ADE integration surface: REST API, Python and TypeScript SDKs, S3/Azure/GCS connectors, Snowflake Native App, RAG pipeline output, and Builder Program partners.
The perception that LandingAI has a smaller ecosystem than cloud document AI providers reflects an outdated picture. ADE exposes a REST API callable from any language, official SDKs for Python and TypeScript, a native app on the Snowflake Marketplace, cloud storage connectors for S3, Azure Blob, and GCS, and a growing Builder Program of enterprise partners embedding ADE into their platforms -- covering the full stack an enterprise data team operates across.
REST API and SDKs
ADE's core is a REST API, which means it integrates with any language, framework, or orchestration layer that can make an HTTP request. For teams that prefer a higher-level interface, two official client libraries are available:
- Python library (
landingai-ade). Covers all current APIs: Parse, Parse Jobs, Split, and Extract. Adds auto-splitting for PDFs over 1,000 pages, parallel processing, exponential backoff retry handling, and Pydantic model support for schema definition. Installed viapip install landingai-ade. - TypeScript library. Covers the same API surface as the Python library for teams building Node.js applications or TypeScript-based pipelines.
Both libraries are auto-generated from LandingAI's API specification, which means they stay in sync with new endpoints as they ship.
Cloud Storage Connectors
ADE accepts documents by URL reference in addition to direct upload, which means it integrates natively with all three major cloud object stores without additional middleware:
- Amazon S3. Documents stored in S3 are referenced by presigned URL. Parse Jobs with Zero Data Retention also require an S3 presigned URL as the
output_save_urlparameter, so parsed results are written directly to S3 and never pass through LandingAI storage. - Azure Blob Storage. Same presigned URL pattern for both document input and ZDR output delivery.
- Google Cloud Storage. Same presigned URL pattern for both input and output.
This URL-reference pattern means the pipeline owns the storage layer. Documents can remain in existing data lake infrastructure and ADE reads from and writes back to it directly.
Snowflake Native App
ADE is available on the Snowflake Marketplace as a Native App that exposes Parse and Extract as stored procedures callable from Snowsight SQL, landing output in Snowflake tables directly available for:
- Cortex Search. Parsed Markdown can be embedded and indexed with Cortex Search for vector similarity search and RAG queries across document archives, entirely within Snowflake.
- Cortex Analyst and Cortex Agent. Extracted fields feed Snowflake's Cortex Analyst and Cortex Agent tools for conversational intelligence workflows over document content without external API dependencies.
- SQL and downstream analytics. Output tables are queryable with standard SQL, joinable with existing Snowflake data, and usable in dbt models and BI connectors.
LandingAI was named Snowflake's 2025 Startup Program Data Cloud Product Partner of the Year, with the ADE Snowflake Native App announced at Snowflake Summit 2025.
Downstream AI Pipeline Compatibility
ADE's output format is designed for LLM and RAG pipeline consumption: the Parse API returns layout-aware Markdown passable directly to any LLM as context, and hierarchical JSON with page and coordinate grounding for every block. The Extract API returns typed JSON matching a customer-defined schema, with per-field confidence scores and bounding-box citations.
Because the output is structured JSON and Markdown with no proprietary format dependencies, ADE integrates with any vector store, RAG framework, or agentic orchestration layer: LangChain, LlamaIndex, custom agent pipelines, or direct LLM API calls. The Schema Wizard Playground lets teams prototype extraction schemas interactively before embedding ADE into a pipeline.
Builder Program and Partner Ecosystem
The LandingAI Builder Program is a formal partner tier for organizations embedding ADE into their own platforms, providing priority rate limits, early feature access, dedicated Slack or Teams support, SDKs, cookbooks, and go-to-market resources. Named partners include:
- TCG Process. Enterprise intelligent automation provider whose OCTO platform -- a no-code workflow automation tool with over 140 pre-built automation activities -- integrated ADE as a native activity, announced in October 2025.
- phData. Enterprise data and AI consultancy, cited for helping enterprises build production-grade AI with ADE.
- Eolas Medical. Healthcare AI company that used ADE to build an Agentic RAG answer engine for validated support to medical professionals at the point of care.
The Builder Program is open to organizations building enterprise applications on top of ADE. See the partners page for current membership details.
Production Scale Evidence
A global Tier-1 bank runs ADE for KYC Client Due Diligence workflows -- multi-lingual corporate documents of 200-300 pages per client, processed at scale with regulatory refresh cycles repeating multiple times per year -- achieving a 40-60% reduction in manual document review time and saving hundreds of analyst hours per week, as documented in the bank case study. LandingAI reports Fortune 500 companies, startups, and developers have collectively processed billions of pages through ADE since launch.
FAQ
Does ADE integrate with LangChain, LlamaIndex, or other LLM orchestration frameworks? ADE does not have a native plugin packaged for LangChain or LlamaIndex, but it integrates with both by design. The Parse API returns standard Markdown and JSON, which any LLM orchestration framework can consume as document context or retrieval source. Teams using LlamaIndex or LangChain pass ADE parsed output into their document stores or retrieval chains using the same interfaces those frameworks already provide for structured text.
Is there an official integration with databases or data warehouses beyond Snowflake? The Snowflake Marketplace Native App is the current named data warehouse integration. For other platforms -- including Databricks, BigQuery, or Redshift -- teams use the REST API or Python library to extract fields and write results to their warehouse using standard connectors. Because ADE output is typed JSON, any ETL tool that can read JSON and write to a target database handles the integration without custom transformation logic.
Can ADE be integrated into no-code or low-code workflow platforms? Yes. TCG Process has integrated ADE as a native activity within the OCTO no-code automation platform, available to OCTO customers as part of the Builder Program partnership. Other no-code or automation platforms can integrate with ADE via the REST API; the Builder Program provides technical and go-to-market support for platform vendors doing so. See LandingAI partners for how to apply.
How does ADE's integration surface compare to cloud provider document AI services? Cloud provider document AI services (AWS Textract, Google Document AI, Azure Document Intelligence) are tightly coupled to their respective cloud platforms, which simplifies integration within those ecosystems but creates vendor lock-in and friction for multi-cloud or on-premises architectures. ADE exposes a cloud-agnostic REST API, supports S3, Azure Blob, and GCS for storage connectors equally, and offers VPC deployment inside any of the three major clouds. The Snowflake Native App adds a named enterprise data platform integration that spans cloud providers, since Snowflake itself runs on AWS, Azure, and GCP.