Remove emoji and em dashes from org card

README.md

@@ -7,15 +7,13 @@ pinned: false
 # IOTEverythin
 
 We build small, production-oriented AI that runs where the data lives. Two
-independent product lines:
-
-- **Voice** — compact Indian-English text-to-speech for conversational agents.
-- **Grounded QA** — extractive question answering that quotes your documents
-  instead of hallucinating.
+independent product lines: compact Indian-English text-to-speech for
+conversational agents, and grounded question answering that quotes your
+documents instead of hallucinating.
 
 ---
 
-# 🎙️ Voice / Text-to-Speech
+# Voice / Text-to-Speech
 
 Compact, production-oriented text-to-speech voices for conversational AI, with a
 focus on Indian-English for customer-support and website voice assistants.
@@ -27,9 +25,9 @@ AI-powered conversational agents across phone and web, with natural-sounding spe
 
 ## The Roxi TTS line
 
-A family of Indian-English voices fine-tuned from the open MOSS-TTS models. Two tiers: the
-0.1B models run in real time for live agents, and the 1.7B model is for pre-rendered or
-premium-quality audio.
+A family of Indian-English voices fine-tuned from the open MOSS-TTS models. Two
+tiers: the 0.1B models run in real time for live agents, and the 1.7B model is for
+pre-rendered or premium-quality audio.
 
 | Model | Base | Best for |
 |---|---|---|
@@ -50,18 +48,18 @@ on each model card.
 
 ---
 
-# 🔒 Grounded QA
+# Grounded QA
 
 [grounded-pointer-qa](https://huggingface.co/IOTEverythin/grounded-pointer-qa) is an
-extractive question-answering model that **cannot hallucinate by construction**: it only
-quotes verbatim spans from your documents, abstains when the answer isn't there, and
-decodes deterministically. Knowledge is hot-swappable — point it at a folder of
-`.txt`/`.md`/`.pdf` files, no retraining.
-
-Built on roberta-base (125M), it runs in milliseconds on consumer hardware: 74.6 EM on
-SQuAD v2 in a full-retrieval setting, and 91.7% answer precision in its "right or silent"
-mode. A natural grounding layer for support agents that must quote policy documents
-instead of improvising.
-
-**Focus:** grounded, verifiable answers; every answer a verbatim quote with a source, or
-an honest abstention; local-first and deterministic.
+extractive question-answering model that **cannot hallucinate by construction**: it
+only quotes verbatim spans from your documents, abstains when the answer is not
+there, and decodes deterministically. Knowledge is hot-swappable: point it at a
+folder of `.txt`/`.md`/`.pdf` files, with no retraining.
+
+Built on roberta-base (125M), it runs in milliseconds on consumer hardware: 74.6 EM
+on SQuAD v2 in a full-retrieval setting, and 91.7% answer precision in its "right or
+silent" mode. A natural grounding layer for support agents that must quote policy
+documents instead of improvising.
+
+**Focus:** grounded, verifiable answers; every answer a verbatim quote with a
+source, or an honest abstention; local-first and deterministic.