Nemo
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About
Nemo is a specialized search and analytics platform designed to help autonomous vehicle developers manage the massive influx of sensor data. As companies working on L2+, L3, and L4 systems accumulate petabytes of driving logs, identifying the specific 5-10% of data that is actually useful for R&D becomes a significant bottleneck. Nemo addresses this by acting as a "Google Search" for sensor data, allowing users to query specific events and traffic scenarios across vast datasets without manual review. The platform is powered by a core scenario extraction engine and contextual scene-understanding AI. It processes raw data from Lidars, cameras, radars, and CAN buses to identify objects, road infrastructure, and complex traffic interactions like aggressive cut-ins or lane departures. Once identified, these scenarios can be exported directly for algorithm training, product testing, or simulation. Beyond search, it provides analytics such as heat maps and time-series plots to help teams understand scenario distribution and identify biases in their datasets. The tool is primarily built for engineering and product development teams within the automotive and robotics industries. Specifically, it caters to those building level 2 to level 4 autonomous systems who need to streamline their data pipeline. It is also highly beneficial for IT and operations managers looking to optimize data storage, as the platform's automated filtering and tiering can reduce "hot storage" costs by up to 85% by moving less relevant data to more affordable tiers. What distinguishes Nemo is its ability to translate raw sensor inputs into high-level semantic descriptions of driving behavior and environment. Unlike generic data management tools, it understands the nuances of road interactions—such as distinguishing a "rolling stop" from a "hard brake"—and the environmental context like fog or rain. This domain-specific intelligence, combined with flexible deployment options for cloud, on-premise, or hybrid environments, makes it a specialized solution for the rigorous security and compute requirements of the automotive sector.
Pros & Cons
Reduces data storage costs by up to 85% through automated tiering.
Supports data from multiple sources including Lidars, cameras, and CAN bus.
Allows for natural language-style queries for specific driving scenarios.
Flexible deployment across cloud, on-premise, and hybrid environments.
Provides detailed analytics on dataset biases and scenario coverage.
Pricing is not transparent and requires contacting sales for a quote.
Requires a private demo to access the full capabilities and sandbox.
Use Cases
Autonomous vehicle developers can quickly find specific corner cases, like aggressive cut-ins, to improve algorithm training and safety testing.
Data engineers can automate the filtering and tiering of petabytes of driving logs, significantly reducing the cost of hot storage.
Testing and validation teams can use coverage analytics to identify gaps in their testing datasets and ensure diverse scenario representation.
Platform
Features
• low compute requirements
• export for simulation and training
• time-series plots
• heat map analytics
• data filtering and tiering
• multi-sensor data scanning
• contextual scene understanding ai
• scenario extraction engine
FAQs
What types of sensor data can Nemo process?
Nemo scans a wide range of sensor inputs including Lidar, camera, and radar data, as well as information from the CAN bus. It uses this data to reconstruct scenes and identify specific objects, road infrastructure, and vehicle behaviors.
Can Nemo help reduce data storage costs?
Yes, the platform can reduce storage costs by up to 85% by automating the data tiering process. It identifies valuable R&D events so companies can keep critical data in expensive "hot" storage while moving the rest to more affordable tiers.
How can the extracted scenarios be used?
Shortlisted scenarios found through the search engine can be exported for various R&D tasks. This includes training machine learning algorithms, product testing, manual annotation, or running simulations.
What deployment options are available?
Nemo is designed to be flexible with IT infrastructure requirements. It can be deployed in the cloud, on-premise, or in hybrid environments, ensuring data security and compliance.
What kind of analytics does the platform provide?
The tool offers scenario distribution and coverage analytics through visual tools like heat maps, time-series plots, and frequency plots. These insights help developers identify biases and gaps in their training datasets.
Pricing Plans
Custom
Unknown Price• Private demo
• Custom deployment
• Full data security
• Scenario extraction
• Analytics dashboard
• Cloud or on-premise deployment
• Low compute requirements
• Micro-services support
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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