Startups > ApertureData

ApertureData

ApertureDB unifies multimodal data, knowledge graphs, and vector search for AI teams.

Seed $16.1M total Founded 2018 Los Gatos, California 10 employees

ApertureDB is a unified database that natively stores and manages images, videos, documents, embeddings, and metadata in a single system with built-in knowledge graph and vector search capabilities. It eliminates the need for enterprises to manually integrate cloud storage, vector databases, and processing libraries by providing a single interface for multimodal AI workloads. The platform is 35x faster at mobilizing multimodal datasets than traditional approaches and 2-4x faster than open-source vector databases, reducing infrastructure setup time by 6-12 months.

Problem solved

Enterprises must manually integrate disparate systems (cloud storage, vector databases, metadata stores, processing libraries) to manage multimodal data for AI pipelines, consuming 6-12 months of infrastructure setup time.

Target customer

CTOs and CDOs at enterprises and AI startups building computer vision, robotics, and generative AI applications that require multimodal data management at scale.

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Founders

Vishakha Gupta

Co-founder & CEO

7+ years at Intel Labs designing systems technologies; PhD in Computer Science from Georgia Tech; led development of VDMS which became ApertureDB's core architecture.

Luis Remis

Co-founder & CTO

10+ years experience at Intel Labs with Vishakha Gupta; worked on visual data management challenges starting in 2016.

Funding history

Seed Round 1 $3M February 2022 Led by Root Ventures · Work-Bench, 2048 VC, Graph Ventures, Alumni Ventures Group, Magic Fund, industry angels

Series A / Additional Round Unknown April 2023 Led by Unknown · Gaingels, Work-Bench, Root Ventures, TQ Ventures, Trajectory Ventures

Seed VC - II $8.25M October 2024 Led by TQ Ventures · Westwave Capital, Interwoven Ventures, high-caliber angel investors

Total raised: $16.1M

Industries

Artificial Intelligence (AI) Analytics Big Data Information Technology Machine Learning Software

Pricing

Not publicly available. 30-day risk-free trial offered for ApertureDB Cloud.

Notable customers

Badger Technologies, Addy.ai, major home furnishings retailer, leading manufacturer, biotech and generative AI startups

Tech stack

jQuery (JavaScript libraries) core-js (JavaScript libraries) Open Graph LottieFiles HTTP/3 Google Analytics (Analytics) HSTS (Security) Google Font API (Font scripts) Google Workspace (Email) Google Hosted Libraries (CDN) Cloudflare (CDN) Webflow (Page builders) Amazon Web Services (PaaS)

Website

aperturedata.io/

Competitors

Pinecone

Vector database focused on semantic similarity; lacks native multimodal storage and knowledge graph capabilities.

Weaviate

Vector database that requires external integration for multimodal data and lacks unified metadata management.

Neo4j

Powerful for graph data but requires external vector databases for hybrid multimodal AI use cases.

Milvus

Open-source vector database optimized for semantic search; does not natively handle multimodal data or knowledge graphs.

MongoDB

General-purpose document database; lacks specialized vector search and multimodal data handling capabilities.

Why this matters: ApertureDB addresses a critical pain point in enterprise AI: the fragmentation of multimodal data infrastructure. By combining the speed of specialized vector search with native multimodal storage and knowledge graphs, the company positions itself to become the de facto data layer for vision and generative AI workloads, similar to how Pinecone dominates pure vector search. The $16.1M funding and strong customer traction (Badger's 2.5x performance gain) suggest real product-market fit in a rapidly growing category.

Best for: Enterprises and AI-native companies building computer vision, robotics, or generative AI applications that need to rapidly unify and search multimodal datasets without months of infrastructure engineering.

Use cases

Robotics Automation & Computer Vision

Badger Technologies uses ApertureDB to manage visual data from multi-camera robot systems. The platform increased vector similarity search performance by 2.5x (from 4K to 10K+ queries per second with stability), enabling real-time decision-making for retail automation robots.

Generative AI & RAG Applications

AI startups use ApertureDB to manage embeddings alongside rich metadata for retrieval-augmented generation. The unified GraphRAG support and unlimited metadata per record eliminate the need to maintain separate vector and relational databases.

Multimodal Data Consolidation

Enterprises consolidate images, videos, documents, and embeddings in a single system with native search. This reduces data integration complexity from requiring 5+ systems (cloud storage, vector DB, relational DB, processing libraries, visualization tools) to one unified platform.

Alternatives

Pinecone Choose Pinecone if you only need fast vector similarity search for text embeddings without multimodal data or knowledge graph requirements.

Neo4j Choose Neo4j if your primary need is powerful graph querying but be prepared to integrate external vector and storage systems for multimodal AI.

MongoDB + Vector Search Choose MongoDB with vector extensions if you prioritize document flexibility over specialized multimodal optimization and can tolerate slower vector performance.

FAQ

What does ApertureDB do? +

ApertureDB is a unified database that natively stores images, videos, documents, embeddings, and metadata while providing built-in knowledge graph and vector search capabilities. It eliminates the need for enterprises to manually integrate multiple systems (cloud storage, vector databases, metadata stores) for multimodal AI workloads. The platform is 35x faster than traditional approaches and reduces infrastructure setup time from 6-12 months to weeks.

How much does ApertureDB cost? +

Pricing is not publicly available. ApertureDB offers a 30-day risk-free trial for ApertureDB Cloud. Contact their sales team for custom enterprise pricing based on usage and deployment scale.

What are alternatives to ApertureDB? +

Key alternatives include Pinecone (vector-only search), Weaviate (vector database with graph support), Neo4j (graph database requiring external integrations), Milvus (open-source vector database), and traditional combinations of MongoDB + cloud storage + vector databases. Each trades off some combination of speed, multimodal support, and ease of integration.

Who uses ApertureDB? +

Target customers are CTOs and CDOs at enterprises and AI startups building computer vision, robotics, and generative AI applications. Known customers include Badger Technologies (robotics automation), Addy.ai (AI platform), and companies in home furnishings, manufacturing, and biotech.

How does ApertureDB compare to Neo4j? +

While Neo4j excels at graph querying, it requires external integration with vector databases for multimodal AI use cases. ApertureDB natively combines knowledge graphs, multimodal storage, and vector search in one system optimized for AI workloads. ApertureDB is also significantly faster (2-4x vs open-source solutions) for vector-heavy queries.

What makes ApertureDB different from vector databases like Pinecone? +

Vector databases like Pinecone focus purely on semantic similarity search for embeddings. ApertureDB natively handles images, videos, documents, and embeddings alongside rich metadata and knowledge graphs. This unified approach eliminates multi-system complexity and provides 35x faster data mobilization for multimodal AI pipelines.