AI Document Processing Platform

This AI-driven platform synthesizes raw information into actionable intelligence—in any form, through any means, at any scale. The core application is built in Python, orchestrating advanced AI models from Google (Gemini), Anthropic (Claude), and Fireworks AI. It features a robust, end-to-end pipeline to read, process, and deliver actionable data, designed to transform vast volumes of information effortlessly.

Tech Stack

• Languages & Core Libraries: Python, Bash
• AI & Machine Learning: Google Gemini API, Fireworks AI SDK, AWS Bedrock (Boto3), Pydantic
• Concurrency: Multiprocessing, Batch Processing
• Cloud & DevOps: AWS (S3, CloudFront)

Key Features

• Modular & Extensible Core
• Dynamic & Schema-Driven Outputs
• End-to-End Content Automation
• Autonomous Content Pipeline
• Flexible High-Performance Processing

Key Features

Modular & Extensible Core

The platform is based on a modular architecture with strict separation of concerns. Making the core maintainable, scalable, and extensible was critical to ensure the platform could evolve effortlessly over time to support various models and workflows.

This has allowed the platform to evolve over time to support various AI models, model providers and workflows effortlessly. It has been the critical design pattern that has enables everything else

Dynamic Schema Engine

A defining innovation of the platform is its dynamic schema engine. It provides dynamic schema selection and produces validated, structured intelligence.

The schemas are decoupled from the core application. This capability decouples the AI platform from downstream consumers, allowing the system to effortlessly serve arbitrary and evolving applications, with zero modifications to the core platform.

All structured output is strictly validated against the selected Pydantic model before being written, ensuring 100% reliability for any consuming service.

End-to-End Content Automation

The platform enables fully autonomous data lifecycles, from initial ingestion to final content delivery. It features a secure serialization/deserialization (SerDes) module that can serialize an entire directory structure into a manifest and securely reconstruct it on another system, along with built-in safeguards for data integrity and security.

Autonomous Content Pipeline

1. Ingestion: New screenshots appear in a designated folder.
2. OCR: Google Gemini is triggered to perform high-accuracy OCR on the images.
3. Analysis: The creation of the OCR text file triggers Fireworks AI to perform analysis and structuring.
4. Deployment: The final structured content is automatically uploaded to an AWS S3 bucket.
5. Final Delivery: A final script is called to invalidate the AWS CloudFront CDN cache.

Flexible High-Performance Processing

To accommodate diverse operational requirements, the platform provides multiple distinct processing modes, empowering users to select the optimal strategy for their specific balance of speed, cost, and contextual depth. This flexibility ensures that every workload is handled in the most efficient manner possible.

• Parallel Contextual Analysis: Processes multiple directories in parallel, where each directory represents a self-contained information silo.
• Massively Parallel Processing: Utilizes Python's multiprocessing to to process multiple files in parallel
• Asynchronous Batch Inference: End-to-end pipeline to orchestrates batch inference in AWS Bedrock
• Serial Contextual Analysis: Provide a unified, coherent context when related information is spread across multiple files.