AI companies are aggressively investing in building integrations for their AI products, so they can ingest knowledge from their users' 3rd party apps and files, and give their AI agents access to automate tasks across the stack. This article dives into the use cases and why there is such strong sense of urgency right now.

Resources are re-usable API connections for your own internal services

3rd party webhooks are critical for enabling real-time integration use case. But because there are no standards, they can be very hard to build and maintain - we cover all the challenges to watch out for in this article.

Build custom triggers on top of any 3rd party webhook API in Paragon, for any integration.

Most AI SaaS companies today are implementing RAG with vector databases, but knowledge graphs are starting to pick up steam as the importance of retrieval accuracy becomes more apparent. We'll compare both options to help you better pick the right approach to use.

How do you reconcile permissions in a vector database within a RAG system when each 3rd party data source has their own permissions system? We explore the various approaches to implementing RAG with permissions in a multi-tenant environment.

Context is critical for LLMs, but it lives in unstructured data that's scattered across hundreds of your users' files and apps. But extracting that valuable context comes with many challenges, which we cover in this article.

Sharing some of the challenges that we had to overcome when building Paragon's workflow engine in order to support our customers' enterprise scale ingestion jobs.

Hitting 3rd party API rate limit errors can lead to data loss and integration outages for your users. But with Smart Rate Limits, this will no longer be an issue

Building a useful AI SaaS product requires your models to have access to your users' external data. But should you fine-tune or use retrieval augmented generation (RAG)? This article compares the two approaches.