BI tools show you numbers. Count helps you make decisions
BI tools like Tableau, Looker, and Omni are built to share data at scale. They’re great for distributing core metrics and supporting day-to-day operations.
Count is designed to help teams understand their business better, use data to solve problems and get everyone aligned so decisions can be made faster. It’s built for agentic analytics: the AI does the analysis, not just the query.
BI tools: “What happened?”
Count: “What’s going on &
what should we do next?”
BI tools are built for single-player workflows. Analysts build dashboards. The business consumes them.
The valuable work - the questions, debates, and decisions - happens elsewhere.
Count gives that work a home. Data teams and business users explore together, build shared understanding, and iterate quickly to find answers.
BI tools depend on clean, pre-modelled data. Exploration is limited, because numbers can't easily be questioned or rebuilt.
Count gives you all the tools to work with any sort of data. SQL, Python, and low-code, allowing you to clean, model, and analyse data in one workflow.
Every step is visible and auditable, so answers can be trusted.
BI tools primarily produce dashboards - every insight has to fit the same format, making it difficult to tell a story or adapt the output to different audiences.
Count lets you present the same analysis as slide decks, metric trees, narrative reports, or dashboards.
The output fits the use case, and you never have to rebuild in another tool.
Compare Count vs BI tools
Count covers all the core BI use cases plus the stuff which actually helps your business improve.
| Capability | BI Tools | Count |
|---|---|---|
| Reporting & distribution | ||
| Operational dashboards | Yes | Yes |
| Scheduled reports & alerts | Yes | Yes |
| Fine-grained permissions | Yes | Yes |
| Semantic model / metric definitions | Yes | Yes |
| Distribute reports at scale across org | Yes | Yes |
| Row-level permissions | Yes | Coming soon |
| Embedded analytics | Yes | — |
| Exploration & analysis | ||
| AI analyst (query generation, pattern detection) | Limited | Yes |
| Advanced visualisations | Limited | Yes |
| SQL & Python in the same workspace | — | Yes |
| Non-linear, canvas-based exploration | — | Yes |
| Branch, drill, and backtrack without rebuilding | — | Yes |
| Connect raw data sources (CSVs, MCP, APIs) | — | Yes |
| Clean and transform data in-workflow | — | Yes |
| Collaboration | ||
| Version control | Yes | Yes |
| Comment & discuss on the analysis | Limited | Yes |
| Visible reasoning & audit trail | — | Yes |
| Analyst & stakeholder work in same space | — | Yes |
| Outputs & presentation | ||
| Dashboards | Yes | Yes |
| Self-service via Slack bot / MCP | Yes | Yes |
| Metric trees & process flows | — | Yes |
| Slide decks & presentations | — | Yes |
| Long form reports | — | Yes |
Create a flywheel of better decisions
Count gives you the tools to not just observe your business but walk through the full analytical life-cycle to a conclusion.
BI tools + Count
Already have a BI tool? No problem. Most of our customers use Count alongside their BI tool, at least initially.
Count supports all major semantic layers and MCP servers alongside data warehouses so you can use the same data in both tools.
Explore integrations →For data & leadership teams
decision-
making
gap.
The decision-making gap.
Why most companies move slower than they should and a diagnostic to find out where you stand.
- →The four-stage maturity curve for decision velocity
- →The four structural barriers nearly every company faces
- →An 8-question diagnostic to map where your org sits
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.