🔥 SOOT

AI-Ready File Ingestion & Tokenization Gateway

SOOT is a lightweight, secure file ingestion layer built to collect, validate, and prepare raw data for AI training pipelines.

It sits at the front of your AI stack — accepting unstructured files, enforcing format rules, and making data ready for tokenization, embedding, and model training.

⚡ What SOOT Does

SOOT solves one problem cleanly: getting raw data into your AI system without garbage.

Accepts files via drag-and-drop or click upload

Enforces strict file type validation

Tracks ingestion state in real time

Secures uploads with authentication & role control

Exposes clean data for downstream AI workflows

🧠 Built for AI Pipelines

SOOT is designed as a data ingestion gateway, not just a file uploader.

It enables:

Dataset collection

Tokenization pipelines

Embedding generation

Fine-tuning workflows

Internal AI tooling

Think of it as the intake valve for your model training system.

🚦 Ingestion Lifecycle

Each file moves through a clear, auditable state:

QUEUED → BURNED (accepted) → REJECTED (unsupported / failed)

This makes ingestion transparent, debuggable, and scalable.

📂 Supported Data Formats

Configurable allow-list on the backend, including:

.txt

.json

.jsonl

.md

.py

.js

.csv

.zip

❌ Unsupported formats are rejected automatically.

🔐 Security & Access Control

JWT-based authentication

Role-based permissions

Admin visibility across all ingested files

User-scoped uploads by default

Built to be safe for internal tools and early production use.

🧱 Tech Stack

Frontend

React

Tailwind CSS

Native Drag & Drop API

Axios

Backend

Node.js + Express

JWT Authentication

Multer (file ingestion)

Storage abstraction (local / cloud-ready)

RBAC (admin / user)

🧩 How It Fits Into Your Stack Raw Files ↓ SOOT (Ingestion + Validation) ↓ Tokenizer / Chunker ↓ Embeddings / Datasets ↓ Model Training / Fine-Tuning

SOOT keeps ingestion isolated, clean, and replaceable — exactly how modern AI infrastructure should be designed.

🧪 Use Cases

Internal AI tooling

Dataset ingestion for LLMs

Code and document preprocessing

Research pipelines

Early-stage AI products

🧭 Roadmap

Content chunking

Token count estimation

Streaming uploads

Hashing & deduplication

Direct tokenizer integration

Dataset versioning

🔥 Why SOOT?

Because not all data deserves to make it into your model.

SOOT burns the noise and feeds the furnace with signal.