Problem

AI teams increasingly need to turn huge volumes of messy, unstructured multimodal data—video, images, audio, text, geospatial information, LiDAR, and other sensor streams—into reliable training and evaluation data. Traditional enterprise data infrastructure was not designed to curate, annotate, align, and manage this information at the speed or scale required by modern AI. The resulting pain is both operational and economic: teams waste labeling and compute resources on uninformative data, struggle to integrate human review into workflows, and lack visibility into why models fail.

The clearest use case is physical AI, including autonomous vehicles, drones, robotics, and industrial systems. These applications must find rare edge cases and distribution gaps across synchronized sensor data before those failures reach production. Encord’s curation process can reduce dataset size by 35%, lowering labeling and compute costs, while helping teams manage petabyte-scale data and improve training efficiency.

Product / Service

Encord provides an AI-native data infrastructure platform for managing the full data-to-model lifecycle. Teams can index and manage multimodal datasets, use embedding-based search and model-in-the-loop curation to identify relevant examples and rare edge cases, and annotate video, LiDAR, audio, text, images, and sensor-fusion data in unified workflows. Built-in label lineage and quality controls support production-scale operations.

The platform also connects data work to model development through model alignment and evaluation, including RLHF workflows, rubric-based evaluation, and pairwise comparison. When the system identifies where an AI model fails, those examples can be routed back into training. Encord says customers have achieved 60% faster model training and evaluation, while Google Cloud’s case study reports a more than 20% improvement in model performance from higher-quality curated datasets.

Market

Encord competes in the AI data infrastructure and AI data development platform market, spanning multimodal data management, data curation, data labeling, quality control, model evaluation, and alignment. Its positioning is particularly focused on frontier and physical AI teams that need a vendor-neutral data layer rather than a narrow labeling service. Comparable and alternative platforms include Scale AI, Labelbox, Snorkel, Dataloop, SuperAnnotate, Appen, V7 Labs, and Hive, although the degree of overlap varies by workflow and customer segment.

The company is commercial and has demonstrated substantial traction rather than being pre-revenue. Encord reports powering more than 300 AI teams and having raised $110 million in total funding, including a $60 million Series C led by Wellington Management in February 2026. Its platform grew from 1 petabyte to more than 5 petabytes in twelve months, while revenue from physical-AI customers grew tenfold; named customers and users include companies such as Woven by Toyota, Skydio, AXA Financial, UiPath, and Vantor.