nveil 1.1.2

AI-powered data visualization — describe what you want, get production charts. Data stays local.

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.

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:

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:

# 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

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

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

1. You describe what you want in plain language
2. NVEIL AI plans the data processing and visualization (only metadata is sent — column names, types, statistics)
3. The Toolkit executes locally — joins, aggregations, pivots, rendering — all on your machine
4. 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 Powered by constraint solving, not random generation. Same input = same output, every time. ⚡ 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 handles geospatial heatmaps, 3D volumes, scientific visualizations, medical imaging (DICOM), network graphs, and 50+ other visualization types — all from natural language.

Save Once, Render Forever

# 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

pip install nveil

Requirements: Python 3.10+

Getting Started

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

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

NVEIL is proprietary software. Bug reports and feature requests are welcome via GitHub Issues.

License

Proprietary. See LICENSE for details.

---

Website • Documentation • Platform