Restore org card, add grounded-pointer-qa section

README.md

@@ -1,35 +1,53 @@
+---
+title: IOTEverythin
+sdk: static
+pinned: false
+---
+
 # IOTEverythin
 
-We build **small, trustworthy AI that runs where the data lives** — on your
-own hardware, over your own documents, with no cloud in the loop.
+We build compact, production-oriented text-to-speech voices for conversational AI, with a
+focus on Indian-English for customer-support and website voice assistants.
+
+## VozVox
+
+These voices power [VozVox](https://www.vozvox.com/), our voice agent platform for
+AI-powered conversational agents across phone and web, with natural-sounding speech.
+
+## 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.
 
-## What we care about
+| Model | Base | Best for |
+|---|---|---|
+| roxi-tts-pro | MOSS-TTS-Local 1.7B | Highest quality and intelligibility, offline or premium |
+| roxi-tts-v3.1 | MOSS-TTS-Nano 0.1B | Real-time, current best small voice |
+| roxi-tts-v3 | MOSS-TTS-Nano 0.1B | Earlier single-speaker voice |
+| roxi-tts-v2 | MOSS-TTS-Nano 0.1B | First Roxi release |
+| roxi-tts-v2-onnx | ONNX build of v2 | CPU inference, no transformers dependency |
+| voxi-tts | MOSS-TTS-Nano 0.1B | Original prototype voice |
 
-- **Grounded over fluent.** Models should prove their answers, not perform
-  them. Every answer with a receipt; every unknown admitted.
-- **Local-first.** Consumer GPUs and edge devices are enough for
-  verification-grade AI — your documents never leave your machine.
-- **Honest evaluation.** Held-out calibration, published negative results,
-  and limitations sections that pull no punches.
+## Grounded QA
 
-## Featured work
+[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.
 
-### 🔒 [grounded-pointer-qa](https://huggingface.co/IOTEverythin/grounded-pointer-qa)
+## Focus
 
-A question-answering model that **cannot hallucinate by construction**: its
-output layer can only quote verbatim spans from *your* documents, a trained
-abstention head refuses when the answer isn't there, and decoding is fully
-deterministic. Knowledge is hot-swappable — point it at a folder of
-`.txt`/`.md`/`.pdf` files and it answers from those, no retraining.
+- Indian-English accent, natural and telephony-aware
+- Small and fast, built on commercially permissive Apache-2.0 base models
+- Grounded, verifiable answers for agents that quote documents rather than improvise
+- Single-speaker branded voices for support calls and website assistants
 
-- 74.6 EM on SQuAD v2 (held-out, full-retrieval setting)
-- **91.7% answer precision** in "right or silent" mode
-- 125M params — milliseconds per answer on a consumer GPU
+## Attribution
 
-```python
-from modeling_proqa import GroundedQA
-qa = GroundedQA("proqa.pt")
-qa.load_folder("my/documents")
-qa.ask("when does the contract expire?")
-# {'answer': '30 November 2026', 'confidence': 0.98, 'source': '...'}
-```
+Models are built on MOSS-TTS (Apache-2.0). Training data includes the IIT-Madras Indic TTS
+English set, used with the required copyright notice shown on each model card.