Project concept

We are building a realtime voice to agent system designed to behave like an active companion during live interactions. The goal is to create an AI layer that listens, understands context, extracts intent, and coordinates actions automatically while the user stays focused on the conversation or workflow. We want something that feels natural and genuinely helpful, not another chatbot window that requires manual prompting.

Live communication is still mostly unstructured, ephemeral, and cognitively heavy. Important details are often lost, follow ups are forgotten, and a lot of human attention is spent on remembering instead of deciding. We believe realtime AI can fundamentally improve this space by offering an always-on layer that captures meaning, organizes information, and acts when needed without slowing the user down. If AI can remove the cognitive overhead of live conversations, it can reshape how both individuals and teams make decisions.

Our starting point is calls because they concentrate high-value information in a fast and chaotic format. People think, speak, negotiate, decide, and recall details at the same time. A companion that can keep up with this pace could transcribe in realtime, detect key signals, generate insights, trigger follow up actions, and prepare structured outputs without interrupting the flow. The result is a reduction in cognitive load and a clear upgrade in communication quality.

This idea is only our entry point. We are not limiting the project to a call assistant. What interests us is the broader interface created by speech plus reasoning plus automation. The same engine could support research, planning, onboarding, sales operations, recruiting calls, customer support, or personal coordination tasks. We want to explore this space openly and see how far an autonomous agent can go when it operates live instead of waiting for prompts. The long-term vision is an AI that can understand workflows in motion and assist across many contexts with minimal user effort.

We also want to investigate the underlying agent architecture. Realtime systems require the agent to balance immediate reactions with deeper reasoning, maintain a short-term memory while storing longer-term context, and choose when to act versus when to remain silent. This creates interesting design challenges around intent detection, action orchestration, confidence thresholds, multi-step reasoning, and surfacing information at the right moments. We want to explore how an agent can operate with autonomy while still keeping the user in control.

We focus on building an AI native experience that feels smooth and intuitive. The agent should remain present without being intrusive, smart without being overwhelming, and adaptive to the user’s intent rather than scripted. We prefer fast iteration, simple architectures, and validation through real interactions. The hackathon is the ideal environment to pressure test our assumptions and see what emerges from realtime behavior. By experimenting quickly, we want to understand how reliable and independent such an agent can become.

We want to stay flexible. If the call use case proves strong, we will push it further. If we discover a more impactful workflow, we will pivot. If another team inspires a better angle, we will explore it. Our goal is to build something that feels alive, valuable, and capable of evolving into a scalable AI companion that a single person could operate and grow. A key part of this experiment is evaluating what elements can be automated end to end, what requires oversight, and how much operational complexity the system introduces or removes.

Goals

Our goals help us explore both the product and the underlying AI systems. They guide what we build during the hackathon while keeping us open to new directions that emerge along the way.

• build a working realtime voice to agent pipeline
• explore context extraction, reasoning patterns, and autonomous action triggering
• design an interface that feels smooth, intuitive, and easy to demo live
• test the capabilities across multiple workflows, not only calls
• explore how AI native UX changes traditional product design
• observe how people interact with realtime reasoning systems
• create a modular foundation that can evolve beyond the weekend
• keep the system simple enough for one person to operate and scale
• validate whether this direction has clear market pull and real problem fit

We expect these goals to adapt as we prototype. The intention is to learn fast and follow the most promising signals.

How others can contribute

We welcome builders, designers, and product thinkers interested in new interaction models.

You can help by shaping user flows, testing scenarios, improving reasoning and context handling, or giving UX and narrative feedback.