Integrations are becoming the backbone of leading AI software. As AI products transition to daily utility, users expect them to connect seamlessly with third-party tools — whether that means automating tasks, retrieving data, or keeping records in sync. In this report, we analyze how 100 of the top AI SaaS companies are leveraging integrations — and uncover the four core use cases that define the market today.

Developers have been burned by existing integration platforms because what they need are not one off abstractions - they need an end-to-end integration infrastructure platform. In this article, we describe what an integration infrastructure platform is and the ways in which it solves problems differently than all existing solutions.

Our latest product, purpose-built for high volume data ingestion and access control for RAG.

As of today, Paragon is no longer an embedded iPaaS company. It is the integration infrastructure for B2B & AI products, with purpose-built products to solve every integration use case.

While MCPs simplify and standardize how AI agents interface with 3rd-party APIs, they don't support the full set of use cases necessary for native product integrations.

Different integration use cases require fundamentally different implementation considerations. We'll walk through the differences between synchronous and asynchronous use cases, and how Paragon provides solutions for both durable long-running tasks as well as responsive time-sensitive tasks.

Learn the fundamentals of AI agent tool calling and how it can enable your AI product to automate tasks across your users' workflows.

One API to give your AI agent 1000+ integration tools.

Everyone is racing to ingest data for RAG - but it isn't always optimal for accurate/efficient retrieval. Learn when you should leverage API-based query-time retrieval instead.

Building AI products? This comprehensive guide covers everything you need to know about retrieval augmented generation (RAG).