Guaranteed Task Completion Time
Enterprise-grade SLAs include task completion times and tasks can be rapidly scaled up and down to meet your requirements.
Scale RapidThe fastest way to production-quality labels.
Scale StudioLabeling infrastructure for your workforce.
Scale 3D Sensor FusionAdvanced annotations for LiDAR + RADAR data.
Scale ImageComprehensive annotations for images.
Scale VideoScalable annotations for video data.
Scale TextSophisticated annotations for text-based data.
Scale AudioAudio Annotation and Speech Annotation for NLP.
Scale MappingThe flexible solution to develop your own maps.
Scale NucleusThe mission control for your data
Scale ValidateCompare and understand your models
Scale LaunchShip and track your models in production
Scale Document AITemplate-free ML document processing
Scale Content UnderstandingManage content for better user experiences
Scale SyntheticGenerate synthetic data
The flexible solution to develop and scale your own custom maps.
Rapidly develop, easily update, and efficiently scale proprietary HD Maps. Improve simultaneous localization and mapping (SLAM) models to enhance autonomous driving systems and develop efficient navigation and routing.
Generate high-precision maps for simulation and real-world testing.
Enrich your stack with detailed understanding of semantic environments.
Improve perception systems by enabling the live detection of map features.
Enhance route planning by adding proprietary metadata to maps.
Build better models faster with data you can trust. Maximize operational efficiency and productivity while reducing the cost of ML projects.
Own your own maps. Add new regions when you need without going through time-consuming processes with vendors, and layer on company-specific metadata (e.g. drop box locations) to develop proprietary maps.
Submit LiDAR with or without image data (including satellite imagery) for annotation. Pre-labeled map titles or existing maps are also supported for fast and seamless validation of and updates to existing maps.
High-precision annotation of road features, lane directions, traffic signs, and more. Establish relationships between objects (e.g. traffic signs to lanes) with object linking to enhance navigation and motion planning models.
Maximize map quality without sacrificing speed. With tiling and stitching, map sections are split into tiles to enhance labeling quality and efficiency and stitched back together to create a comprehensive map.
Ensure maps never go out of date. Changes to map tiles (e.g. new stop signs) can be quickly detected and validated using a variety of inputs and Scale’s dynamic workforce. Updated map tiles seamless fit into existing maps.
Have confidence in the quality of annotated maps. Quality assurance systems built into the product monitor and prevent errors. Confidence scores trigger varying levels and types of human review.
Get started today with on-demand, or chat with us about an enterprise plan.
Enterprise-grade SLAs include task completion times and tasks can be rapidly scaled up and down to meet your requirements.
Each enterprise customer is paired with a dedicated engagement manager who will ensure smooth on-boarding and continued data delivery.
Enterprise engagements provide upfront and volume-based discounts, and is the most cost-effective solution for high-quality labels. Plus with Scale AI, there are no platform fees.
Industry leading quality datasets safe-guarded against ever-changing, messy data.
Mapping tasks submitted to the platform are first broken down into tiles to maximize labeling efficiency. Areas with insufficient data are marked as no data zones before the base vector annotations are added within the annotatable region. Semantic annotations are then layered on before relationships between annotations are established with linking. Once vector, semantic, and linking annotations are complete, tasks are stitched back together to create a comprehensive map.
A more cost effective approach to HD map labeling, this task workflow guarantees industry-leading quality at speed.
Scale Mapping is trusted by leading machine learning teams to develop more accurate models.