“I just find Count a lot more reliable, and so I feel safe actually telling the team this is our goal for the quarter, because I know that this is accurate data.”Racheal @ Narrative“The fact that we end up with a durable artifact *and* all of the thinking behind it.”Rob @ Lovehoney“This is what your agents in Count do. They make the reasoning transparent.”David @ CIL“Linking the visualization of the output to the code base is quite cool... you can see the SQL that led to these figures.”Eb @ Bumble“The collaboration side is so easy.”Henrique @ Loqbox“You can create a report. Everyone can access it. It's always up to date... Count is better in that sense of sharing, and it always being live data.”Racheal @ Narrative“The fact that that's so easy to provision is great.”Jared @ Upstatement“I just send the link, he jumps on... he can see the queries, the charts I'm doing, and then I can immediately send it to a product manager as well.”Henrique @ Loqbox“Count allows me to go to more granular detail... I like how I can put sticky notes on it.”Rob @ Lovehoney“Until Count I felt like I was running my business blind”Chris, CEO at a cybersecurity company“Best data tool I have discovered since dbt”Jessica, manager at a series C startup

4.8/5 stars SOC 2 Type II

AI for product teams

Self-serve the analysis you'd wait two weeks for.

Explore product data and present a crisp retention or experiment story in one canvas.

Try for free

A real Count canvas: a revenue retention exploration with cohort breakdowns.

Cursor

Select objects, pan the canvas, or scale things in proportion.

Code

Add SQL and Python cells that run on your data.

Low-code

Build visuals, tables and maps with a point-and-click editor.

Controls

Interactive inputs — date pickers, selects, sliders, filters.

Sticky

Drop a sticky note for comments or reminders.

Text

Add a markdown-enabled text box.

Shapes & media

Draw shapes and connectors, sketch, stamp, or embed images and links.

Frame

Group objects in a frame that resizes and moves as one.

Comment

Pin comments to objects and tag colleagues.

Supporting faster decisions at data-led companies

Map the model's claims

One claim. The whole argument behind it.

Peek under the hood and follow the model's path of reasoning. On the Count canvas, you can pinpoint it, tweak it, and resolve it.

Gemini thread · audit

Key Claims

For a meaningful share of tasks, fact-checking AI output costs as much time as doing the task manually — a "verification tax" that caps realized ROI regardless of model quality.C3

Tool fragmentation without any coherent orchestration layer is the current default state for most teams in this sample, not an edge case.C7

On top of that,

"not another tool" fatigue is a real headwind for AI product positioning in this market, separate from any doubts about AI capability itself.C8

Usage/credit caps are a real, hidden operational constraint on AI adoption that is likely under-discussed in most public research on the topic.C9

GEMINI THREAD · AUDIT

Key Claims

For a meaningful share of tasks, fact-checking AI output costs as much time as doing the task manually — a "verification tax" that caps realized ROI regardless of model quality.C3

Tool fragmentation without any coherent orchestration layer is the current default state for most teams in this sample, not an edge case.C7

On top of that,

"not another tool" fatigue is a real headwind for AI product positioning in this market, separate from any doubts about AI capability itself.C8

Usage/credit caps are a real, hidden operational constraint on AI adoption that is likely under-discussed in most public research on the topic.C9

Meanwhile,

vendor concentration/lock-in anxiety is building as major data platforms bundle AI more deeply into their existing footprints — a market-wide dynamic, not just one respondent's experience.C10

Why Count

Where you, your team and your agents dig in together.

Count is a collaborative canvas for deep research and analytics, where teams work with AI agents to dig into their business and turn what they find into decisions everyone trusts.

Why teams switch to Count

Go deeper than a chatbot
Multiple agents chase a question across SQL, Python, the web and your connected tools, blending quantitative data with the transcripts, docs and notes around it.
See how every answer was reached
No black box. Watch the agent write each query and follow its reasoning, then edit or branch from any point.
One place the team thinks together
A shared, real-time canvas where people and agents build side by side, so everyone reaches the same conclusion instead of a different answer each.
Answers that stay live and shareable
Turn any investigation into reports and metric trees that refresh on a schedule and stay traceable to the query behind them.

Features

See the difference

Go from question to chart in one canvas, across SQL, Python and drag-and-drop visuals. Blend cohort retention and feature adoption with user research and support tickets to see why a launch moved. Self-serve the answer instead of queuing with the data team.

Move from analysis to a shareable story in the same space, so it survives past one person's laptop. Reports and slides stay live as the data updates, and bring engineering, design and the business along. They can see the working and buy in.

Concurrent sub-agents and web research surface what a quick cut would miss. The agent writes and runs the queries and builds editable cells and charts as it goes. You get depth fast, without shipping something wrong.

Tutorials

Watch Count in practice

Tutorials, use cases, and agent best practices — for your viewing pleasure.

Integrations

Bring in any data and share answers anywhere

Connect your warehouse, semantic layer, spreadsheets and business apps via MCP. Everything explorable in one place, with answers flowing back out to Slack, reports and your other AI tools.