Integrations
Count fits neatly into your existing data infrastructure and turns it AI-native.
Leverage all the functionality and data in your data warehouse directly in Count.
Connect Count directly to an existing semantic layer or use Count’s own semantic layer to define business metrics.
Business applications
Connect Count directly to your business applications and bring in your data and business context from hard to reach places.
Access your dbt models and meta data directly in the canvas and push back any changes directly from within Count.
Version control and sync agent context and business logic within Count metrics directly with GitHub.
Use Count’s API to control your workspace, schedule workflows and access your data.
Ask Count’s AI agent questions directly from Slack and get answers to anyone in your organization, instantly.
Access Count’s agent or query any data source directly within any other AI platform.
All your sources together without the warehouse bill
Connect your warehouse, semantic layer, CSVs, and business tools in one canvas.
Count's compute layer routes queries intelligently - big joins run on your warehouse, everything else runs on Count's servers or in your browser. You get full access to all your data without every ad hoc question hitting your Snowflake bill.
Learn more →FAQs
Not exactly.
Most teams start using Count alongside their existing BI tool. Traditional BI is great for tracking metrics and sharing dashboards, but it’s not where people actually work with data. Count fills that gap - giving you a space to explore, analyze and think through problems.
Over time, teams use Count in different ways. Some move more of their reporting into Count, replacing static dashboards with something more flexible and collaborative. Others keep their BI tool for large-scale operational reporting, while using Count for deeper analysis and decision-making.
A lot of the messy, fragmented workflow teams rely on today.
SQL queries and notebooks for analysis. Slides for presenting. Spreadsheets for stitching things together. Slack threads for discussion. Count brings all of that into one place - so the work, the thinking, and the decisions stay connected.
Over time, many teams also reduce or replace parts of their BI stack. Instead of maintaining static dashboards, they use Count for more flexible, collaborative ways of understanding and improving the business.
Count’s agent is powered by leading models from Anthropic, OpenAI and Google.
It works with the context you provide (including your data, logic and previous analysis) and can run queries across the sources you’ve connected. This lets it explore questions, generate analyzes and go deeper, faster than a human alone.
Your data stays under your control. We don’t train models on it, and the agent will always ask permission before accessing external data sources.
Count runs queries in three places: directly on your data warehouse or connected sources, on Count’s servers, and in your browser. This flexible approach lets you combine data across sources while reducing the load on your warehouse.
For many teams, this also lowers costs. By shifting exploratory work out of the warehouse, some customers see significant reductions in compute spend.
Count’s infrastructure is hosted in the US and EU, and you can choose where your data is processed.
Yes.
Count is built with security and compliance at its core. We are SOC 2 compliant and adhere to GDPR requirements, with support for HIPAA where needed.
We apply industry-standard practices across data access, encryption and infrastructure to ensure your data is protected at every step.
For full details, visit our Trust Center at trust.count.co.