classes, classifications, attributes, options, and nested conditional logic.
- [Ontology resources](https://docs.unitlab.ai/api-sdk-cli/sdks/ontology-resources.md): List, filter, retrieve, create, and save Workspace Ontologies.
- [Workflow stages and tasks](https://docs.unitlab.ai/api-sdk-cli/sdks/workflow-stages-and-tasks.md): Find a stage, enumerate its tasks, and perform role-appropriate task actions.
- [Bulk workflow operations](https://docs.unitlab.ai/api-sdk-cli/sdks/bulk-workflow-operations.md): Assign or move multiple tasks with explicit targets and dry-run validation.
- [Create and download releases](https://docs.unitlab.ai/api-sdk-cli/sdks/create-and-download-releases.md): Create an explicit project release and download annotations or represented source files.
- [Return types and raw payloads](https://docs.unitlab.ai/api-sdk-cli/sdks/return-types-and-raw-payloads.md): Use typed dataclasses for stable fields and raw payloads for diagnosis or forward-compatible access.
- [Exceptions, timeouts, and retries](https://docs.unitlab.ai/api-sdk-cli/sdks/exceptions-timeouts-and-retries.md): Handle authentication, validation, permission, subscription, not-found, network, request-timeout, and processing-timeout failures.
- [Pattern: Assets to a release](https://docs.unitlab.ai/api-sdk-cli/sdks/pattern-assets-to-a-release.md): Upload durable assets, publish a dataset version, attach it, wait for processing, and create a release.
- [Pattern: Cloud folder to project](https://docs.unitlab.ai/api-sdk-cli/sdks/pattern-cloud-folder-to-project.md): Browse an approved prefix, import it into a project, and monitor the created Batch Queue.
- [Pattern: Workflow worker](https://docs.unitlab.ai/api-sdk-cli/sdks/pattern-workflow-worker.md): Claim available tasks, perform bounded work, and submit through the configured stage edge.
- [Pattern: Custom embedding pipeline](https://docs.unitlab.ai/api-sdk-cli/sdks/pattern-custom-embedding-pipeline.md): Create a versioned vector space, load asset or frame embeddings, and run scoped search.
- [Automation observability and audit](https://docs.unitlab.ai/api-sdk-cli/sdks/automation-observability-and-audit.md): Record enough context to diagnose outcomes without logging secrets or sensitive source data.
- [CLI overview and output modes](https://docs.unitlab.ai/api-sdk-cli/clis/cli-overview-and-output-modes.md): Navigate the command tree and choose concise human output or JSON output.
- [CLI: projects and Data Units](https://docs.unitlab.ai/api-sdk-cli/clis/cli-projects-and-data-units.md): Create, update, inspect, delete, upload, import, attach, and inspect project Data Units and sources.
- [CLI: Batch Queues](https://docs.unitlab.ai/api-sdk-cli/clis/cli-batch-queues.md): List, inspect, wait for, and diagnose project Batch Queues.
- [CLI: Assets and folders](https://docs.unitlab.ai/api-sdk-cli/clis/cli-assets-and-folders.md): Upload assets, manage metadata, create and inspect folders, synchronize cloud folders, and create Data Groups.
- [CLI: datasets and versions](https://docs.unitlab.ai/api-sdk-cli/clis/cli-datasets-and-versions.md): Create datasets, add sources, publish versions, and inspect draft or versioned membership.
- [CLI: embedding spaces](https://docs.unitlab.ai/api-sdk-cli/clis/cli-embedding-spaces.md): Create spaces, load JSONL vectors, upsert individual vectors, and run scoped vector search.
- [CLI: workflow tasks](https://docs.unitlab.ai/api-sdk-cli/clis/cli-workflow-tasks.md): List stages and tasks, control ownership and priority, perform stage actions, and inspect history.
- [CLI: ontologies and cloud storage](https://docs.unitlab.ai/api-sdk-cli/clis/cli-ontologies-and-cloud-storage.md): Create or update ontology JSON, list compatible ontologies, and browse safe cloud metadata.
- [CLI: releases](https://docs.unitlab.ai/api-sdk-cli/clis/cli-releases.md): Create, inspect, list, and download versioned release outputs.
- [CLI automation and exit behavior](https://docs.unitlab.ai/api-sdk-cli/clis/cli-automation-and-exit-behavior.md): Build shell and CI jobs that parse JSON, preserve errors, and verify remote state before retrying.
- [API overview](https://docs.unitlab.ai/api-sdk-cli/apis/api-overview.md): Understand the public HTTP resource model, base URL, version boundary, and request lifecycle.
- [API authentication](https://docs.unitlab.ai/api-sdk-cli/apis/api-authentication.md): Create, store, send, rotate, and validate workspace API keys safely.
- [Projects API](https://docs.unitlab.ai/api-sdk-cli/apis/projects-api.md): Create, list, retrieve, update, and delete Projects with explicit state checks.
- [Project data and Batch Queues API](https://docs.unitlab.ai/api-sdk-cli/apis/project-data-and-batch-queues-api.md): List Data Units, upload files, attach governed sources, and monitor ingestion.
- [Assets and folders API](https://docs.unitlab.ai/api-sdk-cli/apis/assets-and-folders-api.md): Upload durable Assets, manage hierarchy and metadata, synchronize cloud folders, and create Data Groups.
- [Cloud storage API](https://docs.unitlab.ai/api-sdk-cli/apis/cloud-storage-api.md): List approved integrations, browse provider content, and import selected paths into a Project.
- [Datasets API](https://docs.unitlab.ai/api-sdk-cli/apis/datasets-api.md): Create reusable Datasets, manage draft membership, publish versions, and read exact snapshots.
- [Ontologies API](https://docs.unitlab.ai/api-sdk-cli/apis/ontologies-api.md): List, retrieve, create, and save reusable Ontology schemas with explicit structures and revisions.
- [Annotations and workflow API](https://docs.unitlab.ai/api-sdk-cli/apis/annotations-and-workflow-api.md): Operate annotation work through stages and tasks with ownership, actions, overrides, and history.
- [Releases API](https://docs.unitlab.ai/api-sdk-cli/apis/releases-api.md): Create versioned Project exports, inspect metadata, and request annotation or source downloads.
- [Embedding spaces API](https://docs.unitlab.ai/api-sdk-cli/apis/embedding-spaces-api.md): Create vector spaces, upsert asset or frame vectors, and run scoped similarity search.
- [Errors, pagination, and retries](https://docs.unitlab.ai/api-sdk-cli/apis/errors-pagination-and-retries.md): Implement deterministic errors, complete traversal, bounded polling, and state-aware retries.

