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![[On-Device AI Chatbot] Part 10: The Future of On-Device AI and TecAce's Roadmap (Conclusion)](https://static.wixstatic.com/media/2ea07e_d1771a9889764093a8c855756693ba51~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_35,blur_30,enc_avif,quality_auto/2ea07e_d1771a9889764093a8c855756693ba51~mv2.png)
![[On-Device AI Chatbot] Part 10: The Future of On-Device AI and TecAce's Roadmap (Conclusion)](https://static.wixstatic.com/media/2ea07e_d1771a9889764093a8c855756693ba51~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_95,enc_avif,quality_auto/2ea07e_d1771a9889764093a8c855756693ba51~mv2.png)
[On-Device AI Chatbot] Part 10: The Future of On-Device AI and TecAce's Roadmap (Conclusion)
The Future of On-Device AI and TecAce's Roadmap Throughout this 9-part series, we have chronicled the entire journey of developing an on-device chatbot—a solution to cloud cost and data security issues. We covered everything from selecting a Small Language Model (SLM) and applying quantization, integrating offline STT/TTS, building local RAG, to rigorously validating quality using AI SuperVision and overcoming hardware performance constraints. In this grand finale, Part 10,
Mar 2
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