TextEase
Helping Neurodiverse individuals with their daily text based tasks
Created on 12th April 2025
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TextEase
Helping Neurodiverse individuals with their daily text based tasks
The problem TextEase solves
We’re building TextEase—an AI-powered reading and response assistant designed for dyslexic and neurodiverse users to communicate effortlessly across real-time platforms like WhatsApp, Discord, and Email.
Our core problem: Millions struggle with reading complex or transliterated messages, responding with clarity, or navigating long unstructured texts. These everyday digital conversations often become frustrating, inaccessible, or overwhelming for neurodiverse individuals. TextEase directly solves this by enabling live message simplification, Roman Hindi-to-English transliteration, instant grammar correction, and speech-to-text/audio replies—right inside the platforms users already use via a browser extension. System is split into two AI-powered modules:
- Text Tools: for summarization, keyword highlighting, simplification, and correction.
- Chat Assist: for real-time voice interaction, translation, and typed/textual message support.
Powered by GROQ AI, GROQ APIs, and deployed on Vercel, the solution leverages LLMs and NLP to personalize assistance in real time. Unlike static accessibility features, TextEase adapts to each user’s preferences ensuring intuitive and inclusive communication.
Despite the rise of digital communication, neurodiverse individuals especially those with dyslexia, ADHD, and autism—are often left behind due to cognitive overload, difficulty reading or typing fast, and limited support tools across platforms. The lack of inclusive solutions leads to social isolation and reduced opportunities in education and employment as well as social life. TextEase directly addresses this gap by offering a unified, real-time platform that enables inclusive and accessible communication for everyone, regardless of neurological differences
Challenges we ran into
While developing TextEase, our AI-powered assistive platform, we encountered several key challenges that tested both our technical and operational capabilities:
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Backend Deployment Issues
Our backend often failed to bind correctly during deployment. Though it worked locally, cloud services like Render and Railway introduced CORS errors, broken API routing, and environment variable issues, delaying the deployment process. -
Chrome Extension Failures
Integrating real-time AI into the browser extension was unstable. API calls from content scripts frequently failed, permissions were tricky under manifest v3, and script injection across webpages wasn’t consistent—leading to major debugging hurdles. -
API Credit Limitations solved by GROQ
Using other APIs required verified billing and had strict usage quotas. As students, limited access to API credits disrupted testing and development, forcing us to use mock data at times. -
Sync and UI Complexity
Maintaining real-time sync between modules like speech, simplification, and grammar correction was challenging. Delays in one module often broke user flow, and designing an inclusive but non-cluttered interface took multiple iterations.
Tracks Applied (1)
Groq track
Groq