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

Name: Estimate construction costs from text with Telegram, OpenAI and DDC CWICR
Availability: InStock
Author: Artem Boiko

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

Open on n8n.io

$20/month : Unlimited workflows

2500 executions/month

Try free

THE #1 IN WEB SCRAPING

Scrape any website without limits

Try free

HOSTINGER

Early Deal
DISCOUNT 20%

Self-hosted n8n

Unlimited workflows - from $4.99/mo

Try free

#1 hub for scraping, AI & automation

6000+ actors - $5 credits/mo

Try free

Overview

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

Receives text messages in Telegram (work lists, specifications, notes)
Parses input with AI (OpenAI/Claude/Gemini) into structured work items
Searches DDC CWICR vector database via Qdrant for matching rates
Calculates costs with full breakdown (labor, materials, machines)
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:

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

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

5. Telegram Webhook

Activate workflow
Telegram Trigger auto-registers webhook
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

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

DDC CWICR Database: GitHub
Qdrant Setup Guide: qdrant.tech/documentation
n8n AI Nodes: docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain

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

Artem Boiko

13 workflows

Complexity

advanced

Published 26 Dec 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/12174/12174.json

Share Your Workflow

Have a useful automation to share? Publish it and help the community.

Submit Your Template How to Submit

Related Workflows

Create fillable document templates from PDF or DOCX with GPT-4o and Google Drive

## 📄Template Creator ### How it works This workflow accepts any uploaded document (PDF or DOCX) via webhook and automatically converts it into a reusable fill-in-the-blank template. **Step 1 — Identify:** GPT-4o first reads the document and determines the document type (e.g., Employment Contract, Invoice, NDA, Lease Agreement, Project Proposal) and the specific variable fields that type of document typically contains. **Step 2 — Templatize:** A second AI pass uses the identified document type and field list to replace all variable content with clearly labeled `[BRACKET]` placeholders while preserving all static boilerplate and structure verbatim. **Step 3 — Deliver:** The cleaned template is rendered to PDF via Gotenberg, uploaded to Google Drive, made publicly accessible, and a JSON response with file URLs is returned to the caller. ### Setup 1. Configure the **Webhook** path if needed (default: `general-template-creator`) 2. Set your **OpenAI** credential on both LLM nodes 3. Set your **Google Drive** credential and confirm the target folder ID in the Upload node 4. Confirm the **Gotenberg** URL matches your self-hosted instance 5. Install the community node `n8n-nodes-word2text` (see ⚠️ warning sticky) ### Customization - Swap GPT-4o for GPT-4.1 or GPT-4.1-mini on the Identify node to reduce cost on the lighter classification task - Add a Switch node after identification to route different document types to type-specific prompts - Modify the Drive folder ID to sort templates into subfolders by document type Document accepts input from a form such as the one found here: [Sample Form](https://iportgpt.com/n8n_assets/template_creator_form.html)

View

Generate PDF pricing proposals from Excel with Gotenberg and Outlook

## How it works 1. **Webhook Trigger** — A web form POSTs prospect details, selected services, discount level, and requestor info to the webhook endpoint. 2. **Fetch Pricing Sheet** — Retrieves all rows from your `pricing_request.xlsx` Excel table in OneDrive/SharePoint. 3. **Process & Price** — Filters the pricing rows to the selected services, applies the correct Discount / Retail / Premium price tier, and calculates the total. 4. **Build HTML** — Renders a fully branded HTML proposal document with cover info, line-item pricing table, terms, and a signature block. 5. **Convert to PDF** — Sends the HTML to a self-hosted [Gotenberg](https://gotenberg.dev) instance, which returns a print-ready PDF. 6. **Email Proposal** — Delivers the PDF as an attachment via Gmail OAuth2 to the requester and any internal recipients you configure.

View

Classify and route email attachments with easybits, Gmail and Google Drive

## What This Workflow Does Receive any business document via email. The attachment is automatically **classified** (Invoice, Contract, or Purchase Order) using easybits Extractor, then **routed** down the correct path where a second Extractor pulls out document-specific data. Each route stores the file in Google Drive and triggers the appropriate action – Invoices go to a finance spreadsheet, Contracts and Purchase Orders trigger Slack notifications. ## How It Works 1. **Receive** – Gmail polls for new emails with attachments every minute 2. **Classify** – easybits Extractor identifies the document type and returns a class label 3. **Route** – A Switch node sends the item down the matching path (Invoice / Contract / PO) 4. **Merge Binary** – The original file is merged back into the routed item (classification strips the binary) 5. **Extract** – A second easybits Extractor pulls fields specific to that document type 6. **Merge Data + File** – Extracted JSON and original binary are combined for upload 7. **Store & Notify** – The file is uploaded to Google Drive; Invoices update a spreadsheet, Contracts and POs trigger Slack alerts --- ## Setup Guide ### 1. Create Your easybits Classification Pipeline 1. Go to **extractor.easybits.tech** and create a new pipeline 2. Add **one field** called `document_class` 3. In the field prompt, describe your classification categories and how to identify each one (see the "easybits: Classify Document" node for a reference prompt) 4. The prompt should instruct the model to return exactly one category label – no explanations, no extra text 5. Adjust the categories and identification criteria to match your specific document types 6. Copy your **Pipeline ID** and connect the credential in the classification node > 💡 **Tip:** The classification prompt is the heart of this workflow. The more specific your category descriptions and decision rules are, the more accurate your results will be. ### 2. Create Three Extraction Pipelines Create one pipeline per document type on **extractor.easybits.tech**: - **Invoice pipeline** – fields: `invoice_number`, `total_amount`, `currency`, `due_date`, `vendor_name` - **Contract pipeline** – fields: `client_name`, `contract_type`, `contract_value`, `currency`, `start_date`, `notice_period` - **Purchase Order pipeline** – fields: `supplier_name`, `po_number`, `order_date`, `expected_delivery_date`, `total_amount`, `currency` Connect each pipeline's credentials to the matching Extractor node. ### 3. Set Up Gmail Connect your Gmail account via OAuth2. Optionally filter by label to only process specific emails. Make sure **Download Attachments** is enabled in the node options. ### 4. Set Up Google Drive Create three folders in Google Drive: **Invoices**, **Contracts**, **Purchase Orders**. Select the correct folder in each Upload node. Set the **Input Binary Field** to `attachment_0` (or whichever field carries the PDF). ### 5. Set Up Google Sheets Create a spreadsheet (or use an existing Master Finance File). Make sure the column headers match the field mappings in the "Update Master Finance Sheet" node. ### 6. Set Up Slack Connect your Slack workspace. Select the channel for contract notifications (e.g. `#contracts`) and the channel for PO updates (e.g. `#operations`). Adjust the message templates if your extracted field names differ. ### 7. Activate & Test Set the workflow to active and send a test email with an invoice, contract, and purchase order attached to verify each route works end to end.

View

Need Custom Automation?

Get help designing a custom n8n workflow that connects your stack and fits your process.

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

Overview

Who's it for

What it does

How it works

Prerequisites

Setup

1. Credentials (n8n Settings → Credentials)

2. Configuration (🔑 TOKEN node)

3. Qdrant Setup

4. Link AI Model Nodes

5. Telegram Webhook

Features

Example Input/Output

Notes & Tips

Categories

Tags

Author

Consulting & Training

Resources

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

Workflow preview

Overview

Who's it for

What it does

How it works

Prerequisites

Setup

1. Credentials (n8n Settings → Credentials)

2. Configuration (🔑 TOKEN node)

3. Qdrant Setup

4. Link AI Model Nodes

5. Telegram Webhook

Features

Example Input/Output

Notes & Tips

Categories

Tags

Author

Consulting & Training

Resources