Compass
Compass is an AI-powered, Slack-native data assistant that turns plain English questions into instant answers, summaries, charts, and insights powered by your actual warehouse data, so teams can make data-driven decisions without waiting on BI backlogs or building dashboards first. It connects directly to major data warehouses (Snowflake, BigQuery, Redshift, Postgres, AWS Athena, Databricks, and more), learns your schema and context, and generates governed, SQL-backed responses and visualizations in the tools your team already uses, all while keeping your data where it lives and under your control. Compass builds organizational context over time so answers become more accurate and relevant, supports collaboration through Slack threads, can schedule recurring analysis, and provides a shared repository of definitions and insights that help reduce analytical silos and reliance on specialized SQL users.
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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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ClarityQ
ClarityQ is an AI data analytics platform for data, product, and business teams. It connects to warehouses such as Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, Athena, and Azure Synapse, where users ask questions in natural language and receive analysis, visualizations, insights, and action plans without remodeling data. Its automatically maintained Context Layer organizes business logic, metric definitions, and table relationships, with changes tracked for review. Answers can show reasoning, generated SQL, and data sources; validation checks results, and follow-up questions clarify ambiguity. Teams create dashboards from prompts, schedule reports to Slack or email, and share results with role-aware access controls. The platform is read-only by default and offers an open API, SOC 2 Type II and GDPR compliance, Private Link, and single-tenant options. Use cases include product, growth, and business analysis for gaming, SaaS, e-commerce, and consumer app companies.
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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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