Numbers Station
Accelerating insights, eliminating barriers for data analysts. Intelligent data stack automation, get insights from your data 10x faster with AI. Pioneered at the Stanford AI lab and now available to your enterprise, intelligence for the modern data stack has arrived. Use natural language to get value from your messy, complex, and siloed data in minutes. Tell your data your desired output, and immediately generate code for execution. Customizable automation of complex data tasks that are specific to your organization and not captured by templated solutions. Empower anyone to securely automate data-intensive workflows on the modern data stack, free data engineers from an endless backlog of requests. Arrive at insights in minutes, not months. Uniquely designed for you, tuned for your organization’s needs. Integrated with upstream and downstream tools, Snowflake, Databricks, Redshift, BigQuery, and more coming, built on dbt.
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GetDot.ai
Dot is an AI data analyst that connects directly to your data warehouse and lets users ask questions in plain language to get instant, trustworthy insights. It integrates with Slack, Teams, or a native web app to deliver ad‑hoc data retrieval, visualizations, root‑cause analysis, and weekly business reports with actionable recommendations. By learning from your existing BI tools, dbt metrics, LookML, SQL queries, and documentation, GetDot.ai ensures consistent, governed answers reinforced by role‑based permissions and row‑level security. Setup is code‑free, with one‑click integrations for Snowflake, BigQuery, Redshift, PostgreSQL, and other SQL‑based sources. Continuous monitoring uncovers unknown unknowns, while a dedicated training and governance workspace lets you refine its behavior and maintain accuracy. Designed for speed and simplicity, Dot replaces dashboard overload by delivering precise answers in seconds rather than days.
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LangGrant
LangGrant connects large language models to your production databases for trusted, governed data analysis. Instead of one-off answers, it produces persistent, versioned Data Plans that show their work - the steps and logic behind every result. Teams review, validate, and reuse these plans without moving data. A single question can join data across multiple databases, and LangGrant auto-adapts to each schema. It works with models like Claude, OpenAI, and Google Gemini, and supports Snowflake, SQL Server, Oracle, PostgreSQL, BigQuery, Redshift, Azure SQL, Databricks, and MySQL. Data Plans move through a code-like lifecycle (test, sign-off, staging, approved) with access controls, PII protection, compliance validation, and audit trails. By reusing reasoning instead of paying AI for every question, it cuts cost, improves collaboration, and builds organizational intelligence. From the team behind Windocks, recognized by Gartner.
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nao
nao is an AI-powered data IDE designed specifically for data teams, combining a code editor with native integration to your data warehouse so you can write, test, and maintain data-centric code with full context. It supports warehouses such as Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Once connected, nao replaces a traditional data-warehouse console by offering schema-aware SQL auto-completion, data previews, SQL worksheets, and the ability to switch easily between multiple warehouses. The core of nao is its AI agent, which has full awareness of your actual data schema, tables, columns, metadata, and your codebase or data-stack context. It can generate SQL queries or full data-transformation models (e.g., for dbt workflows), refactor code, add or update documentation, run data-quality checks and data-diff tests, and even surface insights or run exploratory analytics, all while respecting data structure and quality constraints.
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