The ability to upload, analyze, and search across documents is an increasingly central feature in the evolution of AI research assistants, and Perplexity AI’s approach reveals a layered, multifaceted system designed to meet the needs of everyday users, knowledge-driven enterprises, and developers building programmatic document workflows. Rather than offering a monolithic solution, Perplexity has architected its file and document support to operate across distinct layers—rangi

Perplexity AI has emerged as a leading provider of web-grounded, research-focused AI solutions, offering developers robust programmatic access to its real-time knowledge engine through a comprehensive suite of APIs. Unlike generic large language model APIs that rely exclusively on static training data, Perplexity’s platform is fundamentally designed to search the internet, ingest user documents, and synthesize answers that are grounded in up-to-date evidence with visible cita

Gemini, Google’s generative AI platform, has steadily broadened its support for file uploads, enabling users to supply documents, media, code, and datasets as part of conversational, analytical, and developer-driven interactions. The practical realities of what Gemini will accept—and how those files are interpreted, constrained, and integrated into the reasoning workflow—are determined by a combination of product surface, subscription plan, and technical implementation, makin