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Case Study

Datapoxy

Universal Data Import Platform

In Development
Web AppAPIAWSReactAIFile ProcessingAPI

The Problem

Businesses waste countless hours manually importing, converting, and cleaning data across incompatible formats and systems. Engineering teams build one-off import scripts that break on edge cases, and non-technical users are locked out of the process entirely.

The Solution

An AI-powered universal data import platform that automates file conversion, column mapping, validation, and migration into any target system. Datapoxy turns messy data into clean, structured records without writing a single line of code.

Key Features

What Datapoxy Does

Drag-and-drop file upload with format auto-detection
AI-powered column mapping and data type inference
Real-time validation with error highlighting
Batch processing for large-scale migrations
API access for programmatic data imports

Product Vision

Become the default data import layer for SaaS products and enterprise systems, enabling any business to move data between systems without engineering resources.

Core Features

Under the Hood

Universal file parser (CSV, Excel, JSON, XML, PDF tables)
AI column mapper with learning capabilities
Validation rules engine with custom rulesets
Template library for common import patterns
Webhook and API integration layer

Technical Direction

Built on AWS with a React frontend, serverless processing pipeline, and AI-assisted transformation engine. Lambda functions handle file parsing at scale while DynamoDB stores mapping templates and validation rulesets.

Tech Stack

ReactTypeScriptAWS LambdaS3DynamoDBOpenAI API

Business Model

Freemium SaaS with usage-based pricing for enterprise volumes

Current Stage

Core architecture designed. File processing pipeline in active development.

Roadmap

  1. 1MVP with CSV/Excel support
  2. 2AI mapping engine
  3. 3API access and developer SDK
  4. 4Enterprise multi-tenant deployment

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