SQLv2: The Open Standard for AI-Native Databases
Unifying SQL, machine learning, vector search, and generative AI into one query language for the AI era.
The Problem
The modern data stack is broken. AI teams juggle 5–7 different systems: PostgreSQL for transactions, Pinecone for vectors, Snowflake for analytics, and external APIs for inference. This fragmentation creates complexity, latency, and skyrocketing costs.
The Solution: SQLv2
SQLv2 reimagines SQL for the AI era, bringing AI-native capabilities directly into the language:
Native ML Inference: Run models inside the database.
Built-In Vector Search: No separate vector database required.
Inline Generative AI: Summarize, classify, and generate text in one query.
Multimedia Data Types: Query images, aud…
SQLv2: The Open Standard for AI-Native Databases
Unifying SQL, machine learning, vector search, and generative AI into one query language for the AI era.
The Problem
The modern data stack is broken. AI teams juggle 5–7 different systems: PostgreSQL for transactions, Pinecone for vectors, Snowflake for analytics, and external APIs for inference. This fragmentation creates complexity, latency, and skyrocketing costs.
The Solution: SQLv2
SQLv2 reimagines SQL for the AI era, bringing AI-native capabilities directly into the language:
Native ML Inference: Run models inside the database.
Built-In Vector Search: No separate vector database required.
Inline Generative AI: Summarize, classify, and generate text in one query.
Multimedia Data Types: Query images, audio, and documents next to structured data.
-- Before SQLv2: 3 systems, ETL pipelines, 500 lines of Python
-- After SQLv2: 1 query, 1 engine
SELECT
customer_id,
PREDICT('churn_model', customer_features) AS churn_risk,
GENERATE_TEXT('Offer for', segment) AS personalized_offer
FROM customers
WHERE embedding <=> EMBED('high-value customer behavior') > 0.85
AND last_purchase < CURRENT_DATE - INTERVAL '30 days';
Why Now?
Production-Ready Inference
In-engine inference is production-ready with predictable latency.
Hardware Acceleration
GPUs and CPUs with vector instructions are now ubiquitous.
Mature Vector Indexing
HNSW, IVF, and PQ algorithms are mature, fast, and reliable.
Security & Compliance
Keeping data and inference in the same place is now a security imperative.
ANSI SQL Compatible
SQLv2 is fully backward-compatible with ANSI SQL:2016. Your existing SQL queries will run unchanged, while new extensions unlock AI-native workloads.
Existing SQL+AI Extensions
Primary Use Cases
E-commerce
Real-time personalization with unified customer data.
Healthcare
Patient similarity search + clinical NLP, data stays in place.
Financial Services
Fraud detection combining transactions + embeddings.
Media
Personalized recommendations from text, images, and video.
Defense
Real-time threat analysis and detection.
An Open Standard for All
Released under Creative Commons Attribution 4.0 and maintained publicly. We invite the community to submit proposals, extensions, and feedback.
Get Involved
Read the Spec
Dive into the full specification on our public GitHub repository.
Join the Community
Sign up for our mailing list to get updates on the standard and community channels.
Try It First
Be among the first to build on a production-grade SQLv2 database.
About SQLv2
SQLv2 is an open standard created by Luis B. Mata, designed to unify AI and SQL into a single language. It’s released under Creative Commons and maintained publicly at github.com/synapcores/sqlv2.
About SynapCores
SynapCores is the first AI-native database implementing SQLv2. Built from the ground up to merge structured data and AI in one engine, SynapCores simplifies infrastructure, reduces costs, and unlocks new possibilities for developers.