Databricks

Snowflake

Cloud data platform for AI

Snowflake is a cloud-based data warehousing platform that enables organizations to store, process, and analyze large volumes of data. It supports AI and machine learning workloads with seamless integrations.

Microsoft Clarity

Free user behavior analytics with heatmaps.

Microsoft Clarity is a 100% free behavioral analytics tool built by Microsoft, widely used by US web developers, digital marketers, SaaS founders, and e-commerce store owners. It gives you a complete visual picture of how real visitors interact with your website — where they click, how far they scroll, and exactly where they lose interest and leave. Clarity's two core features are heatmaps and session recordings. Heatmaps show you aggregate click, scroll, and move patterns across your entire site in a color-coded visual. Session recordings let you replay individual user visits, including the ability to automatically flag frustration signals like rage clicks (rapid repeated clicking) and dead clicks (clicking on non-interactive elements). For US businesses managing CCPA compliance, Clarity is fully CCPA- and GDPR-compliant and automatically masks sensitive input fields — passwords, credit card numbers, and personal data — without any manual configuration.

Vanna AI

Open-source Python RAG framework for SQL database generation

Vanna AI is an open-source Python-based RAG (Retrieval-Augmented Generation) framework engineered to generate high-accuracy SQL queries from plain English questions. Unlike general-purpose chatbots that frequently hallucinate non-existent database columns and tables, Vanna trains specifically on your database schema, table DDL, documentation, and historical query logs. When a business user or developer asks a natural language question, Vanna retrieves relevant schema definitions and validated SQL examples to construct an accurate, executable SQL query for PostgreSQL, Snowflake, BigQuery, MySQL, SQLite, or SQL Server. With over 12,000 GitHub stars, Vanna allows organizations to deploy self-hosted text-to-SQL agents inside Slack, Streamlit dashboards, or internal REST APIs without exposing private database records to third parties.

Unstructured

Enterprise document ingestion & unstructured ETL pipeline for RAG

Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.

RAGFlow

Open-source RAG engine based on deep document understanding & OCR

RAGFlow is an open-source enterprise RAG (Retrieval-Augmented Generation) engine based on deep document understanding and multimodal document parsing. Unlike naive RAG systems that slice documents into arbitrary character chunks—scrambling complex tables, footnotes, and multi-column layouts—RAGFlow preserves the original semantic structure of complex documents. Powered by DeepDoc computer vision models, RAGFlow extracts clean text, recognizes complex tables across multiple pages, identifies corporate hierarchies, and visually highlights exact source citations with bounding boxes inside PDF viewers. With self-hosted Docker deployment, low-code workflow orchestration, and native support for local and cloud LLMs, RAGFlow is widely adopted by enterprise organizations requiring zero hallucination in legal, financial, and technical document analysis.

Gemini Pro 1.5

Massive context window for complex data

Google's high-performance multimodal model capable of processing up to 2 million tokens, including long videos and codebases.