Overview
Formula Bot is an AI-powered data analytics platform that lets users upload data and ask questions in plain English to get instant insights. It generates charts, spreadsheets, dashboards, and presentations, with transparent SQL, Python, and R code and connectors.
Our take
Formula Bot started as spreadsheet formula generation and has grown into a broader data analysis product. The original job — describing what you want and getting a working formula — remains the strongest use, because spreadsheet formulas are well documented and the model is reliable at them. The analysis side needs the same verification as any conversational analytics.
Worth it if
Spreadsheet users who know what they want to calculate but not the formula syntax.
Look elsewhere if
Governed business reporting where metric definitions must be consistent and auditable.
✨ Features
Natural Language Analysis
Ask questions about your data in plain English and receive instant insights.
Dashboards & Charts
Automatically generates visualizations, dashboards, and presentations from your data.
Data Connectors
Connects to spreadsheets, databases, and PDFs with web-scraping capabilities.
Code Transparency
Exposes the underlying SQL, Python, and R generated for each analysis.
⚖️ Pros & Cons
Pros
Cons
💰 Pricing
Starter
$18 /mo
- 250 messages/month
- 2 automated dashboards
- 2,500 AI Actions
- Premium AI models
- 200MB file upload limit
Max
$29 /mo
- Per user; save ~30% yearly
- Unlimited messages
- Shared team workspace
- 5,000 AI Actions per user
- 10 automated dashboards
- 500MB file upload limit
Enterprise
$149 /mo
- Per user billing
- 15,000 AI Actions per user
- 20 automated dashboards
- Max compute (8 CPU, 8GB)
- Unlimited white-label embeds
Categories
❓ Frequently Asked Questions
Are the generated formulas reliable?
Generally yes, and worth testing on a known case anyway. The failure mode is a formula that computes something subtly different from what you asked — a range off by a row, a condition that excludes blanks unintentionally. Check the result against a small subset you can verify by hand before applying it to the full sheet.
Should I trust its analysis of my data?
Verify it the same way you would verify a colleague's number: understand what it calculated, not just what it concluded. Conversational analytics is reliable in proportion to how well your columns are named and how clearly your metrics are defined. Cryptic headers produce confident, wrong answers.