AI Workflow Automation for Data Analysts

Describe the workflow you want to automate: we'll build you a step-by-step spec you can run today.

First agent free · No account required

Build Your Own Data Analyst Bot

All you need to do is document the steps you take as a Data Analyst. We'll use your steps to create instructions for AI to take over your recurring tasks from now on.

Describe your Data Analyst workflow

Walk us through what you do, which data sources you pull from, what cleaning or transformation you apply, where the output goes. Plain language, no SQL or code required.

We map it to an automation

BYOBot turns your description into your agent: a prompt for any AI chat tool, a browser agent script that runs it for you, and a full pipeline ready for a developer.

AI takes over your recurring tasks

You receive your agent, ready to use. Start in a chat tool to test the logic, go hands-free with a browser agent, or hand the pipeline spec to a developer to deploy.

One agent for your data workflow. Run it anywhere.

In your AI chat tool

Ready-to-use prompts for any AI chat tool

No setup. No code. Open Claude, paste the prompt, automate your data workflow. Start here to validate the logic: paste in your data and confirm the output looks right.

Works with ChatGPT, Claude, Gemini, or any LLM: free to start

In a browser agent

An AI agent drives your browser for you

A browser agent prompt that opens your data sources, runs the pull or query, cleans and formats the output, and drops it into your report template or dashboard: with you validating the output before it goes anywhere.

Works with any AI browser agent tool

In your stack

A full blueprint for unattended automation

An API-driven pipeline with source polling, data transformation logic, validation rules, and delivery workflows. Ready to hand to a developer or deploy with an automation platform.

Connects to Zapier, Make, n8n, or custom APIs

What you get that an LLM alone won't give you

✦ Portable

Download it, use it with any browser-capable AI

A chat answer dies in the thread. Your agent is a file you own: run it in ChatGPT, Claude, or Gemini, drop it into a browser agent, or hand it to your stack, as many times as you like.

✦ Conversational

Training your AI agent is super easy

No prompt engineering degree required. Describe what you do, the tabs you open, the data you move, and BYOBot asks the questions that turn your knowledge into the spec.

✦ Tested

Custom QA milestones in every agent

Every build ships with smoke tests and pass, warn, and fail checks for each step. You'll know it ran right, not just that it ran.

See Yourself Using AI?

BYOBot can help automate data analyst workflows for all of these roles, regardless of your experience or skill level.

The data prep layer, automated by AI

Why data prep stays manual: every data source has different schemas, export formats, quirks, and edge cases. A CSV from one tool has headers in a different format than another. One API returns timestamps in UTC, another in local time. A database table has a column that's sometimes NULL and sometimes a string. This heterogeneity makes it hard to automate without an agent that can reason about what it's looking at instead of just following rigid rules.

How BYOBot differs from traditional ETL tools: it produces a spec for an agent that understands the data context, not just the field names. If it encounters an unexpected schema or a data type mismatch, it handles it according to rules you define instead of failing silently. The browser agent does this in your browser with you watching. The pipeline spec is a full production spec that can be deployed with dbt, Airflow, or a custom pipeline.

Breadth of analyst automation: from scheduled pulls to cleaning pipelines to automated stakeholder digests. Most of the recurring work in data analysis (the pulls, the joins, the validation, the formatting) can be described once and automated after, freeing time for actual analysis.

Frequently Asked

What data analyst workflows can BYOBot automate? +

BYOBot automates the prep layer of analysis: scheduled data pulls from multiple sources, data cleaning and transformation pipelines, weekly stakeholder reports generated from raw data, data syncs between warehouses and dashboards, cross-source data joins, and formatted metrics digests. Any pull-clean-transform-deliver workflow that runs on a regular schedule can become a BYOBot automation.

Does it work with SQL databases and warehouses? +

Yes. BYOBot produces specs that work with BigQuery, Snowflake, PostgreSQL, and other databases. For the browser agent, the agent can interact with SQL interfaces and export tools. For the pipeline spec, the full spec includes query execution, transformation logic, and data delivery steps. It handles both no-code tools and SQL-based warehouses.

How is this different from dbt or Airflow? +

dbt and Airflow are powerful but require significant engineering overhead to set up and maintain. They're orchestration frameworks. BYOBot is the spec layer that comes before orchestration: it produces a detailed spec of what the transformation should do, which can then be handed to dbt, Airflow, or a developer. BYOBot is faster to describe and execute at the chat prompt and 2.

Can it replace a data pipeline tool? +

The chat prompt and 2 can handle one-off or semi-automated data workflows without needing a dedicated tool. The pipeline spec provides a spec that's ready to hand to dbt, Airflow, or a custom pipeline. BYOBot is the spec and human-in-the-loop layer that makes building pipelines faster and more accessible: not a replacement, but a faster path to a production pipeline.

Do I need to know how to code? +

No. The chat prompt is a copy-paste prompt for an AI chat tool: no SQL required. The browser agent can navigate data tools and export interfaces. The pipeline version is a spec a developer implements, but you'll hand them a complete, detailed spec rather than starting from scratch.

How does it handle data quality issues mid-pipeline? +

BYOBot specs include validation and error handling logic. The agent can check for missing values, unexpected schemas, and data anomalies, then either flag them for review or handle them according to rules you define. The chat prompt and 2 run with your review before delivery. The pipeline spec includes a QA checklist and error handling for production deployments.