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Estimate construction costs from text with Telegram, OpenAI and DDC CWICR

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Created by: Artem Boiko || datadrivenconstruction

Artem Boiko

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Last update 12 hours ago

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A Telegram bot that converts natural-language work descriptions into detailed cost estimates using AI parsing, vector search, and the open-source DDC CWICR database with 55,000+ construction work items.

Who's it for

  • Contractors & Estimators who need quick ballpark figures from verbal/text descriptions
  • Construction managers doing feasibility checks on-site via mobile
  • BIM/CAD professionals integrating text-based estimation into workflows
  • Developers building construction cost APIs or chatbots

What it does

  1. Receives text messages in Telegram (work lists, specifications, notes)
  2. Parses input with AI (OpenAI/Claude/Gemini) into structured work items
  3. Searches DDC CWICR vector database via Qdrant for matching rates
  4. Calculates costs with full breakdown (labor, materials, machines)
  5. Exports results as HTML report, Excel, or PDF

Supports 9 languages: 🇩🇪 DE · 🇬🇧 EN · 🇷🇺 RU · 🇪🇸 ES · 🇫🇷 FR · 🇧🇷 PT · 🇨🇳 ZH · 🇦🇪 AR · 🇮🇳 HI

How it works

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────────┐
│  Telegram   │ →  │  AI Parse    │ →  │  Embeddings │ →  │   Qdrant     │
│  Text Input │    │  (GPT/Claude)│    │  (OpenAI)   │    │   Search     │
└─────────────┘    └──────────────┘    └─────────────┘    └──────────────┘
                                                                  ↓
┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌──────────────┐
│   Export    │ ←  │  Aggregate   │ ←  │  Calculate  │ ←  │  AI Rerank   │
│ HTML/XLS/PDF│    │   Results    │    │    Costs    │    │   Results    │
└─────────────┘    └──────────────┘    └─────────────┘    └──────────────┘

Step-by-step:

  1. User sends /start → selects language → enters work description
  2. AI Parse extracts work items: name, quantity, unit, room
  3. Query Transform optimizes search terms for construction domain
  4. Embeddings API converts query to vector (OpenAI text-embedding-3-small)
  5. Qdrant Search finds top-10 matching rates from DDC CWICR
  6. AI Rerank selects best match considering context and units
  7. Calculate applies quantities, sums labor/materials/machines
  8. Report sends Telegram message + optional Excel/PDF export

Prerequisites

Component Requirement
n8n v1.30+ (AI nodes support)
Telegram Bot Token from @BotFather
OpenAI API For embeddings + LLM parsing
Qdrant Vector DB with DDC CWICR collections loaded
DDC CWICR Data github.com/datadrivenconstruction/DDC-CWICR

Setup

1. Credentials (n8n Settings → Credentials)

  • OpenAI API — required for embeddings and text parsing
  • Anthropic API — optional, for Claude models
  • Google Gemini API — optional, for Gemini models

2. Configuration (🔑 TOKEN node)

bot_token     = YOUR_TELEGRAM_BOT_TOKEN
QDRANT_URL    = http://localhost:6333
QDRANT_API_KEY = (if using Qdrant Cloud)

3. Qdrant Setup

Load DDC CWICR collections for your target languages:

  • DE_construction_rates — German (STLB-Bau based)
  • EN_construction_rates — English
  • RU_construction_rates — Russian (GESN/FER based)
  • ... (see DDC CWICR docs for all 9 languages)

4. Link AI Model Nodes

  1. Open OpenAI Model nodes
  2. Select your OpenAI credential
  3. (Optional) Enable Claude/Gemini nodes for alternative models

5. Telegram Webhook

  1. Activate workflow
  2. Telegram Trigger auto-registers webhook
  3. Test with /start in your bot

Features

Feature Description
🤖 Multi-LLM Swap between OpenAI, Claude, Gemini
🌍 9 Languages Full UI + database localization
📝 Smart Parsing Handles lists, tables, free-form text
🔍 Semantic Search Vector similarity + AI reranking
📊 Cost Breakdown Labor, materials, machines, hours
✏️ Inline Edit Modify quantities, delete items
📤 Export HTML report, Excel, PDF
💾 Session State Multi-turn conversation support

Example Input/Output

Input (Telegram message):

Living room renovation:
- Laminate flooring 25 m²
- Wall painting 60 m²
- Ceiling plasterboard 25 m²
- 3 electrical outlets

Output:

✅ Estimate Ready — 4 items found

1. Laminate flooring ✓
   25 m² × €18.50 = €462.50
   └ Labor: €125 · Materials: €337.50

2. Wall painting ✓
   60 m² × €8.20 = €492.00
   └ Labor: €312 · Materials: €180

3. Ceiling plasterboard ✓
   25 m² × €32.00 = €800.00
   └ Labor: €425 · Materials: €375

4. Electrical outlets ✓
   3 pcs × €45.00 = €135.00
   └ Labor: €95 · Materials: €40

─────────────────────
Total: €1,889.50

[↓ Excel] [↓ PDF] [↻ Restart]

Notes & Tips

  • First run: Ensure Qdrant has DDC CWICR data loaded before testing
  • Rate accuracy: Results depend on query quality; AI reranking improves matching
  • Large lists: Bot handles 50+ items; progress shown per-item
  • Customization: Edit Config node for UI text, currencies, database mapping
  • Extend: Chain with your CRM, project management, or reporting tools

Categories

AI · Data Extraction · Communication · Files & Storage

Tags

telegram-bot, construction, cost-estimation, qdrant, vector-search, openai, multilingual, bim, cad

Author

DataDrivenConstruction.io
https://DataDrivenConstruction.io
[email protected]

Consulting & Training

We help construction, engineering, and technology firms implement:

  • Open data principles for construction
  • CAD/BIM processing automation
  • AI-powered estimation pipelines
  • ETL workflows for construction databases

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

Resources

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