NVEIL Toolkit

Describe your data. Get production charts. Your data stays local.

Quickstart • API Reference • Examples • Changelog

NVEIL is an AI-powered data visualization toolkit. Write one line of natural language, and NVEIL processes your data and generates publication-ready visualizations — no chart code, no hallucinations, no data leaving your machine.

server.ts

import nveil

nveil.configure(api_key="nveil_...")

# Pass a file path directly — no DataFrame loading required.
spec = nveil.generate_spec("Revenue by region, colored by quarter", "sales.csv")

fig = spec.render("sales.csv")   # 100% local — no API call
nveil.show(fig)                   # opens in browser

From your shell

After pip install nveil the nveil command is on your $PATH:

server.ts

export NVEIL_API_KEY=nveil_...

# Ground yourself on the dataset (shape / dtypes / head preview)
nveil describe sales.csv

# Generate HTML + PNG + a reusable .nveil spec, print the explanation
nveil generate "Revenue by region, colored by quarter" \
  --data sales.csv --format all --explain

# Re-render an existing spec on fresh data — no API call
nveil render chart.nveil --data new_sales.csv

For AI agents (Claude Code / Claude Desktop / Cursor / Codex / …)

NVEIL ships first-class integrations:

bash

# Claude Code / Claude Desktop — install the bundled skill
nveil install-skill

# Claude Desktop, Cursor, any MCP client — add an MCP server:
# {"mcpServers": {"nveil": {"command": "nveil", "args": ["mcp"]}}}
nveil mcp                    # stdio server; launched by the MCP client

NVEIL multi-panel dashboard with charts, heatmaps, and flow diagrams

Why NVEIL?

Capability	NVEIL	Chatbot data analysis¹	LLM-to-viz libraries²	Traditional plotting³
Natural-language input	✓	✓	✓	✗
Raw data stays on your machine	✓	✗	✗	✓
Only schema + stats sent to server	✓	✗	✗	N/A
Deterministic, reproducible output	✓	✗	✗	✓
Offline re-rendering, zero API calls	✓	✗	✗	✓
Portable saved specs (`.nveil` files)	✓	✗	✗	✗
2D + 3D + geospatial + scientific	✓	2D	2D	varies
Multi-backend (Plotly, VTK, DeckGL)	✓	✗	✗	✗
Data processing engine	✓	✓	partial	✗

_{¹ ChatGPT Advanced Data Analysis, Claude Analysis tool, Gemini Data Agent · ² PandasAI, LIDA, Julius, Vanna · ³ Plotly, Matplotlib, Seaborn}

How It Works

Code

Your Data ──> Toolkit ──metadata only──> NVEIL AI ──> Processing Plan ──> Local Execution ──> Result
               ^                                                           ^
          raw data stays here                                     raw data stays here

You describe what you want in plain language
NVEIL AI plans the data processing and visualization (only metadata is sent — column names, types, statistics)
The Toolkit executes locally — joins, aggregations, pivots, rendering — all on your machine
You get a figure — Plotly, VTK, or DeckGL, auto-selected for your data

Key Features

🧠 Two Engines in One

Data processing (joins, pivots, aggregations, geocoding, time series) AND visualization generation from a single prompt.

🔒 Data Privacy by Design

Raw data never leaves your machine. Only column names, types, and aggregate statistics are sent.

📈 Multi-Backend Rendering

Auto-detects the best engine: Plotly (2D charts), VTK (3D/medical), DeckGL (geospatial).

🧪 Auditable Results

⚡ Offline Rendering

spec.render() runs 100% locally with zero API calls.

💾 Reusable Specs

Save to .nveil files, reload later, render on new data — no server needed.

Beyond Simple Charts

NVEIL AI chat — conversational data exploration with geospatial heatmaps

NVEIL handles geospatial heatmaps, 3D volumes, scientific visualizations, medical imaging (DICOM), biosignal data (EDF/EDF+), network graphs, and 50+ other visualization types — all from natural language.

Save Once, Render Forever

python

# Generate once (API call)
spec = nveil.generate_spec("Monthly trend by category", df)
spec.save("trend.nveil")

# Reload anywhere — no API call, no server, no cost
spec = nveil.load_spec("trend.nveil")
fig = spec.render(fresh_data)
nveil.save_image(fig, "report.png")

Installation

Terminal

pip install nveil

Requirements: Python 3.10+

Getting Started

Create an account at app.nveil.com
Generate an API key in Settings
Start visualizing

server.ts

import os
import nveil

nveil.configure(api_key=os.environ["NVEIL_API_KEY"])

spec = nveil.generate_spec("scatter plot of price vs area", df)
fig = spec.render(df)
nveil.show(fig)

See the examples/ directory for more usage patterns.

Documentation

Full documentation is available at docs.nveil.com:

Quickstart Guide
Core Concepts — sessions, specs, and the two-stage flow
API Reference — full reference for all public functions
Privacy Model — what data is sent, what stays local
Examples — bar charts, multi-dataset, offline rendering

Contributing

Contributions are welcome under the project's Contributor License Agreement. Bug reports and feature requests are welcome via GitHub Issues.

License

GNU AGPL v3 or later. See LICENSE. Commercial dual-licensing is available — contact pierre.jacquet@nveil.com.

Website • Documentation • Platform