Landing AI is an artificial intelligence (AI) company based in San Francisco and founded by Andrew Ng, which specializes in providing computer vision and Visual AI solutions for commercial use. Landing AI provides companies with an end-to-end solution to create, deploy and scale AI-based visual inspection and document processing capabilities. Landing AI targets large-scale enterprises looking to implement deployment-ready AI capabilities in areas such as automation and analytics.
Founded by Andrew Ng, co-founder of Coursera and former head of Google Brain, to provide Visual AI solutions.
Provided an end-to-end AI platform for industrial visual inspection.
Committed funds in its most recent funding round as part of three funding rounds, totaling $57 million.
Introduced the ADE platform for intelligent document processing and multi-modal document intelligence.
Cloud-based with edge deployment capabilities
Proprietary computer vision models with visual prompting, agentic document intelligence, and industrial inspection capabilities
Technologies from RocketReach; AI capabilities from official descriptions
To begin development, sign up at va.landing.ai, generate an API Key from your profile, and develop applications using either the Landing AI Python Libraries or REST Endpoints. Begin by developing applications utilizing Landing Lens for Model Training or ADE for Document Extraction. Full documentation provides example usage of both cURL and Python.
The Landing Lens APIs manage Cloud-Based Model Training, Image Upload, and Deployment via REST. Landing Edge manages Local Inference via HTTP Endpoints (ports 7000-8000) to be deployed to Devices.
ADE provides the following APIs: Parse (Document-to-Markdown), Split, and Extract. Parse Jobs API manages Large Files Asynchronously using Public URLs for Input/Output with Pre-Signed Cloud Storage URLs.
API Keys provide Access Control. ADE utilizes Pre-Signed URLs to avoid Storing Sensitive Data. No Credentials are Stored by Landing AI Services. Additional Security Controls are available to Enterprise Customers.
Primary Support for Python via Official Libraries (landingai-python, landingai_ade). TypeScript Library Available. REST APIs can be used with Any Language (cURL Examples Provided).
Free Tier Available for Agentic APIs. Contact Sales for Production Pricing and Enterprise Plans. Pricing Page Located at https://www.landing.ai/pricing-agentic-apis.
Yes, Landing Edge Web APIs allow for Local Inference on HTTP Endpoints. Supports Image File Uploads and Numpy Array Memory-Mapped Inference. Configure Ports 7000-8000 in Inspection Points.
Landing Edge Limited to Specific Ports and Local Deployment. ADE Requires Public URLs and Cloud Storage Integration. No Webhooks or Public Rate Limits/SLA Documented.
The LandingAI API is a collection of API's that can be used to create custom computer vision applications in an enterprise environment using both cloud-based models and edge-based deployment. It includes a robust set of python SDK's as well as extensive documentation which makes it very easy to develop against for manufacturing and document processing use cases. Most suited to enterprises that need to deploy vision AI at scale rather than general purpose API's.
LandingAI is most effective when deployed for industrial vision AI and when creating custom document extraction applications; however, requires technical knowledge and a specific application use case to derive maximum value.
LandingAI has a mature API that is focused on three areas: LandingLens (a cloud based CV platform), LandingEdge (an edge based inference solution) and Agentic Document Extraction (ADE). Has a strong set of python SDK's that are supported by extensive documentation. Focuses on providing enterprise level computer vision capabilities versus consumer level.
Good - detailed technical documentation from docs.landing.ai, GitHub repos, and product pages. Limited public info on pricing, rate limits, SLAs, and customer case studies.
LandingAI has active github presence including repositories for the landingai-python (LandingLens SDK) and landingai_ade libraries. LandingAI also maintains comprehensive documentation at docs.landing.ai which includes examples of how to write code against each of the LandingAI API's.
The ADE is a parser of complex documents that can parse to JSON and/or Markdown. ADE provides an asynchronous method called "Parse Jobs API" which parses large documents from public URL addresses. ADE was built for use in RAG search and LLM training pipelines.
Inference using configurable HTTP ports (7000-8000) to perform edge inference on images, numpy arrays, and memory mapped transfers. Provides an edge based solution ideal for industrial inspections without being dependent upon cloud access.
ADE generates API Keys through the va.landing.ai profile dashboard. Supports environment variables (.env file), direct header passing. Also includes a eu specific key endpoint.
Enabling Robots To Recognize & Respond To Objects Without Complex Programming
Identifying, Classifying, And Categorizing Defects In Quality Assurance Applications
Identifying Complex Patterns In Visual Data For Inspection And Analysis
Reducing Initial Training Time & Accelerating Deployment With Pre-Trained Models
Providing Intelligent Data Processing Pipelines To Efficiently Develop Models
Takes unstructured visual data from documents and images and transforms it into actionable information.
Offers simple to use tools so that you can deploy a Vision AI application quickly and easily without having to have expert level knowledge of AI.
Once deployed the user will be able to retrain the AI for new applications or scenarios as needed without needing any help.
| Application Type | Industries | Key Benefits | Specific Use Cases |
|---|---|---|---|
| Item Picking & Sorting | Logistics, Food & Beverage, Retail | Faster commissioning, greater flexibility, faster deployment | Autonomous item-picking, sorting operations |
| Depalletizing | Logistics, Manufacturing, Food Processing | Reduced labor dependency, increased throughput, dynamic environment adaptation | Mixed-load depalletizing, variable product handling |
| Quality Inspection | Manufacturing, Electronics, Automotive | Defect detection consistency, improved production yield, visual analysis | Weld analysis, paint analysis, PCB inspection, solder quality inspection |
| Precision Assembly Inspection | Electronics, Automotive, Medical Devices | Complex tolerance validation, defect identification, multi-region analysis | Wafer inspection, die inspection, lead frame validation, PCB build validation |
| Biotech & Pharmaceutical Inspection | Biotech, Pharmaceutical | Critical quality assurance, high-volume processing, reliability | Vial inspection, cell counting, cell sorting, microparticle detection |
| Healthcare & Food Applications | Healthcare, Food & Beverage | Automation previously thought impossible, high-accuracy validation | Volume measurement, diagnostics, food quality control |
| Integration Type | Target Robot Classes | Compatible Applications | Deployment Model |
|---|---|---|---|
| LandingLens Vision Module | All ABB collaborative and industrial robots | Item-picking, sorting, depalletizing, quality inspection | Embedded in ABB software suite |
| Pre-trained Vision Models | Standard 6-axis robots | Logistics, healthcare, food & beverage automation | Ready-to-deploy with customization options |
| Custom Vision Training | All robot types | Application-specific defect detection, object recognition | User-trained via LandingLens platform |
| End-Effector Vision Integration | Collaborative and cobot variants | Flexible manufacturing, dynamic environments | Integrated with RobotStudio digital twin |
Allows users to build a Vision AI model using drag and drop functionality which does not require any programming skills.
Provides embedded LandingLens capability directly in ABB’s native simulation and programming environment.
Users are able to test the virtual environment before physically deploying the robot.
Offers industry standard templates for Picking, Sorting, Depalletizing, and Inspection Workflows.
Allows integration via REST APIs, Python, and TypeScript to allow for customizations beyond what is available out of the box.
Allows both System Integrator and End-User to independently retrain the AI for new scenarios or applications after the initial deployment.
Allows modular extraction of structured data from visual documents and images via APIs
