1. Our Mission

At HinglishAI (accessible at https://englishtohinglish.com), our mission is to build intelligent, culturally resonant language tools that reflect how modern bilingual communities actually communicate. Over 500 million people worldwide use Hinglish as their primary vernacular for digital commerce, social interaction, video creation, and daily communication.

Traditional machine translation tools were architected around rigid monolingual paradigms, often forcing everyday English phrases into archaic, literary Hindi that feels disconnected from contemporary speech. We designed our platform from the ground up to support natural code-switching by combining NLP Part-of-Speech tagging with neural machine translation models trained on real conversational data.

2. The Team Behind HinglishAI

HinglishAI was created and is maintained by a small team of bilingual engineers and computational linguistics researchers based in India. Each member brings specialized experience in South Asian language processing, full-stack web development, and applied NLP research.

Arjun Nair — Founder & Lead Engineer

Arjun is a software engineer with a background in natural language processing and full-stack web development. He built the original English to Hinglish translation pipeline to solve a genuine personal pain point: no existing translation tool correctly handled the code-switching patterns that urban Indians actually use in everyday conversation. He leads all core engine development and API architecture.

Priya Sharma — Computational Linguistics Advisor

Priya holds a postgraduate degree in linguistics with a specialization in South Asian language contact phenomena. She advises on the linguistic accuracy of the translation pipeline — including verb conjugation rules, the Matrix Language Frame model implementation, grammatical gender handling, and honorific-level distinctions across Hindi registers. She reviews and refines the noun preservation lexicon on an ongoing basis.

Editorial & QA Contributors

Translation quality is verified by a rotating team of bilingual Hindi-English editors — native speakers who review output samples weekly against real conversational benchmarks drawn from social media, WhatsApp group data (anonymized), Bollywood dialogue corpora, and digital news sources. These editors flag regressions, verify slang accuracy, and maintain our growing synonym and idiom database.

3. Our Linguistic Technology

Our multi-stage translation pipeline is built around the Matrix Language Frame (MLF) model from computational sociolinguistics:

NLP Entity & Noun Extraction: We isolate English nouns, proper nouns, compound entities (e.g. laptop, meeting, video, feedback), and modern technological terms that should remain in English to produce natural Hinglish.
Neural Clause Translation: We translate the underlying syntactic frame into Hindi, preserving accurate Subject-Object-Verb (SOV) order, verb conjugations, tense agreement, and case markers (ko, se, mein, par, ka, ke, ki).
Adaptive Re-substitution: Extracted English nouns are dynamically re-integrated into the Hindi sentence, producing natural, colloquial Hinglish grammar.
Dual-Script Rendering: Users toggle between standard Devanagari script and phonetic Roman Latin script with a single click — both are generated in one API pass.

4. Our Values

Free & Accessible: 100% free with no paywalls, hidden limits, or forced registration.
Privacy-First: Zero text logging. All translation queries are processed in-memory on edge serverless workers and immediately discarded.
Linguistically Authentic: Continuously refined with real-world conversational datasets, social media trends, and colloquial idioms across South Asian bilingual communities.
High Performance: Sub-500ms latency powered by Cloudflare global edge infrastructure.

5. Get in Touch

Have questions, linguistic feedback, or enterprise API inquiries? Visit our Contact Page or reach out directly at support@englishtohinglish.com. We read every message and respond within 2 business days.