## Release Notes

- [Release Notes](https://docs.unitlab.ai/release-notes/release-notes.md): Product updates across Unitlab's annotation, review, data, automation, and platform workflows.
- [2026 RELEASES](https://docs.unitlab.ai/release-notes/2026-releases.md): Unitlab product updates released through July 2026.
- [2025 RELEASES](https://docs.unitlab.ai/release-notes/2025-releases.md): Unitlab product updates released in 2025.
- [2024 RELEASES](https://docs.unitlab.ai/release-notes/2024-releases.md): Unitlab product updates released in 2024.
- [2023 RELEASES](https://docs.unitlab.ai/release-notes/2023-releases.md): Unitlab product updates released in 2023.
- [Unitlab Python SDK 3.0.0](https://docs.unitlab.ai/release-notes/unitlab-python-sdk-3.0.0.md): Released July 27, 2026 — Unitlab Python SDK and CLI 3.0.0 for current platform resources and production automation.
- [Enterprise documentation revision](https://docs.unitlab.ai/release-notes/enterprise-documentation-revision.md): Published July 29, 2026 — current-source enterprise documentation for the Unitlab platform and SDK.

## Solutions

- [Unitlab Solutions](https://docs.unitlab.ai/solutions/unitlab-solutions.md): Design enterprise Unitlab programs around decisions, context, controls, and reproducible delivery.
- [Manufacturing and mining](https://docs.unitlab.ai/solutions/industry-solutions-guides/manufacturing-and-mining.md): Design context-preserving inspection, PPE, equipment, defect, event, and operational-state programs.
- [Agriculture](https://docs.unitlab.ai/solutions/industry-solutions-guides/agriculture.md): Build multi-angle crop, fruit, livestock, and field datasets with consistent identity, count, condition, and temporal policy.
- [Medical AI](https://docs.unitlab.ai/solutions/industry-solutions-guides/medical-ai.md): Coordinate DICOM planes, 3D context, reports, ontology policy, and specialist review for medical data programs.
- [Autonomous systems](https://docs.unitlab.ai/solutions/industry-solutions-guides/autonomous-systems.md): Curate synchronized driving, robotics, and temporal scene data across cameras, motion, identities, and rare scenarios.
- [Multi-camera and multi-angle work](https://docs.unitlab.ai/solutions/multimodal-patterns-guides/multi-camera-and-multi-angle-work.md): Design synchronized Data Groups and Workbench layouts for factories, agriculture, retail, robotics, and spatial operations.
- [Document and audio intelligence](https://docs.unitlab.ai/solutions/multimodal-patterns-guides/document-and-audio-intelligence.md): Pair audio, transcripts, PDFs, entities, relations, regions, and structured context for customer, compliance, and research workflows.
- [Video, documents, and 3D context](https://docs.unitlab.ai/solutions/multimodal-patterns-guides/video-documents-and-3d-context.md): Combine temporal evidence with manuals, reports, medical or spatial context, and 3D representations.
- [Human–AI annotation operations](https://docs.unitlab.ai/solutions/program-design-guides/human-ai-annotation-operations.md): Combine model proposals, human judgment, correction, review, and feedback without losing accountability.
- [Quality governance playbook](https://docs.unitlab.ai/solutions/program-design-guides/quality-governance-playbook.md): Define ownership, policy, calibration, cohort inspection, correction, escalation, and evidence for a production data program.
- [Model-in-the-loop iteration](https://docs.unitlab.ai/solutions/program-design-guides/model-in-the-loop-iteration.md): Use embeddings, model proposals, issues, review outcomes, and releases to build targeted feedback cohorts.
- [Platform architecture](https://docs.unitlab.ai/solutions/architecture-and-adoption-guides/platform-architecture.md): Map Unitlab data, project, human, model, automation, quality, and delivery layers into an enterprise system.
- [Evaluation and rollout](https://docs.unitlab.ai/solutions/architecture-and-adoption-guides/evaluation-and-rollout.md): Move from a representative pilot to a controlled production data operation with explicit go/no-go evidence.
- [Solution planning checklist](https://docs.unitlab.ai/solutions/additional-resources-guides/solution-planning-checklist.md): Translate a new use case into explicit Unitlab decisions, owners, controls, acceptance evidence, and downstream validation.