Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The open-source bridge for executing AI models directly within your SQL database workflow.
AIDB is a high-performance middleware designed to bridge the gap between large language models (LLMs) and relational databases. As we move into 2026, AIDB has established itself as the premier solution for 'AI-resident data' workflows, allowing engineers to execute complex inference tasks directly within SQL environments. Unlike traditional decoupled systems that require extensive ETL processes to move data to an AI model, AIDB optimizes query execution by pushing AI tasks down to the data layer. Its architecture includes an advanced AI Query Optimizer that handles model batching, result caching, and cost-aware execution plans. This ensures that enterprise data remains secure and latency is minimized. By 2026, it supports a hybrid deployment model, allowing for seamless switching between local open-weights models (via vLLM) and hosted frontier models like GPT-5 or Claude 4. It is particularly effective for real-time sentiment analysis, automated data cleansing, and semantic search over structured legacy databases without requiring a complete migration to vector-only stores.
Analyzes SQL queries to determine the most cost-efficient way to batch AI model calls.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Caches AI model outputs based on semantic similarity of inputs rather than exact string matches.
Allows a single SQL query to utilize multiple models (e.g., GPT-4 for logic, Llama-3 for extraction).
Automatically injects database schema context into LLM prompts for better SQL generation.
Includes a PII masking layer that redacts sensitive data before it reaches external AI APIs.
Seamlessly joins vector search results with standard relational table data.
Supports real-time streaming of AI-generated content into database views.
Analyzing thousands of product reviews stored in SQL to identify trending issues.
Registry Updated:2/7/2026
Inconsistent job titles and company names across millions of records.
Non-technical managers needing complex data without writing SQL.