Blue Printing Press

Overview

1. The Problem

Traditional Service Blueprinting workshops are resource-intensive, requiring skilled facilitators for 2-4 hour sessions with quality varying by facilitator expertise. They're difficult to scale due to limited facilitator availability, manual note-taking often misses key insights, and post-session synthesis takes days—resulting in 8-16 hours total per blueprint.

2. The Solution

blueprinting.press is an AI-powered facilitation system that guides conversations using best-practice techniques, enforces the Service Blueprinting framework to maintain focus, captures everything in real-time structured format, generates artifacts automatically (live Mermaid diagrams + polished deliverables), and scales infinitely with consistent quality—reducing total time to 2-3 hours per blueprint.

3. Innovation and Features

Multi-Agent Architecture: Specialized AI agents for intent classification (GPT-4o-mini), facilitation, domain expertise, validation, and document generation working in coordination

RAG-Enhanced Context: Upload client documents (PDF, DOCX, MD) that are indexed and retrieved contextually during facilitation

Real-Time Visualization: Live-updating Mermaid diagrams as users provide input, showing the blueprint being built

State-Driven Workflow: Guided progression through Customer Actions → Frontstage → Backstage → Support Processes with validation at each stage

Session Management: Pause/resume capabilities with full conversation history preservation

4. Libraries and Frameworks

Backend: Python 3.11+, FastAPI, LangChain, SQLAlchemy, Pydantic

AI/ML: OpenAI GPT-4o/GPT-4o-mini, ChromaDB (vector database), text-embedding-3-small

Frontend: React, Next.js 14, Tailwind CSS, Mermaid.js, Lucide icons

Infrastructure: Docker, PostgreSQL, Uvicorn

Design Process Summary

The design followed an API-first, multi-agent architecture approach. We started by defining clear user personas (service design facilitators and client stakeholders) and established success metrics targeting a 70% reduction in workshop time. The system was decomposed into specialized agents—each responsible for a distinct task (intent parsing, facilitation, domain expertise, validation)—coordinated through an orchestration layer with a well-defined state machine. RAG was integrated early to enable contextual facilitation using client documents. The frontend was designed around a workshop metaphor with real-time diagram feedback, while the backend prioritized scalability through Docker containerization and database abstraction (SQLite for MVP, PostgreSQL for production).

Overview

1. The Problem

2. The Solution

3. Innovation and Features

4. Libraries and Frameworks

Design Process Summary

Tech Stack