My Role

As the Architect & Lead Developer, I was responsible for
• Designing the system architecture for knowledge structuring and AI integration
• Developing workflows to convert scattered information into structured datasets
• Implementing Retrieval-Augmented Generation (RAG) pipelines to enhance AI readiness

‍

Software and Tools

• AI & ML Frameworks: LangChain, LlamaIndex, OpenAI API
• Knowledge Management: Obsidian
• Data Processing & Search: Vector databases (MongoDB NoSQL)
• Automation & Integration: Python, Docker, ZenML
• Collaboration & Documentation: Dropbox, Google Docs, Confluence

‍

Research Methods

• User Research & Pain Point Analysis: Interviewed engineers to understand onboarding and knowledge transfer challenges
• Competitive Analysis: Studied enterprise knowledge management solutions (e.g., Confluence, Guru)
• AI Readiness Assessment: Evaluated structured vs. unstructured data bottlenecks in engineering workflows
• Prototyping & Iterative Development: Building MVPs for RAG-based search and documentation automation

‍

Objectives & Constraints

Objectives:
• Centralize and structure fragmented engineering knowledge
• Enable rapid knowledge retrieval through AI-powered search
• Automate documentation and process standardization

Constraints:
• Scattered data sources—Information was stored in emails, local drives, notebooks, and legacy systems
• Context dependency—Engineering knowledge often relies on tribal expertise rather than structured documentation
• Scalability challenges—The system needed to handle large datasets and diverse file formats
• Security & IP Protection—Ensuring confidentiality and access control for sensitive company data

‍

Stakeholders & Collaborations

• Engineering Teams – Primary users, providing feedback on system usability
• AI & Automation Experts – Assisting with LLM fine-tuning and deployment
• Product & IT Teams – Ensuring security, compliance, and infrastructure scalability
• Company Leadership – Supporting AI readiness and long-term adoptioan

‍

Challenges & Impact

Challenges:
• Engineering teams wasted time searching for information across multiple platforms
• Onboarding was slow due to siloed expertise and lack of structured knowledge
• Process inefficiencies led to repeated mistakes, knowledge loss, and miscommunication
• Existing documentation efforts were unstructured and inconsistent
• You gotta know a guy that knows a gal to find who has the knowledge you need.

The Impact:
• Faster onboarding—New engineers ramp up quickly with AI-assisted knowledge retrieval
• Reduced knowledge loss—Structured documentation preserves expertise
• Optimized workflows—Automated processes eliminate redundant tasks
• AI-powered insights—Predictive analytics help teams make data-driven decisions
• You can find others who have worked on things similar to you for leveraging existing work instead of reinventing the wheel.

‍

Approach & Methodology

Step 1: Structuring Scattered Knowledge
• Develop an automated ingestion pipelines to pull data from Dropbox, emails, logs, and Obsidian notebooks
• Apply an NLP-based categorization to label and structure documents

Step 2: AI-Enhanced Search & Retrieval
• Implement Retrieval-Augmented Generation (RAG) to enable natural language queries
• Integrate vector databases to power semantic search over engineering documentation

Step 3: Automation & Pipeling Deployment
• Create ingestion and retrieval pipelines

Step 4: Scaling & AI Readiness
•  Develop a chat interface to chat with existing knowledge base and to work as a copilot helping outline and plan your work.

‍

Execution & Implementation

• Convertunstructured data into a structured knowledge repository
• Develop an AI-powerered semantic search system to extract and summarize information
• Build automation workflows to sync knowledge bases nightly.
• Design a chat interface to query existing knowledge

‍

Outcomes & Impact

Key Success Metrics:
• Looking to reduced onboarding time for new engineers by 40%
• Increase documentation adoption and structured knowledge sharing
• Automate retrieval of design insights, reducing time spent searching for past work
• Scalable AI framework enabling future expansion into automating existing processes with the assistance of GenAI

‍
Long-Term Impact:
• Less reliance on tribal knowledge, reducing expertise loss
• Faster innovation cycles by enabling data-driven design decisions
• AI-driven optimization—automating design, verification, and process improvements

Key Takeaways & Personal Growth

• AI-enhanced knowledge management bridges the gap between expertise and automation
• Structured documentation is the foundation for AI adoption in engineering
• AI readiness requires cultural shifts, not just technical tools

‍

Industry & Career Impact

• Hive Mind sets a new standard for AI-driven engineering knowledge management
• Automating knowledge retrieval accelerates innovation and reduces costly mistakes
• AI-powered systems will define the future of engineering collaboration