Glass Doctor

Key Outcomes

• Employees now get answers in seconds instead of waiting on calls or messages
• Centralized, searchable knowledge base for policies, measurements, and training
• Reduced support burden on senior staff and managers
• Faster onboarding for new employees

Background

Glass Doctor operates with extensive internal documentation, policies, procedures, training materials, and operational references critical to day-to-day work.

Before this project, employees relied on calls and internal messages to get answers. Knowledge lived with a handful of experienced staff, creating constant interruptions, delays, and inconsistent responses.

Leadership needed a secure, internal AI system that could turn existing documents into a reliable, always-available source of truth without exposing data externally or requiring complex workflows.

The Challenge

Business Challenges

• Support overload from repetitive internal questions
• Slow response times blocking employee productivity
• Difficult onboarding for new hires
• Knowledge sprawl across static documents with no effective search

Operational Pain Points

• Employees interrupted others for basic policy or measurement questions
• Inconsistent answers depending on who was contacted
• No visibility into what questions were being asked most often

Technical Constraints

• Internal-only system with strict access control
• AI responses must stay grounded in approved documents
• OCR needed for scanned PDFs
• Context window limits with large document sets

The Solution

Stackup Solutions designed and built Glass Doctor Chatbot, a secure, single-tenant, web-based AI knowledge assistant powered by Retrieval-Augmented Generation (RAG).

The platform converts internal documents into an intelligent, conversational interface while giving administrators full control over users, content, and AI behavior.

Architecture & Technical Decisions

Core Architecture

• Frontend: Next.js for fast, responsive UI
• Backend: Node.js for scalable API handling
• Database: MySQL for users, roles, and chat logs
• Vector Database: Pinecone for high-performance semantic search
• AI Layer: ChatGPT with strict RAG constraints
• Auth: Passport.js + JWT with role-based access

RAG Training Pipeline

• Admin uploads approved documents (PDF, DOC/DOCX)
• OCR extracts text from scanned files when required
• Content is chunked into controlled, context-safe segments
• Chunks are embedded and stored in Pinecone
• Documents become AI-ready automatically after upload

Query & Response Flow

1. User asks a question in the chat interface
2. Query is converted into an embedding
3. Pinecone retrieves the most relevant document chunks
4. Retrieved context is injected into a guarded system prompt
5. ChatGPT generates an answer only from provided context
6. If no relevant content exists, the AI safely declines to guess

This approach prevents hallucinations and ensures every response stays aligned with internal documentation.

Key Features

Admin Panel

• User management (Super Admin, Admin, Employees)
• Secure password resets and account control
• Document & media library with validation and upload progress
• AI training automation on document upload
• Read-only chat logs for auditing and improvement
• Usage metrics dashboard

Employee Experience

• Clean chat interface with conversation history
• Instant answers for policies, measurements, and procedures
• In-app document viewer for PDFs, DOCs, and images
• Secure authentication with profile management

Implementation Process

Discovery & Strategy

• Identified high-friction knowledge workflows
• Defined AI guardrails and security boundaries

Architecture & RAG Design

• Chunking strategy optimized for long operational documents
• OCR tuning to handle inconsistent scans
• Context window constraints carefully managed

Platform Build

• Full admin + user system with role-based access
• Automated AI training pipeline
• Secure logging and audit trails

Launch & Iteration

• Live production deployment
• Prompt refinement using real chat logs
• Continuous improvement without retraining the entire system

Results & Impact

While exact metrics were not formally tracked, the impact was immediate and clear:

• Significant reduction in internal messages and calls
• Employees now get answers in seconds instead of waiting
• Managers and senior staff reclaimed time from repetitive questions
• New hires onboard faster with self-service access to knowledge
• Leadership gained visibility into what employees actually ask

The system is live in production and is considered a successful resolution of the original support and knowledge challenges.