Golden Gate homepage with two workflows: Off-boarding (capture knowledge) and On-boarding (explore narrative & graph).
Deep-dive analysis across all documents, automatically generating targeted AI interview questions to surface knowledge gaps.
AI-powered knowledge transfer interview, asking targeted questions to extract implicit expertise from the off-boarding employee.
Question preview panel showing the source file and AI deep-dive analysis for context and traceability.
on-boarding narrative page with chatbox, the narrative is based on the interview summary and deep dives.
Agent-generated onboarding knowledge graph from project files and off-boarding AI interviews (dashed = interview, star = file).
AI-generated interview summary consolidating key decisions, risks, dependencies, and extracted facts after the knowledge transfer session.
Onboarding QA agent answering project questions using deep dives and interview summaries as context.

Golden Gate – AI-Powered Knowledge Transfer 🌉

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

work 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
Gap Detection & Smart Question Generation
- AI identifies inconsistencies and missing context
- Prioritizes questions by risk and impact
- References specific files, formulas, and code

gaps

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

interview

interviewing

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
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
Custom file parsers (10+) for:
- 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