Build Your First AI Data Analyst Chatbot
Chat with your data sources to retrieve information and perform precise calculations using an LLM and calculator tool.
What it does
Chat directly with your Google Sheets or databases to retrieve information and perform precise calculations using an LLM and calculator. You can analyze your data and execute math operations simply by asking questions in a chat interface.
Step by step
- Define the API endpoint for the Google Sheets or database connection with specific authentication headers.
- Implement a calculator tool that accepts numeric inputs and returns precise float results to avoid LLM hallucination in math.
- Structure the HTTP request payload to include user queries and context from previous turns.
Where the LLM does the work
- Generate natural language responses that interpret raw data rows into actionable business insights for the user.
- Determine whether a complex calculation is needed by analyzing the user's intent before invoking the calculator tool.
- Format tabular data retrieved from the source into a concise, readable summary suitable for chat interfaces.
Watch out for
LLMs often make arithmetic errors so always route calculation-heavy queries through the dedicated calculator tool instead of asking the model directly.
Sensitive data might be exposed in logs if you do not sanitize PII before sending it to the LLM provider.
Tools that fit
OpenAI Chat Model LLM LLM reasoning and chat interaction
HTTP Request API Retrieving data from external sources or databases
Calculator Service Performing mathematical operations on retrieved data
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Build Your First AI Data Analyst Chatbot: Chat with your data sources to retrieve information and perform precise calculations using an LLM and calculator tool. ## Specification - What it does: Chat directly with your Google Sheets or databases to retrieve information and perform precise calculations using an LLM and calculator. You can analyze your data and execute math operations simply by asking questions in a chat interface. - Trigger: Run manually (Manual · on demand) - Autonomy: Fully hands-off - Expected setup effort: under an hour - Tools/services involved: - OpenAI Chat Model: LLM reasoning and chat interaction - HTTP Request: Retrieving data from external sources or databases - Calculator: Performing mathematical operations on retrieved data ## Known pitfalls, handle each one explicitly in your implementation 1. LLMs often make arithmetic errors so always route calculation-heavy queries through the dedicated calculator tool instead of asking the model directly. 2. Sensitive data might be exposed in logs if you do not sanitize PII before sending it to the LLM provider. ## Reference implementation https://n8n.io/workflows/3050 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/build-your-first-ai-data-analyst-chatbot/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for data analysts who want the pull-and-summarize grind automated.
Developers can adapt this to their own repo or ticket queue with the same trigger and tools.