Case study · 02 / 06

OpenBlueprint

From Measurements to Intelligent Blueprints.

Stack: Next.js · TypeScript · Tailwind CSS · Prisma · AI APIs · 3D Visualization
Links: GitHub Live Demo

Fig. 02 — OpenBlueprint · conceptual render

Screenshots

Actual captures from the running application.

Fig. 02.1 — OpenBlueprint interactive 2D blueprint editor

Fig. 02.2 — OpenBlueprint 3D home visualization

Fig. 02.3 — OpenBlueprint construction cost estimator

Overview

An AI-powered architectural planning and preliminary blueprint generation platform for homes — plot dimensions in, editable floor plans out, with 2D editing, 3D visualization, cost estimation, and an AI design assistant.

Problem

Early-stage home planning is guesswork: room needs, plot constraints, light, ventilation, and cost are reasoned about separately, if at all.

How it works

01
Plot measurements
02
Room requirements
03
AI analysis
04
Design strategies
05
2D blueprint + 3D view
06
Cost estimation

OpenBlueprint turns natural-language requirements into structured constraints, generates five design strategies with a zone-based BSP layout engine, and offers a CAD-like 2D editor, clay-render 3D views, INR cost estimation, and a RAG knowledge assistant.

Key features

5 strategies incl. Vastu-compliant + BSP engine
CAD-like 2D editor + 45-item furniture library
Clay-render 3D visualization
INR cost estimator with 4 build grades
Natural-language AI design assistant
RAG architectural knowledge assistant + export

Lessons learned

Lesson 1: Encoding domain rules — zones, ventilation, Vastu — before generating beats freeform output.
Lesson 2: A visible step-by-step pipeline builds trust in AI-assisted design.
Lesson 3: Preliminary layouts must be framed as starting points, never final plans.

My contribution — Designed and built the platform — strategy generation, BSP-based layout logic, 2D editor tooling, 3D visualization, cost estimation, and the AI/RAG assistants.

Note — Preliminary layouts for exploration — not a substitute for a licensed architect.