Inspiration
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Jarvis
Inspiration
Voice AI feels natural to use, but creating a reliable voice application still requires developers to connect streaming audio, speech recognition, reasoning, knowledge retrieval, and speech synthesis. We built Jarvis to hide that complexity behind a simple conversation.
What it does
Jarvis lets users describe the voice assistant they need in everyday language. It turns that request into a personalized assistant with a defined role, instructions, knowledge, and actions, then makes it available as an interactive real-time voice experience. Instead of starting with APIs and configuration files, builders can prototype by speaking.
How we built it
Jarvis uses a Python orchestration layer to manage conversations and the assistant-generation workflow. LiveKit handles real-time audio, Deepgram converts speech to text, and an OpenAI model interprets intent and plans the assistant. Retrieval-augmented generation supplies relevant context, while Rime produces spoken responses. Langfuse helps us observe and improve prompts, Azure hosts supporting services, and search integration lets assistants retrieve current public information when needed.
Challenges we ran into
Our hardest challenge was reducing end-to-end latency while preserving accurate reasoning. We also had to handle interruptions, maintain conversational context, generate consistent assistant configurations, and prevent irrelevant retrieved information from affecting responses.
Accomplishments that we're proud of
We created an end-to-end prototype that transforms a spoken idea into a usable voice assistant. Jarvis combines real-time conversation, contextual knowledge, and tool-driven actions in an experience designed for both technical and non-technical users.
What we learned
We learned how streaming audio, speech models, language models, and retrieval systems behave as one real-time pipeline. Small choices in prompts, chunking, observability, and turn handling made a major difference to responsiveness and reliability.
What's next for Jarvis
Next, we plan to add reusable assistant templates, multilingual voices, stronger safety controls, deeper integrations, collaborative editing, and one-click deployment across phone and web channels.
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