How AI‑First Lakehouse Redefines Data Platforms for Multimodal Analytics

Infrastructure Evolution: Unified Storage for Multimodal Data

Traditional OLAP engines have been optimized for structured data (e.g., Hive, OLAP, Lakehouse with Parquet/ORC). With large‑language models (LLM) and multimodal AI workloads, enterprises now need to store and query unstructured assets such as text, images, and video. The current split—big‑data teams managing structured storage and AI teams handling local files on GPU‑enabled machines—creates governance gaps and costly data movement. Introducing an AI‑native storage engine (e.g., Lance ) that provides high‑performance indexing and retrieval for multimodal objects allows the Lakehouse to become a single source of truth for both structured and unstructured data.

Kernel Capabilities: AI‑Native Query and In‑Database Inference

Native Vector Retrieval and Mixed Search

Simple semantic search is insufficient for high‑precision business scenarios. The engine must support mixed retrieval that combines traditional inverted‑index keyword matching with vector similarity search. A typical pipeline performs a coarse recall using either keyword or vector filters, followed by a fine‑grained re‑ranking that fuses both signals. This enables use cases such as contract‑clause search, e‑commerce image‑to‑image search, and visual question answering in online education.

In‑Database AI

Write‑time processing : During data ingestion the system automatically parses raw files, splits them into logical chunks, and generates embeddings (text, image, video) inside the storage engine. No external ETL scripts or manual labeling are required.

Query‑time inference : LLM capabilities are exposed as built‑in SQL functions. Users can invoke AI directly in a query, e.g.,

SELECT ai_sentiment(comment) FROM live_comments WHERE stream_id = 12345;

This allows real‑time filtering of noisy comments, detection of purchase intent, and automatic chatbot responses without exporting data to an external service.

Embedding inference in the kernel reduces latency, cuts external‑API costs, and improves throughput by filtering data before model execution.

Agent‑First Architecture: Exploratory Execution

AI agents perform multi‑turn reasoning, self‑correction, and high‑concurrency query generation. They often produce fuzzy SQL statements with descriptive constraints such as "precision > 80%" or "timeout < 2 s". To support this, the platform must provide:

Millisecond‑level elastic scaling of CPU and GPU resources, enabling simultaneous execution of structured analytics and GPU‑intensive model inference.

Fast metadata services that expose both schema definitions and semantic annotations, allowing agents to discover data structures on the fly.

Support for descriptive constraints in the execution engine, so the planner can dynamically trade off accuracy versus resource consumption based on the agent’s requirements.

Platform Autonomy: AI‑Driven Self‑Evolution

Learning best practices : The system continuously analyzes internal logs to extract optimization patterns (e.g., common query shapes, data skew) and internalizes them as automated management policies.

Intelligent fault detection : AI models monitor runtime metrics to automatically locate hidden performance regressions or failures, reducing reliance on manual troubleshooting.

Auto‑materialized views (Auto‑MV) : By correlating slow‑query analysis with workload characteristics, the engine automatically creates and maintains materialized views that accelerate recurring queries without user intervention.

These autonomous capabilities eliminate the need for custom UDFs and provide a smoother developer experience while the platform continuously optimizes performance and reliability.