Semantix

Semantix — Explainable AI for Better Communication

Comment

Inspiration

Many AI writing tools can generate or rewrite text, but they rarely explain why a sentence sounds unclear, uncertain, or unprofessional. I was inspired by the idea that AI should help people become better communicators, not just produce better documents.

As students and developers, I noticed that communication issues often stem from subtle linguistic patterns such as hedging, vague language, passive voice, and ambiguous references. I wanted to build a tool that combines NLP, linguistics, and AI to make those hidden patterns visible and understandable.

What it does

Semantix is an explainable linguistic intelligence platform that analyzes text and identifies communication issues before generating improved alternatives.

Users can paste a sentence, email, message, or paragraph into the application and receive:

  • Linguistic analysis
  • Tone and confidence signals
  • Detection of hedge words and vague language
  • Passive voice identification
  • Unclear reference detection
  • Structured explanations
  • Suggested rewrites

Unlike traditional writing assistants, Semantix focuses on explainability and education, helping users understand why certain language choices affect clarity and communication.

How I built it

Semantix was built as a full-stack monorepo.

Frontend:

  • Next.js
  • TypeScript
  • Tailwind CSS

Backend:

  • FastAPI
  • Python

NLP:

  • spaCy for tokenization, sentence analysis, entity extraction, and dependency parsing
  • Hugging Face classifiers for tone, formality, or sentiment once the rewrite path feels stable.

Architecture:

  • Shared TypeScript contracts between frontend and backend
  • Modular provider architecture for rewrites
  • Deterministic fallback rewrites for reliability
  • Optional integrations for OpenAI and Hugging Face providers

The frontend presents analysis results through a dashboard that includes issue detection, rewrite suggestions, provider status, linguistic explanations, and language reference material.

Challenges I ran into

One of the biggest challenges was balancing explainability with usability.

Many NLP systems produce raw technical information that is difficult for everyday users to understand. I had to translate linguistic concepts into actionable insights while still preserving their meaning.

Another challenge was building a flexible architecture that could support multiple AI providers without creating dependency issues or exposing sensitive credentials.

I also spent significant time refining the user interface so that complex linguistic information felt approachable rather than overwhelming.

Accomplishments that I'm proud of

I'm proud that Semantix is more than a simple AI wrapper.

Some accomplishments include:

  • Building a complete full-stack application
  • Creating a modular provider architecture
  • Integrating real NLP analysis using spaCy
  • Detecting communication issues such as hedging, passive voice, vague language, and unclear references
  • Developing an explainable workflow rather than a black-box rewrite experience
  • Designing a polished dashboard that presents linguistic insights in an accessible way

Most importantly, I built a system that helps users understand language instead of simply generating it.

What I learned

Through this project I learned:

  • How to combine traditional NLP techniques with modern AI workflows
  • How dependency parsing can be used to identify communication patterns
  • The importance of explainability when building AI products
  • How to design a scalable provider architecture for multiple AI services
  • How linguistic concepts can be translated into practical user-facing features

I also gained experience building and deploying a full-stack monorepo with shared contracts and API-driven architecture.

What's next for Semantix

I see Semantix evolving into a broader linguistic intelligence platform.

Future plans include:

  • Refining the Hugging Face-powered tone and formality classification
  • Persistent analysis history
  • Exportable analysis reports
  • Pinecone-powered retrieval of linguistic examples
  • Resume and cover letter analysis -Language learning and ESL support
  • Slang and dialect evolution analysis
  • Browser and email integrations
  • Multilingual support

Our long-term vision is to help people understand not only what language says, but how language creates meaning, confidence, and intent.

Built With

Share this project:

Updates