AutoTable — One-Click Spreadsheet Cleaner

AutoTable is a specialized SaaS application engineered for high-efficiency spreadsheet sanitization. It addresses the common pain points of knowledge workers by automating the transformation of messy, inconsistent tabular data into validated, ready-to-use formats.

Table of Contents

• Product Overview
• Feature Specification
  • Automated Cleaning Pipeline
  • Advanced Pro Module
• Technical Architecture
  • Tech Stack
  • Project Methodology
• Programmatic API Specification
• Development and Onboarding
• Security and Compliance

Product Overview

AutoTable provides a frictionless workflow for users dealing with survey data, payroll exports, research logs, and administrative records. The system is built on a "Privacy-First" architecture where files are ephemeral and processing is handled in memory before being committed to a secure, short-term database storage.

Key Value Propositions

• Operational Efficiency: Reduces spreadsheet cleaning time from hours to seconds.
• Accuracy: Implements deterministic algorithms for type coercion and normalization.
• Privacy Core: Automated TTL (Time-To-Live) for all uploaded datasets ensures data sovereignty.

Application Preview

Landing Page

User Dashboard (Upload Interface)

Pricing and Subscription Plans

Feature Specification

Automated Cleaning Pipeline

The core engine (lib/cleaner.ts) executes a multi-stage, idempotent transformation pipeline:

1. Header Normalization: Converts inconsistent headers into standardized snake_case format, stripping illegal characters and whitespace.
2. Whitespace and Unicode Sanitization: Executes deep trimming and NFC normalization to remove hidden character artifacts.
3. Deterministic Type Detection: Identifies and coerces values into canonical Number, Date, and Boolean types.
4. Structural Optimization: Identifies and removes null/empty rows based on configurable density thresholds.

Advanced Pro Module

Subscription-tier users gain access to advanced heuristic-based features:

• Fuzzy Deduplication: Utilizes similarity scoring to identify nearly identical records that standard exact-match filters miss.
• Smart Column Merging: Heuristically detects related fields (e.g., firstname, lastname) and combines them into structured entities.
• Contact Standardization: Normalizes international phone formats and standardizes email casing.
• Multi-Format Support: Unlocks processing for Excel (.xlsx) and JSON formats alongside standard CSV.

Technical Architecture

Tech Stack

AutoTable utilizes a modern, typed stack designed for scalability and performance:

• Frontend: Next.js 15 (App Router), React 18, Tailwind CSS.
• State Management: React Hook Form with Zod validation.
• Component Library: Radix UI primitives for accessible design.
• Core Logic: TypeScript-based processing engine supplemented by xlsx and csv-parse.
• Persistence: PostgreSQL (via Supabase) utilizing Row Level Security (RLS) policies.
• Authentication: Multi-modal (Magic Link / Password) Supabase Auth.
• Financials: PayPal API integration for subscription lifecycle management.
• Infrastructure: Sentry (Error Tracking), Vitest (Testing), GitHub Actions (CI/CD).

Project Methodology

Directory structure follows a modular Next.js architecture:

Programmatic API Specification

AutoTable exposes a RESTful API for batch processing.

Endpoint: POST /api/clean

Authentication: Supabase JWT Session required.

Payload: multipart/form-data

• file: One or more files (Bulk supported for Pro users).
• operations: JSON string defining cleaning toggles (e.g., {"removeDuplicates": true}).

Response Structure:

{
  "success": true,
  "results": [
    {
      "jobId": "uuid",
      "filename": "data.csv",
      "rowsOriginal": 1200,
      "rowsCleaned": 1150,
      "downloadUrl": "/api/download/uuid?format=csv"
    }
  ]
}

Development and Onboarding

Prerequisites

• Node.js >= 18.x
• pnpm >= 9.x
• PostgreSQL / Supabase Instance

Local Setup

1. Clone and Install:

   git clone https://github.com/VoxDroid/AutoTable.git
   cd AutoTable
   pnpm install
   

2. Environment Configuration: Configure .env with the following variables:

   DATABASE_URL=
   NEXT_PUBLIC_SUPABASE_URL=
   NEXT_PUBLIC_SUPABASE_ANON_KEY=
   PAYPAL_CLIENT_ID=
   PAYPAL_CLIENT_SECRET=
   

3. Database Migration:

   pnpm db:setup
   

4. Execute Development Instance:

   pnpm dev
   

Engineering Standards

• Linting: Rules enforced via ESLint and Prettier. Run pnpm lint.
• Typing: Strict TypeScript mode enabled. Run pnpm type-check.
• Testing: Vitest for unit logic. Run pnpm test.

Security and Compliance

• Data Retention: Cleaned data resides in the cleaning_jobs table and is purged based on system TTL policies.
• Row Level Security: Database policies ensure that data is only accessible to the authenticated owner.
• Encryption: All transfers are forced over TLS 1.3; data is encrypted at rest within the database layer.

---

Private SaaS Application. Managed by VoxDroid.