HackStack

Project Info

Golden Gate

Devpost

Inspiration

Many organizations face the same structural challenge: tribal and tacit knowledge are critical assets, yet they remain hidden, fragmented, and difficult to reuse. Key decisions are often shaped by unwritten assumptions, and dependencies are understood informally rather than explicitly documented. During workforce transitions – employee turnover, project handoffs, or re-org – that contextual layer is often lost. As a result, teams spend lots of time reconstructing context instead of moving forward. Golden Gate was built to address this discontinuity. We systematically preserve and transfer institutional intelligence. When people move, organizational context should remain accessible.

What it does

Golden Gate is an agentic knowledge preservation and transfer platform. The system analyzes project artifacts in iterative passes to surface implicit assumptions, decision logic, and potential knowledge gaps. It then conducts structured, context-aware interviews with the departing employee to clarify reasoning, resolve inconsistencies, and formalize tacit understanding. The output is a structured onboarding package designed to accelerate continuity. This includes: A synthesized summary of project artifacts A structured summary of interview insights A knowledge graph to visualize relationships and dependencies An interactive Q&A agent trained on the preserved knowledge The goal is to make institutional context accessible to the incoming team member, reducing time spent reconstructing prior thinking and enabling a smoother transition. Core Flow Intelligent File Analysis Upload project artifacts (Excel, Python, SQL, Jupyter, PDFs, PowerPoints, etc.) Multi-pass deep-dive analysis extracts: Documented logic Hidden dependencies Undocumented assumptions Knowledge gaps Intelligent File Analysis Upload project artifacts (Excel, Python, SQL, Jupyter, PDFs, PowerPoints, etc.) Multi-pass deep-dive analysis extracts: Documented logic Hidden dependencies Undocumented assumptions Knowledge gaps Documented logic Hidden dependencies Undocumented assumptions Knowledge gaps Gap Detection & Smart Question Generation AI identifies inconsistencies and missing context Prioritizes questions by risk and impact References specific files, formulas, and code Gap Detection & Smart Question Generation AI identifies inconsistencies and missing context Prioritizes questions by risk and impact References specific files, formulas, and code Context-Aware Conversational Interview Conducts natural, structured interviews Dynamically discovers new gaps in real time Maintains cross-file awareness Extracts structured facts with confidence scoring Conducts natural, structured interviews Dynamically discovers new gaps in real time Maintains cross-file awareness Extracts structured facts with confidence scoring Onboarding & Transfer Package Generation Synthesizes knowledge into organized documentation: Decisions Rules Dependencies Risks Historical reasoning Enhances original project files with extracted insights Trains a living AI assistant on the full knowledge base Onboarding & Transfer Package Generation Synthesizes knowledge into organized documentation: Decisions Rules Dependencies Risks Historical reasoning Decisions Rules Dependencies Risks Historical reasoning Enhances original project files with extracted insights Trains a living AI assistant on the full knowledge base Living Knowledge Agent New hires and teammates interact with an AI trained on the preserved knowledge Provides source citations and confidence levels Keeps knowledge searchable and durable Living Knowledge Agent New hires and teammates interact with an AI trained on the preserved knowledge Provides source citations and confidence levels Keeps knowledge searchable and durable Golden Gate doesn’t just store documents — it preserves intelligence.

How we built it

Backend LangGraph for multi-step agentic workflows with human-in-the-loop interrupt/resume Python + FastAPI for API routing and streaming OpenAI GPT-5.2 powering: Deep-dive analysis Question generation Conversational interviewing Knowledge synthesis Deep-dive analysis Question generation Conversational interviewing Knowledge synthesis Custom file parsers (10+) for: Excel formulas Python AST SQL schemas Jupyter notebooks Structured PDFs and presentations Excel formulas Python AST SQL schemas Jupyter notebooks Structured PDFs and presentations Frontend Next.js 14 + TypeScript Tailwind CSS Server-Sent Events (SSE) for real-time pipeline visualization: Parse → Deep Dive → Gap Detection → Questions → Interview → Synthesis Technical Innovation Multi-pass analysis per file (structure → critique → tacit extraction) Cross-file reasoning Dynamic question backlog generation Structured LLM-synthesized summaries instead of raw transcripts Real-time AI progress streaming

Challenges we ran into

Maintaining conversational quality across 50K+ token project contexts Managing LangGraph state reducers during interrupt/resume cycles Streaming real-time AI events to the frontend without memory leaks Deduplicating knowledge gaps across multiple artifacts without losing nuance Designing prompts that extract tacit reasoning rather than generic explanations

Accomplishments we're proud of

Built a production-ready, multi-agent workflow system in 36 hours Implemented 10+ working file parsers Created a fully context-aware conversational interview engine Achieved live, real-time AI progress visualization Zero mock backend flows — fully functional pipeline Designed specifically for conversational excellence in the Decagon track Established strong product branding around knowledge continuity 🌉

What we learned

Most critical knowledge lives in the gaps between files Context depth dramatically improves conversational intelligence Agentic workflows outperform linear prompt chains for complex reasoning Real-time streaming significantly improves trust and UX Structured synthesis is more valuable than raw transcripts Knowledge preservation is a universal problem — not just an HR problem

What's next

Enhanced AI Capabilities Voice/video knowledge capture Multi-employee knowledge synthesis Automated knowledge graph generation Enterprise Integrations Slack and Teams triggers for transition events HRIS integrations (Workday, BambooHR) Compliance tracking (SOX, GDPR) Multi-tenant SaaS with role-based access control Advanced Onboarding Adaptive onboarding paths by role Knowledge verification quizzes Suggested peer connections Continuous Knowledge Management Living documentation that updates over time Proactive gap detection before transitions occur Knowledge health scores for teams and projects Golden Gate’s vision is to make knowledge preservation automatic. Preserve knowledge. Power every transition. 🌉

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • AnthropicIn code
  • CSSIn code
  • FastAPIIn code
  • JavaScriptIn code
  • Next.jsIn code
  • OpenAIIn code
  • PythonIn code
  • ReactIn code
  • SQLIn code
  • Tailwind CSSIn code
  • TypeScriptIn code

11 of 11 appear in the indexed code.

AI coding agents

  • Claude CodeConfig
  • CursorConfig

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

782 KB

Source files

127

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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