One Storage Layer

Every Data Type. Every AI Workload.

A single Lakehouse Volume ingests structured tables, raw images, documents, and vectors-then serves them through SQL, API, or any compute engine. One copy. Zero duplication.

Your Data Lake Wasn't Built for AI

Traditional data systems were designed for BI dashboards and SQL reports. AI demands more-multimodal data, vector search, real-time inference. SINGDATA LAKEHOUSE was architected for this from day one.

Traditional Approach-Data Silos, AI/BI Separated

Structured DW + Object Storage + Vector DB - three separate systems, each requiring its own ingestion, maintenance, and access layer

Unstructured data (images, PDFs, video) can't be analyzed in your data warehouse - requires additional ETL pipelines and separate tooling

Calling AI models from data pipelines requires engineers to write Python, manage API credentials, and build custom orchestration

Vector search, scalar filtering, and full-text search each require different systems - hybrid retrieval across modalities is a major engineering project

High storage costs from full data replication - unstructured data at scale creates unsustainable infrastructure spend

Proprietary data formats create vendor lock-in - migrating compute engines or cloud providers costs months of engineering effort

SINGDATA LAKEHOUSE - Unified Foundation, AI Native

Lakehouse Volume unified storage for all data types.One copy serves structured, unstructured, and vector workloads - zero duplication, unified governance

Direct SQL access to images, documents, and unstructured data.No ETL required - query a PDF or analyze an image using the same SQL interface as your tables

AI Functions encapsulate AI as SQL functions, ready to use out of the box.SELECT ai_analyze(col) FROM table - no Python, no API wiring

Unified scalar + vector + inverted index storage.Hybrid retrieval - text-to-image, image-to-image, and filtered vector search - all from a single query layer

Storage costs reduced to 10% of traditional.Tiered storage, deduplication, and Iceberg compression eliminate redundant copies - EB-scale capacity without EB-scale bills

Apache Iceberg open format - no lock-in.Run Spark, Flink, Trino, or any compute engine over the same data without migration or reformatting

Four AI-Native Capabilities

Built Into the Storage Layer

Not bolted-on integrations-these capabilities live inside Lakehouse Volume and work with your existing SQL workflows.

AI Functions

Multimodal Search

Semantic View

MCP Server

Invoke AI Models Directly in SQL

AI Functions encapsulate large models as native SQL functions - so data analysts can call AI without writing a single line of Python. Supports local fine-tuned models and cloud APIs alike, embedded directly in your data pipelines.

Native SQL Invocation

SELECT ai_function(column) FROM table - call AI like any SQL function. No external API calls, no Python wrappers, no context switching

Image Recognition

Vehicle damage assessment, product defect inspection, OCR text extraction from scanned documents - visual AI directly in your data workflow

Text Analysis

Sentiment analysis, text classification, entity extraction, content generation - all NLP capabilities available as SQL functions on any text column

Pipeline Integration

Seamlessly embed AI calls inside scheduled data tasks - form real-time automated AI job flows without building separate orchestration infrastructure

10+

Built-in AI Functions

Custom

Model Integration

ms

Inference Latency

AI-Ready Data for Every Industry

From smart insurance assessment to real-time security surveillance - see how teams across industries deploy SINGDATA LAKEHOUSE as their AI data foundation.

Smart Damage Assessment

AI Functions

Image Recognition

Upload vehicle damage photos, call AI Functions via SQL to auto-detect damage areas and severity, and generate assessment reports - reducing claims processing from days to minutes.

Smart Security Surveillance

Multimodal Search

Image-to-Image

Millisecond-level face and vehicle image search across massive video archives. Real-time alert triggering when persons of interest appear across distributed camera networks.

Enterprise Smart Q&A

Semantic View

RAG

Transform enterprise documents, databases, and operational data into a queryable knowledge base. Business users get answers in plain English - no SQL, no tickets, no waiting on analysts.

E-commerce Visual Search

Text-to-Image

Hybrid Search

Shoppers describe what they're looking for - "floral summer dress, under $80" - and get precisely matched product images. Text-to-image plus scalar filtering in a single query.

Medical Imaging Analytics

Image-to-Image

AI Diagnosis

Similar case image retrieval helps doctors find analogous historical diagnoses in seconds. AI Functions run preliminary lesion detection directly on imaging data stored in Lakehouse Volume.

Automated Data Engineering

MCP Server

Agent

Natural language-driven data development via connected AI Agents. Agents auto-create data sources, write SQL transformations, and configure scheduling - without a human writing a line of code.