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Beyond SQL: The Query Language Multimodal AI Really Needs

ApertureDB has its own query language, AQL, using JSON as its native format, because traditional languages like SQL, Cypher, were insufficient for managing, searching, and processing multimodal AI data at scale. AQL allows for expressing complex data types and operations, and ApertureDB also offers simplified interfaces, including SQL and SPARQL wrappers, and natural language access through RAG and MCP. The core idea is to prioritize the problem's solution over existing language barriers, aligning with the evolving needs of AI systems.

Blogs

September 17, 2025

Blogs

August 28, 2025

Smart AI Agents In the Wild

The future of AI isn’t bigger models—it’s smarter agents. Discover how real-world teams are using ApertureDB to power agents that see, reason, and act."

Blogs

September 2, 2025

ApertureDB and AI Workflows: Building Blocks of Multimodal AI Applications

ApertureDB AI Workflows are designed to simplify the creation of multimodal AI applications by providing modular, flexible, and purpose-built components for AI pipelines. These workflows automate common AI/ML tasks such as data ingestion, search, and data correlation, integrating with ApertureDB's graph, vector, and multimodal capabilities, and partnering with models and services from other tools.

ApertureDB and AI Workflows: Building Blocks of Multimodal AI Applications

Blogs

June 12, 2025

Automating Knowledge Graph Creation with Gemini and ApertureDB - Part 1

This blog shows how to build a knowledge graph using ApertureDB and Gemini 2.5 Flash to power smarter RAG systems. Part 1 covers extracting and storing entities, enabling real-world use cases like semantic search and AI-powered customer support.