What I Built
The Chaos Translator is an AI-powered platform that transforms scattered information from conversations and documents into structured, actionable intelligence.
It can process WhatsApp chat exports, PDFs, DOCX, PPTX, images, and audio recordings, bringing related information together instead of treating every file as an isolated source.
It extracts and connects:
People and participants
Tasks, owners, and deadlines
Important decisions and events
Dependencies and unanswered questions
Contradictions across different sources
Evidence-backed action plans
Its key differentiator is cross-source intelligence. For example, if a WhatsApp conversation mentions one deadline while an uploaded document mentions another, The Chaos Translator identifies the conflict and presents the supporting evidence instead of silently choosing one.
Users can also investigate their uploaded information through conversational AI and trace answers back to their original sources.
The Problem
I built this for my friends, particularly those who regularly collaborate on projects, hackathons, college activities, and group work.
Important information often gets scattered across WhatsApp groups, documents, screenshots, and voice notes. Deadlines get buried in conversations, responsibilities become unclear, and decisions are difficult to track.
Finding out what was decided, who is responsible, and what needs to happen next can become a task of its own.
The Chaos Translator aims to eliminate this information chaos by connecting scattered details and turning them into one actionable picture.
Demo
Demo Video Link: https://drive.google.com/drive/folders/1Jl-xs_hhN7MYlpdhMsU3VPUOVefHQQ-r?usp=sharing
Source Code
GitHub Repository: https://github.com/srujankaleru2007/Chaos_Translator.git
How I Built It
The project uses a full-stack architecture:
Frontend: Next.js, React, TypeScript, and Tailwind CSS.
Backend: Python and FastAPI.
Database: MongoDB Atlas for persistent storage.
AI and Reasoning: Qwen 3.5 4B for lightweight extraction and GPT-OSS-20B through Groq for complex reasoning and cross-source analysis.
Document Processing: Docling for document extraction.
OCR: PyTesseract integration for image text extraction.
Audio Processing: Faster-Whisper integration for speech transcription.
The system follows a source-first architecture: extracted information retains its original source and evidence references, allowing the intelligence layer to connect findings without losing their provenance.
Why Open Innovation Matters
The project brings together open-source models, document-processing tools, and developer platforms to solve a practical problem.
Rather than relying on a single closed system, it combines specialised tools and models into a modular architecture. This makes the platform easier to extend, inspect, and adapt.
Categories
GitHub Copilot
ElevenLabs
MongoDB
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