🔬 Generate photo-based construction cost estimates with GPT-4 Vision and DDC CWICR

⚡ 38 views · 🔬 Document Extraction & Analysis

💡 Pro Tip — HTTP Request scraping tends to break when sites update their markup. If you’re scraping a major platform, check if ScraperNode covers it — it has maintained scrapers for LinkedIn, Instagram, TikTok, YouTube, and 20+ other platforms that return structured data.

Description

Upload a construction photo via web form → get a detailed cost estimate with work breakdown, resource costs, and professional HTML report. Powered by GPT-4 Vision and the open-source DDC CWICR database (55,000+ work items).

Who’s it for

Site managers who need quick estimates from mobile photos
Renovation contractors evaluating project scope from initial site visit
Real estate inspectors estimating repair costs
Construction consultants providing rapid ballpark figures
DIY enthusiasts planning home improvement budgets

What it does

Collects photo + region/language via n8n Form
Analyzes photo with GPT-4 Vision (room type, elements, dimensions)
Decomposes visible elements into construction work items
Searches DDC CWICR vector database for matching rates
Generates professional HTML report with cost breakdown

Supports 9 regions: 🇩🇪 Berlin · 🇬🇧 Toronto · 🇷🇺 St. Petersburg · 🇪🇸 Barcelona · 🇫🇷 Paris · 🇧🇷 São Paulo · 🇨🇳 Shanghai · 🇦🇪 Dubai · 🇮🇳 Mumbai

How it works

┌──────────────┐    ┌───────────────┐    ┌───────────────┐    ┌──────────────┐
│  Web Form    │ →  │  STAGE 1      │ →  │  STAGE 4      │ →  │  Loop Works  │
│  Photo+Lang  │    │  GPT-4 Vision │    │  Decompose    │    │  per item    │
└──────────────┘    └───────────────┘    └───────────────┘    └──────────────┘
                           ↓                     ↓                    ↓
                    ┌─────────────────────────────────────────────────────┐
                    │  Identify room, elements, fixtures, dimensions      │
                    │  → Break down into 15-40 construction work items    │
                    └─────────────────────────────────────────────────────┘
                                                                     ↓
┌──────────────┐    ┌───────────────┐    ┌───────────────┐    ┌──────────────┐
│  HTML Report │ ←  │  STAGE 7.5    │ ←  │  STAGE 5      │ ←  │  Qdrant      │
│  Response    │    │  Aggregate    │    │  Parse+Score  │    │  Vector DB   │
└──────────────┘    └───────────────┘    └───────────────┘    └──────────────┘

Pipeline stages:

Stage	Node	Description
1	GPT-4 Vision	Analyzes photo: room type, elements, materials, dimensions
4	GPT-4 Decompose	Breaks elements into work items with quantities
5	Vector Search + Score	Finds matching rates in DDC CWICR, quality scoring
7.5	Aggregate & Validate	Sums costs, groups by phase, validates results
9	HTML Report	Generates professional estimate document

Prerequisites

Component	Requirement
n8n	v1.30+ with Form Trigger support
OpenAI API	GPT-4 Vision + Embeddings access
Qdrant	Vector DB with DDC CWICR collections
DDC CWICR Data	github.com/datadrivenconstruction/DDC-CWICR

Setup

1. n8n Credentials (Settings → Credentials)

OpenAI API — required (GPT-4 Vision + text-embedding-3-large)
Qdrant API — your Qdrant instance connection

2. Qdrant Collections

Load DDC CWICR embeddings for your target regions:

DE_BERLIN_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
ENG_TORONTO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
RU_STPETERSBURG_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
ES_BARCELONA_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
FR_PARIS_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
PT_SAOPAULO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
ZH_SHANGHAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
AR_DUBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR
HI_MUMBAI_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR

3. Activate Workflow

Import JSON into n8n
Link OpenAI + Qdrant credentials to respective nodes
Activate workflow
Access form at: https://your-n8n/form/photo-estimate-pro-v3

Features

Feature	Description
📸 Photo Analysis	GPT-4 Vision identifies room type, elements, fixtures
📏 Dimension Estimation	Uses reference objects (doors, tiles) for sizing
🔧 Work Decomposition	Breaks down to 15-40 specific work items
🎯 Quality Scoring	Rates match quality (high/medium/low/not_found)
📊 Phase Grouping	PREPARATION → MAIN → FINISHING → MEP
💰 Cost Breakdown	Labor, materials, machines per item
✅ Validation	Warns if <50% rates found or missing demolition
🌍 9 Languages	Full localization + regional pricing

Form Fields

Field	Type	Options
📷 Upload Photo	File	.jpg, .png, .webp
🌍 Region & Language	Dropdown	9 regions with currencies
🏗️ Work Type	Dropdown	New / Renovation / Repair / Auto
📝 Description	Textarea	Optional context

Example Output

Input: Bathroom photo (renovation)
Region: 🇩🇪 German - Berlin (EUR €)

Generated Work Items:

PREPARATION (3 items)
├── Demolition of wall tiles — 12 m² — €180
├── Demolition of floor tiles — 4.5 m² — €95
└── Disposal of construction waste — 0.8 m³ — €120

MAIN (8 items)
├── Floor waterproofing — 4.5 m² — €225
├── Wall waterproofing wet zone — 8 m² — €280
├── Floor screed — 4.5 m² — €135
├── Wall tiling — 22 m² — €880
├── Floor tiling — 4.5 m² — €225
├── Toilet installation — 1 pcs — €320
├── Sink installation — 1 pcs — €185
└── Shower cabin installation — 1 pcs — €450

FINISHING (3 items)
├── Ceiling painting — 4.5 m² — €68
├── Grouting — 26.5 m² — €133
└── Silicone sealing — 8 m — €48

MEP (4 items)
├── Socket installation — 2 pcs — €90
├── Light point installation — 2 pcs — €120
├── Mixer/faucet installation — 2 pcs — €160
└── Ventilation installation — 1 pcs — €85

─────────────────────────────────────
TOTAL: €3,799.00
Labor: €1,520 · Materials: €1,900 · Machines: €379
Quality: 78% high match · 18 work items

Quality Scoring System

Score	Level	Meaning
60-100	🟢 High	Exact match with resources
40-59	🟡 Medium	Good match, minor differences
20-39	🟠 Low	Partial match, review needed
0-19	🔴 Not Found	No suitable rate found

Scoring factors:

Has price in database (+30)
Has resources breakdown (+25)
Unit matches expected (+20)
Material keywords match (+15)
Work type keywords match (+10)
Vector similarity >0.5 (+10)

Notes & Tips

Best photo angles: Capture full room, include reference objects (doors, sockets)
Renovation mode: AI automatically adds demolition works
Validation warnings: Check if <50% rates found — may need manual additions
Rate accuracy: Depends on DDC CWICR coverage for your region
Extend: Chain with PDF generation, email delivery, or CRM integration

Author

DataDrivenConstruction.io
https://DataDrivenConstruction.io
info@datadrivenconstruction.io

Consulting & Training

We help construction, engineering, and technology firms implement:

AI-powered visual estimation systems
CAD/BIM data processing pipelines
Vector database integration for construction data
Multilingual cost database solutions

Contact us to test with your data or adapt to your project requirements.

Resources

DDC CWICR Database: GitHub
Qdrant Documentation: qdrant.tech/documentation
n8n Form Trigger: docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.formtrigger

⭐ Star us on GitHub! github.com/datadrivenconstruction/DDC-CWICR

🔗 Nodes Used

Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model, n8n Form Trigger, Qdrant Vector Store

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup