Developing an AI-Powered Data Visualization Tool

Recently, I was working on a campaign rollout for a new product launch, tracking user behavior across email, site interactions, product usage, and a half-dozen other data points. As the spreadsheets grew more complex, it was sometimes difficult to surface answers to basic questions like: What does the product data reveal about user needs and behaviors?

Excel got slower as the datasets got larger, Tableau often took a while to set up dashboards, and it was at times sluggish when trying to share them. What I needed was a tool that could take in raw data, interpret plain-language questions, and quickly return clear insights that stakeholders could use without a steep learning curve or an additional cost to the budget. So, I built one.

The idea was simple: Create a platform that lets anyone upload a dataset, ask a question in natural language, and receive both a suggested chart visualization and a written explanation without having to undergo any training or do any manual setup. If you’re curious about how to build something similar, here’s how to approach it.

Before anything can be visualized or interpreted, your app needs to understand what kind of data it’s working with. That means:

• Accepting common formats like CSV, Excel, or JSON
• Using a backend tool like Pandas to load and structure the data
• Detecting column types such as datetime, text, and numeric
• Handling missing values and cleaning inconsistencies

At this point, the app is ingesting and shaping the data while writing logic that parses column names, detects outliers, simplifies confusing structures, and summarizes the dataset for the model that will eventually make decisions about it. One of the first things I learned in this process is that even the best model can't make up for missing structure. By adding preprocessing steps that renamed fields and flagged ambiguous columns, the quality of responses improved dramatically. This foundational layer determines how well the rest of the system will work. Clean data structure leads to an app that can easily interpret different types of data variables.

Key preprocessing steps:

• Standardize column names
• Infer and label types accurately
• Strip symbols and unify formats like date strings
• Drop duplicate or empty columns
• Generate a schema summary with column names, types, and sample values

Once the data is clean, you need a way for users to query it in plain language. That’s where the language model comes in. A large language model (LLM) can handle freeform questions and translate them into a structure the app can understand. Instead of writing rules for every use case, the model can interpret the question, reference the data, and respond with a meaningful output.

There are a many strong LLM options depending on your development environment.

Open-source (Local Deployment):

• Mistral, Meta’s LLaMA 2, and Google’s Gemma are solid open-weight models that can run locally with minimal hardware.
• These can be deployed via Ollama, which allows models to run on your machine without API calls.
• Gemma includes commercially permitted weights but carries a more restrictive license than the others. Review terms before production use.

Hosted (API-Based):

• If you're building for production, hosted models like Claude or GPT-4 offer reliable performance and scale.
• They integrate easily with orchestration frameworks and support larger context windows.

Tip: If you’re prototyping or working with private datasets, try Mistral or LLaMA 2 locally. I used Mistral for development, and it worked well. For production environments, GPT-4 or Claude offer better consistency and support.

To get useful output from any LLM, your backend constructs a prompt that includes:

• A structured summary of the dataset such as column names, types, and sample rows dynamically pulled from the file
• The user’s question
• Instructions asking the model to return both a chart configuration and a short written insight

This is what makes the application generative. It doesn’t just interpret a question, it generates a usable response based on the structure and content of the dataset.

Example backend prompt:

You are a data assistant. Here is a summary of the dataset:
Columns: ['signup_date', 'page_views', 'signups']
Types: [datetime, numeric, numeric]
Sample row: ['2024-01-01', 5321, 148]

User question: How did signups change over time?

Return:
1. Recommended chart type
2. Fields to use on x and y axes
3. A short plain-English insight

Example model response:

{
  "chart_type": "line",
  "x_axis": "signup_date",
  "y_axis": "signups",
  "insight": "Signups increased steadily in Q1, with a noticeable spike mid-March."
}

Once you have a structured response from the model, you need a way to manage how that response gets processed, validated, and turned into something your application can use. That’s where an orchestration layer fits in.

An orchestration layer bridges the gap between raw input and user-facing output. Rather than manually wiring together the steps between a user's question and the rendered chart, the orchestration layer provides a consistent framework that handles that flow. It helps with:

• Building reusable prompt templates
• Chaining multiple tasks together, like parsing a schema, selecting a chart type, then generating an insight
• Validating model outputs and handling exceptions
• Formatting results into structures your frontend can render

LangGraph is one example of this kind of orchestration layer, but the idea applies broadly. You don’t have to use anything heavy. In my experience, even a lightweight orchestration setup—like a class that manages the prompt structure and response parsing—can be a huge improvement. It keeps your logic centralized and your prompts consistent, which helps avoid bugs and drift as the tool evolves.

If you’re building this into a larger application, I’d recommend starting with something small and modular. Focus on one step at a time (like returning a chart configuration), then build from there. Adding structure early makes it much easier to test, maintain, and expand later.

Once the model provides a chart structure and insight, you can render it using a visualization library. Two good options are Matplotlib for static charts or Plotly for interactive ones.

Pair the chart with a natural-language summary generated by the model. This readable insight gives users something they can quickly understand, explain, or copy into a slide deck. It’s one of the features that makes using a language model genuinely valuable in a data context.

One of my favorite moments came when I watched a teammate type in a vague question such as “How are things changing over time?” and the app generated a clean line chart and a summary that pointed out spikes in user behavior and when they occurred. It wasn’t a particularly deep insight, but watching the app generate something helpful and easy to use, made the whole process feel worthwhile.

Most people assume that building custom internal tools is out of reach. But with today’s ecosystem, Pandas, open-source LLMs, orchestration libraries, and well-structured backend logic, you can design tools that speak the unique language of your team, your campaigns, your content, and your customers.

You don’t need a subscription. You don’t need permission. You just need a clear problem to solve and the willingness to build something that answers it.