The Data Analyst Bot: A Case Study with OpenAI Assistants API

Dashboards are static. Questions are dynamic. This case study explores how we used the OpenAI Assistants API with Code Interpreter to build a tool that allows executives to “talk to their data.”

The Challenge: “Death by Dashboard”

Our executives needed answers like: “Show me the correlation between marketing spend and churn rate for Q3.”

• Old Way: Request a ticket -> Data Team writes SQL -> Export CSV -> Make Chart (3 Days).
• New Way: Ask the bot (30 Seconds).

The Solution: Assistants API + Code Interpreter

We didn’t need to teach the LLM math. We just needed to give it a Python sandbox.

1. File Upload

We upload the sanitized CSV dataset to the Assistant’s file storage.

2. Code Execution

When a user asks a question, the Assistant writes and executes Pandas code to analyze the data.

3. Chart Generation

The Assistant uses matplotlib to generate a .png file, which is then returned to the user in the chat UI.

Implementation (Node.js)

const assistant = await openai.beta.assistants.create({
  name: "Data Analyst",
  instructions: "You are a data expert. Write Python code to visualize data.",
  tools: [{ type: "code_interpreter" }],
  model: "gpt-4-turbo",
});

// Create a thread with the user's file
const thread = await openai.beta.threads.create({
  messages: [
    {
      role: "user",
      content: "Graph the monthly revenue trend.",
      file_ids: [file.id]
    }
  ]
});

Results

Conclusion

The Code Interpreter tool is a game changer. It turns the LLM from a “Guesser” into a “Calculator,” making it reliable enough for business intelligence.