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TranscriptAI
Meeting AI that understands Japanese soft rejections
Most meeting AIs miss when someone says no indirectly. TranscriptAI detects nemawashi — Japanese soft rejections like 「検討させてください」(sounds like "I'll consider it", means no). 16 patterns, confidence scored. Keigo formality register via MeCab. Hindi indirect speech. APPI-compliant PII masking. 22% → 94% accuracy across 5 iterations. English · Hindi · Japanese. Live on HuggingFace.
Hi PH! I'm Kunal — a developer from Uttarakhand, India.
I built TranscriptAI because Japanese business communication almost never says "no" directly. 「検討させてください」literally means "let me consider it." It means no. Most NLP systems get this completely wrong.
I spent 2 months studying Japanese corporate linguistics and built 16 nemawashi detection patterns with confidence scoring, keigo register detection via MeCab morphological analysis, and cross-script speaker identity resolution (田中 ↔ Tanaka ↔ Director).
Also handles Hindi indirect speech — 8 patterns, Devanagari and Roman both.
Accuracy went from 22% → 93% across 5 iterations.
Happy to answer any questions — especially on the Japanese NLP side. That's where most of the hard work happened.
🔗 Live demo: huggingface.co/spaces/KunalTheBeast/TranscriptAI
💻 Code: github.com/aiKunalBisht/Transcript-ai
About TranscriptAI on Product Hunt
“Meeting AI that understands Japanese soft rejections”
TranscriptAI was submitted on Product Hunt and earned 6 upvotes and 1 comments, placing #65 on the daily leaderboard. Most meeting AIs miss when someone says no indirectly. TranscriptAI detects nemawashi — Japanese soft rejections like 「検討させてください」(sounds like "I'll consider it", means no). 16 patterns, confidence scored. Keigo formality register via MeCab. Hindi indirect speech. APPI-compliant PII masking. 22% → 94% accuracy across 5 iterations. English · Hindi · Japanese. Live on HuggingFace.
On the analytics side, TranscriptAI competes within Productivity, Developer Tools and Artificial Intelligence — topics that collectively have 1.6M followers on Product Hunt. The dashboard above tracks how TranscriptAI performed against the three products that launched closest to it on the same day.
Who hunted TranscriptAI?
TranscriptAI was hunted by Kunal Bisht. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of TranscriptAI including community comment highlights and product details, visit the product overview.
Kunal Bisht