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

Upload a construction photo via web form โ†’ 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: ๐Ÿ‡ฉ๐Ÿ‡ช Berlin ยท ๐Ÿ‡ฌ๐Ÿ‡ง Toronto ยท ๐Ÿ‡ท๐Ÿ‡บ St. Petersburg ยท ๐Ÿ‡ช๐Ÿ‡ธ Barcelona ยท ๐Ÿ‡ซ๐Ÿ‡ท Paris ยท ๐Ÿ‡ง๐Ÿ‡ท Sรฃo Paulo ยท ๐Ÿ‡จ๐Ÿ‡ณ Shanghai ยท ๐Ÿ‡ฆ๐Ÿ‡ช Dubai ยท ๐Ÿ‡ฎ๐Ÿ‡ณ Mumbai

How it works

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ Web Form โ”‚ โ†’ โ”‚ STAGE 1 โ”‚ โ†’ โ”‚ STAGE 4 โ”‚ โ†’ โ”‚ Loop Works โ”‚ โ”‚ Photo+Lang โ”‚ โ”‚ GPT-4 Vision โ”‚ โ”‚ Decompose โ”‚ โ”‚ per item โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ†“ โ†“ โ†“ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ Identify room, elements, fixtures, dimensions โ”‚ โ”‚ โ†’ Break down into 15-40 construction work items โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ†“ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ HTML Report โ”‚ โ† โ”‚ STAGE 7.5 โ”‚ โ† โ”‚ STAGE 5 โ”‚ โ† โ”‚ Qdrant โ”‚ โ”‚ Response โ”‚ โ”‚ Aggregate โ”‚ โ”‚ Parse+Score โ”‚ โ”‚ Vector DB โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

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 โ†’ Credentials) OpenAI API** โ€” required (GPT-4 Vision + text-embedding-3-large) Qdrant API** โ€” 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 | |---------|-------------| | ๐Ÿ“ธ Photo Analysis | GPT-4 Vision identifies room type, elements, fixtures | | ๐Ÿ“ Dimension Estimation | Uses reference objects (doors, tiles) for sizing | | ๐Ÿ”ง Work Decomposition | Breaks down to 15-40 specific work items | | ๐ŸŽฏ Quality Scoring | Rates match quality (high/medium/low/not_found) | | ๐Ÿ“Š Phase Grouping | PREPARATION โ†’ MAIN โ†’ FINISHING โ†’ MEP | | ๐Ÿ’ฐ Cost Breakdown | Labor, materials, machines per item | | โœ… Validation | Warns if <50% rates found or missing demolition | | ๐ŸŒ 9 Languages | Full localization + regional pricing |

Form Fields

| Field | Type | Options | |-------|------|---------| | ๐Ÿ“ท Upload Photo | File | .jpg, .png, .webp | | ๐ŸŒ Region & Language | Dropdown | 9 regions with currencies | | ๐Ÿ—๏ธ Work Type | Dropdown | New / Renovation / Repair / Auto | | ๐Ÿ“ Description | Textarea | Optional context |

Example Output

Input: Bathroom photo (renovation)
Region: ๐Ÿ‡ฉ๐Ÿ‡ช German - Berlin (EUR โ‚ฌ)

Generated Work Items: PREPARATION (3 items) โ”œโ”€โ”€ Demolition of wall tiles โ€” 12 mยฒ โ€” โ‚ฌ180 โ”œโ”€โ”€ Demolition of floor tiles โ€” 4.5 mยฒ โ€” โ‚ฌ95 โ””โ”€โ”€ Disposal of construction waste โ€” 0.8 mยณ โ€” โ‚ฌ120

MAIN (8 items) โ”œโ”€โ”€ Floor waterproofing โ€” 4.5 mยฒ โ€” โ‚ฌ225 โ”œโ”€โ”€ Wall waterproofing wet zone โ€” 8 mยฒ โ€” โ‚ฌ280 โ”œโ”€โ”€ Floor screed โ€” 4.5 mยฒ โ€” โ‚ฌ135 โ”œโ”€โ”€ Wall tiling โ€” 22 mยฒ โ€” โ‚ฌ880 โ”œโ”€โ”€ Floor tiling โ€” 4.5 mยฒ โ€” โ‚ฌ225 โ”œโ”€โ”€ Toilet installation โ€” 1 pcs โ€” โ‚ฌ320 โ”œโ”€โ”€ Sink installation โ€” 1 pcs โ€” โ‚ฌ185 โ””โ”€โ”€ Shower cabin installation โ€” 1 pcs โ€” โ‚ฌ450

FINISHING (3 items) โ”œโ”€โ”€ Ceiling painting โ€” 4.5 mยฒ โ€” โ‚ฌ68 โ”œโ”€โ”€ Grouting โ€” 26.5 mยฒ โ€” โ‚ฌ133 โ””โ”€โ”€ Silicone sealing โ€” 8 m โ€” โ‚ฌ48

MEP (4 items) โ”œโ”€โ”€ Socket installation โ€” 2 pcs โ€” โ‚ฌ90 โ”œโ”€โ”€ Light point installation โ€” 2 pcs โ€” โ‚ฌ120 โ”œโ”€โ”€ Mixer/faucet installation โ€” 2 pcs โ€” โ‚ฌ160 โ””โ”€โ”€ Ventilation installation โ€” 1 pcs โ€” โ‚ฌ85

โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ TOTAL: โ‚ฌ3,799.00 Labor: โ‚ฌ1,520 ยท Materials: โ‚ฌ1,900 ยท Machines: โ‚ฌ379 Quality: 78% high match ยท 18 work items

Quality Scoring System

| Score | Level | Meaning | |-------|-------|---------| | 60-100 | ๐ŸŸข High | Exact match with resources | | 40-59 | ๐ŸŸก Medium | Good match, minor differences | | 20-39 | ๐ŸŸ  Low | Partial match, review needed | | 0-19 | ๐Ÿ”ด 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 โ€” may need manual additions Rate accuracy:** Depends on DDC CWICR coverage for your region Extend:** Chain with PDF generation, email delivery, or CRM integration

Categories

AI ยท Data Extraction ยท Document Ops ยท Files & Storage

Tags

photo-analysis, gpt-4-vision, construction, cost-estimation, qdrant, vector-search, form-trigger, html-report, multilingual

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

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

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Author:Artem Boiko(View Original โ†’)
Created:2/13/2026
Updated:3/11/2026

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