Deeplakev1.0.1

A legal firm ingests thousands of contract PDFs into a Deeplake managed table, extracting text and metadata using the PDF ingestion pipeline. They enable BM25 text search so paralegals can query clauses or party names across all documents instantly.

An online retailer stores product images as FILE schema and their CLIP embeddings. Customers can perform vector similarity search on the embedding column to find visually similar products, improving recommendation relevance.

A social media platform ingests user-uploaded videos into a Deeplake table. They use the FILE schema for video paths and store frame-level embeddings. A vector similarity search flags duplicate or inappropriate content by comparing embeddings.

A medical research lab ingests radiology images (e.g., X-rays, MRIs) into a managed table. They store image embeddings and metadata, then use hybrid search combining vector similarity with structured filters (patient age, modality) to retrieve relevant cases for model training.

A SaaS company ingests support ticket texts and their embeddings into Deeplake. They use BM25 and vector search to find similar past tickets, accelerating resolution and identifying recurring issues.

Offer Deeplake as a fully managed database service for AI workloads, charging based on storage volume and query usage (e.g., per GB stored, per vector query executed). Integrate into existing data pipelines to upsell premium features.

Build custom data ingestion services using Deeplake's SDK for clients, handling preprocessing, format conversion, and real-time sync. Charge per pipeline setup and per record ingested.

Provide vector similarity and hybrid search as a value-added service on top of existing databases. License the search engine or offer it as a managed API with pay-per-query pricing.