n8n-cost-estimation

Automated Cost Estimation Pipeline

Business Case

Problem Statement

Traditional cost estimation requires:

• Manual work item lookup in price databases
• Time-consuming element classification
• Expert knowledge of pricing standards
• Repetitive data entry

Solution

Free open-source n8n pipeline that converts CAD (Revit 2015-2026) files into full cost and time estimates using AI (LLM) and vector database with 55,000+ work items.

Business Value

Traditional Role	Automated Alternative
BIM Manager manually exports data	Pipeline auto-classifies elements
Junior Estimator searches databases	Vector search finds matches in ms
Senior Estimator maps assemblies	LLM identifies quantity parameters
Foreman calculates labor hours	DDC CWICR contains documented norms
Project Manager aggregates costs	Pipeline outputs phased breakdown

Processing speed: 3-10 seconds per element group

Technical Implementation

Pipeline Architecture

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Revit/IFC   │───>│ CAD2DATA    │───>│ Structured  │
│ File        │    │ Converter   │    │ Excel/CSV   │
└─────────────┘    └─────────────┘    └─────────────┘
                                             │
                                             ▼
┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│ Cost Report │<───│ Price Match │<───│ LLM Class.  │
│ HTML/Excel  │    │ DDC CWICR   │    │ + QTO       │
└─────────────┘    └─────────────┘    └─────────────┘

n8n Pipeline Steps

1. File Conversion Node

// Execute CAD converter
const filePath = $input.first().json.file_path;
const outputDir = filePath.replace(/\.[^.]+$/, '');

const command = `RvtExporter.exe "${filePath}" complete bbox`;

// Returns: { xlsx_path, dae_path }

2. Load Elements

// Read converted Excel into n8n
const xlsx = $node["Read Binary Files"].json;
const elements = xlsx.sheets["Elements"];

// Group by category for processing
const grouped = elements.reduce((acc, el) => {
  const cat = el.Category;
  if (!acc[cat]) acc[cat] = [];
  acc[cat].push(el);
  return acc;
}, {});

return Object.entries(grouped).map(([category, items]) => ({
  json: {category, items, count: items.length}
}));

3. LLM Classification

// Prompt for Claude/GPT classification
const prompt = `
You are a construction estimator. Given these BIM elements:
Category: ${$input.first().json.category}
Sample elements: ${JSON.stringify($input.first().json.items.slice(0,5))}

1. Identify the construction work type
2. List relevant quantity parameters (Volume, Area, Length, Count)
3. Suggest standard work items from construction norms

Return as JSON:
{
  "work_type": "...",
  "quantity_params": ["Volume", "Area"],
  "suggested_items": ["Concrete foundation", "Formwork"]
}
`;

4. Vector Search in CWICR

// Search DDC CWICR database for matching work items
const qdrantClient = require('@qdrant/js-client-rest');

const searchResults = await qdrantClient.search('ddc_cwicr_en', {
  vector: await getEmbedding($input.first().json.work_description),
  limit: 10,
  score_threshold: 0.7
});

return searchResults.map(r => ({
  json: {
    work_code: r.payload.work_item_code,
    description: r.payload.description,
    unit: r.payload.unit,
    unit_price: r.payload.unit_price,
    similarity: r.score
  }
}));

5. Calculate Costs

// Match quantities to prices
const elements = $node["Load Elements"].json;
const prices = $node["Vector Search"].json;

let totalCost = 0;
const breakdown = [];

for (const el of elements.items) {
  const matchedPrice = prices.find(p => p.similarity > 0.8);
  if (matchedPrice) {
    const quantity = el.Volume || el.Area || 1;
    const cost = quantity * matchedPrice.unit_price;
    totalCost += cost;

    breakdown.push({
      element: el.Name,
      quantity: quantity,
      unit: matchedPrice.unit,
      unit_price: matchedPrice.unit_price,
      total: cost
    });
  }
}

return [{json: {totalCost, breakdown}}];

6. Generate Report

// Create HTML report
const data = $input.first().json;

const html = `
<!DOCTYPE html>
<html>
<head>
  <title>Cost Estimate Report</title>
  <style>
    body { font-family: Arial, sans-serif; margin: 20px; }
    table { border-collapse: collapse; width: 100%; }
    th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }
    th { background-color: #4CAF50; color: white; }
    .total { font-size: 1.5em; font-weight: bold; }
  </style>
</head>
<body>
  <h1>Cost Estimate Report</h1>
  <p class="total">Total: $${data.totalCost.toLocaleString()}</p>
  <table>
    <tr><th>Element</th><th>Quantity</th><th>Unit</th><th>Price</th><th>Total</th></tr>
    ${data.breakdown.map(row => `
      <tr>
        <td>${row.element}</td>
        <td>${row.quantity.toFixed(2)}</td>
        <td>${row.unit}</td>
        <td>$${row.unit_price.toFixed(2)}</td>
        <td>$${row.total.toFixed(2)}</td>
      </tr>
    `).join('')}
  </table>
</body>
</html>
`;

return [{json: {html, filename: 'estimate_report.html'}}];

Real-World Results

Example project (rac_basic_sample.rvt):

• Processing time: ~30 minutes with ChatGPT
• Elements analyzed: 500+
• Automatic classification: 95% accuracy
• Manual review needed: 5% edge cases

Key Insight from Community

"My subjective take: professionals who ignore workflow automation and AI-agents today have roughly 5 years before the construction industry moves past them. The tools are free and open. The data is open. The only question is who learns to use them first."

Prerequisites

• n8n (local or hosted)
• DDC CAD converters
• DDC CWICR database
• OpenAI/Anthropic API key
• Qdrant vector database

Resources

• GitHub: cad2data Pipeline repository
• Database: OpenConstructionEstimate-DDC-CWICR
• Community: n8n Workflows for Construction (Telegram)