About Us

The Data and Memory Layer for AI

We started with a question: how would AI teams deal with data that is no longer one type but connected and rich, without spending most of their time on data infrastructure?

The outcome of AI depends as much on the data fed into it as on the models used, yet most enterprise AI initiatives stall on data silos and infrastructure that cannot scale. ApertureDB breaks those silos. It is a unified graph-vector-multimodal database that brings vector search, a knowledge graph, and native storage for text, documents, images, video, audio, and blobs into a single production-grade engine. Teams can get ROI from AI without spending 6 to 9 months stitching it together.

ApertureDB and its AI workflows help teams start with a single workload and grow into production. Aperture Nexus extends that journey to memory, giving people and AI agents a place to commit what they learn, with context stamped on every commit, and retrieve it later. Hooks let the models and business-specific logic reason over it. Together they are built for one outcome: ultimately enabling AI cognition, on data and context that are connected from the start.

Our Team

Vishakha Gupta

CEO

With a Ph.D. in Computer Science from Georgia Tech and an M.S. from Carnegie Mellon University, Vishakha has spent her career at the intersection of systems, data, and AI — from heterogeneous multicore architectures to graph databases and multimodal data management. Her current work focuses on enabling organizations to build fast, reliable, and persistent multimodal intelligence.

Ali Asadpoor

CTO

Ali brings a hands-on approach to software architecture and engineering leadership, with a focus on clear design, practical problem-solving, and developer productivity. At ApertureData, he leads technology strategy and engineering, helping build the data infrastructure that powers multimodal AI applications.