Unlock the full potential of your data

Automatically find, group, and take action on data of interest:

1. Use a vector search, such as a natural language or similarity search, to surface specific data

2. Reduce labeling time and spend by taking bulk action on these data rows — add metadata or bulk classify data rows and send them as pre-labels for human-in-the-loop review 

3. Create a data curation pipeline to automatically surface and classify high-impact data based on specific parameters

A single place to visualize, explore, and search for data:

1. Securely integrate Labelbox with an existing data store

2. Combine the best of vector search with traditional search capabilities

3. Launch downstream ML applications to improve model performance, send data to a labeling project, and more

Surface high-impact data in your ever-growing data store and extract valuable insights:

1. Leverage intuitive filters to instantly pinpoint relevant data containing specific attributes 

2. Create targeted data slices to unearth high-value examples, reveal trends, and analyze model performance on a subset of data 

3. Combine semantic and vector searches to find similar data points within seconds

Better visualize and contextualize data:

1. Apply leading foundation models to automatically classify and enrich your data

2. Enhance data with custom or pre-computed embeddings

3. Add contextual data with custom metadata

4. View ground truth labels

5. Assess model performance with custom or off-the-shelf predictions

Transform your labeling operations with time and cost saving automation: 

1. Cut labeling costs by 65+ by importing pre-labels 

2. Accelerate workflows with model predictions from Labelbox’s Model Foundry or your own custom models

3. Create quality annotations in a fraction of the time with auto-annotation tools

Improve model performance by sending high-impact data for labeling:

1. Utilize few-shot classification to pre-label data and send it to a labeling project for time and cost effective review 

2. Queue a batch of data rows from Catalog and send it to a labeling project in Annotate

3. Prioritize the order of batches and expedite a high-value batch of data to the front of the labeling queue

Turn your data into actionable insights for AI:

1. Use active learning workflows to identify and annotate your most valuable data points

2. Create high-quality training data for AI applications across computer vision, large language models, generative AI, and more

3. Label data with an internal workforce or securely orchestrate labeling projects across external labeling teams

4. Loop in SMEs and access world class on-demand labeling support from Boost

Improve quality and efficiency with custom workflows:

1. Tailor review steps and route specific tasks to key stakeholders

2. Create ad-hoc review tasks as project needs evolve

3. Automatically send incorrect labels to re-work

Leverage the complementary strengths of human evaluation and AI feedback:

1. Generate perfect data with internal experts and a team of skillful labelers with expertise in RLHF, evaluation and red teaming

2. Ensure helpful, trustworthy, safe outputs with highly accurate datasets for instruction tuning, RLHF, and supervised fine-tuning

3. Balance AI-generated feedback at scale with human review to efficiently scale model performance while maintaining quality

Train a model 3x faster in the same environment where data is stored:

1. Configure all essential model parameters with test, validation, and training data splits

2. Reproduce model results by restoring previous data splits without third party tools or notebooks

3. Connect to cloud service provider environments to easily launch model training from Labelbox

Get full transparency with labeler and project level analytics:

1. A single source of truth for a project's labeler and annotation analytics

2. Gain access to detailed reporting on labeling throughput, efficiency, and quality

3. Rather than flying blind, drill into specific metrics such as review and rework time, total time spent, and more

Data quality issues can undermine your model's performance:

1. Use your model as a guide in an active learning workflow to find and fix labeling mistakes, unlabeled data classes, and poorly crafted data splits

2. Find all instances where model predictions and ground truth disagree

3. Visually compare ground truth and model predictions to surface mispredictions or labeling mistakes

Diagnose model errors and debug them:

1. Leverage auto-metrics to surface mispredictions or mislabeled data

2. Analyze the distribution of annotations and predictions

3. Filter on specific metrics such as IOU or confidence score to drill into model errors and find specific examples

Ensure helpful, trustworthy and accurate outputs with human-in-the-loop validation: 

1. Rich capabilities for benchmarking, ranking, selection, NER, and classification

2. Easily compare and A/B test outcomes across models & prompts

3. Confidently select the model that performs the best on your data and fine-tune it to quickly improve and scale model performance

Understand how a model is improving:

1. Collaboratively version and compare training data and hyperparameters across iterations in a single place

2. Visually compare and view comparison metrics between two experiments

3. Measure and understand the value in each iteration

Unlock gains at every iteration:

1. Surface candidate labeling mistakes and send them to a labeling project to be corrected

2. Curate high-impact unlabeled data to boost model performance

3. Identify data on which the model is underperforming and find similar unlabeled data in Catalog to send into a labeling project