AI-powered interview analytics platform that evaluates candidates using computer vision, NLP, and speech analysis to generate data driven performance insights.
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Updated
Jul 17, 2026 - Python
AI-powered interview analytics platform that evaluates candidates using computer vision, NLP, and speech analysis to generate data driven performance insights.
AI-powered interview transcript analysis platform built with FastAPI, LangGraph, and Next.js 15. Transform interview transcripts into actionable insights using Claude AI with timeline extraction, entity recognition, and sentiment analysis.
给用户研究员的AI分析助手:通读所有资料,找盲区,查矛盾,给可验证的结论。claude代码插件
AI-powered customer interview analysis tool — semantic search, insight extraction, and research synthesis built for the Great Question stack.
An AI research assistant for qualitative in-depth interviews — from a stakeholder brief to a finished report, driven by one conversation. 13 stages, ~25 composable skills, grounded-theory analysis, two modes (assistive/autonomous).
Evidence-grounded interview analysis skill for Claude — verbatim-checked quotes, reliability council, cross-interview insights. RU + EN.
DINOH Evaluation: synthetic examples and evaluation methods for oral-history assistance. Placeholder model backend; no model-performance results. OHPIPE is developed separately.
将用户访谈转录稿整理为可回溯的分析草稿,并标出需要核对的疑点。A skill for evidence-linked interview analysis and review.
TACIT: LLM-assisted qualitative coding that keeps the record: where the codebook came from, what the model proposed, and what the researcher changed. Python + Streamlit; Gemini, Ollama or any OpenAI-compatible server; transcripts can stay on your machine. pip install tacit-qda
면접 연습과 분석을 위한 웹·AI 에이전트 결합 프로젝트
A production-ready multimodal AI system that analyzes video interviews using speech, NLP, and facial cues to generate explainable hiring intelligence.
A multi-agent system for qualitative content analysis based on Mayring's method. Combines OpenAI agents and vector search to automate segmentation, coding, and category refinement of interview data.
Know what to build next from your customer interviews
Multimodal AI system for automated interview analysis, using LLMs for transcript-based summarization, evaluator-style feedback, and comparative model analysis.
This is a serverless AI workshop project built with AWS CDK (Python) that demonstrates cloud infrastructure for AI applications for the Workshop
Full-stack AI interview analysis MVP using FastAPI, Whisper, NLP scoring, PostgreSQL, Next.js, and Streamlit.
Local-first recording-to-transcript pipeline with structured extraction and optional Notion publishing.
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