SynapCores offers an AI‑native database that integrates machine‑learning primitives—such as embedding generation, classification, and generative AI—as native SQL functions, eliminating the need for external API calls and data movement. The platform provides high‑throughput vector indexing, sub‑millisecond inference, and unified handling of text, vectors, time‑series, and relational data, enabling low‑latency, scalable AI workloads within existing enterprise data pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often need to combine traditional relational databases with separate vector stores and external AI services to run embeddings, predictions, or generative models. This fragmented architecture forces data movement, introduces latency, and drives up infrastructure costs while requiring custom integration work.
Solution
SynapCores delivers an AI‑native database that integrates machine‑learning primitives directly into the SQL engine. By exposing embedding generation, inference, and generative AI as built‑in SQL functions, developers can execute AI workloads without calling external APIs or moving data between systems. The platform provides high‑throughput vector indexing and sub‑millisecond inference, enabling real‑time semantic search and streaming analytics on text, vectors, time‑series, and structured records. Because all operations run inside a single database, organizations reduce latency, simplify architecture, and lower operational expenses while retaining familiar SQL tooling and security controls.
Target Audience
The primary customers are data engineering and AI/ML teams at large enterprises—such as fintech, e‑commerce, and SaaS providers—that require scalable, low‑latency AI workloads integrated with their existing relational data stores.
Features
- Native AI functions (embeddings, classification, generation) callable from standard SQL queries, eliminating external API calls
- High‑performance vector indexing with advanced ANN algorithms supporting billion‑scale semantic search
- Real‑time stream processing with millisecond‑level inference for fraud detection, personalization, and anomaly monitoring
- Multi‑modal data model that unifies text, dense vectors, time‑series, and relational data in a single schema
- Sub‑millisecond query latency through in‑database GPU/accelerator execution paths
- Zero‑integration deployment: existing ETL pipelines and BI tools connect unchanged via standard database drivers
- Built‑in role‑based access control and encryption to meet enterprise security and compliance requirements