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I built Vetra, an AI technical interviewer that makes interviews more structured, adaptive, and realistic.
Vetra conducts real-time voice interviews, explores a candidate’s experience, adapts its questions based on their responses, evaluates performance, and generates structured assessments.
I built the interview orchestration with LangGraph, added real-time voice interaction using Gemini Live, and created a separate evaluation layer for evidence-based scoring.
The goal was to create an AI interview experience that feels more like a real technical conversation than a chatbot.
Jonathan, keeping the evaluation layer separate from the interviewer is a smart choice. When the same model asks and scores, it tends to grade its own questions kindly. How are you handling silence and long pauses on the voice side? That was the hardest part for us on a voice AI build.
AlphaTrace is a crypto whale intelligence and copy-trading platform built around Hyperliquid. It tracks high-performing wallets, analyzes trading activity and market signals, and enables users to discover and follow whale strategies through a modern, data-driven interface.
This is cool!
A strong specialist skill is useless if the wrong system gets invoked. Long-running AI work breaks in boring, expensive ways.
That is not a prompting problem. It is a routing and authority problem.
How I can help you:
I build project routers for teams using multiple AI workflows, agents, or specialist assistants so that they work with each other as a team.
I design source-of-truth resolutions so your agents retrieve the current rules instead of improvising or relying on assumptions or stale chat data.
I create reusable skill registries and specialist handoff logic hardened by determined and dedicated flow states reducing the need for model reliance on its own internal and displaced memory context.
I separate project states with individualized canonical states bound by rules living under essential model instructions and routed properly so unrelated workflows do not contaminate one another, but exist with awareness of the role each project plays, enabling a persistently growing ecosystem to develop organically.
I add governance wrappers, escalation hooks, and recovery mirrors around existing AI stacks so that trust is designed at the root, and variance is eliminated with rule based source archiving and implementation.
Trust means closing your eyes at night and sleeping soundly because your agents got your back. And I can put it all together for you, precision results sourced and undisputed because I got tired of doing things the hard way.
Phillip, agree that routing is where it breaks. In our builds the worst bugs came from an agent pulling an old rule from chat history instead of the current source. Pinning one source of truth per project fixed more than any prompt change did.