DDC CWICR Construction Cost Database

What does this agent do, and when should you use it?

DDC CWICR (Construction Work Items, Components & Resources) is an open cost database holding 55,719 work items and 27,672 resources across 30 regions and 23 languages, with every record laid out in an 85-field schema covering classification, rate identity, resources, labor, machinery, price variants, aggregates and mass/services. Data ships as XLSX, Parquet and CSV, plus pre-built Qdrant snapshots that already contain OpenAI text-embedding-3-large (3072-dimension) vectors for 11 language collections, so semantic retrieval works without any embedding job of your own. Four n8n workflow JSONs turn the data into working estimators: a Telegram text bot, a photo web form, a universal bot accepting text/photo/PDF, and a CAD (BIM) pipeline that reads Revit/IFC/DWG exports and produces 4D/5D estimates. Costing follows resource-based norms — labor hours, material quantities and machine time multiplied by regional prices — which keeps the technology norms portable while only prices change per market. Licensing is split: data under CC BY-NC 4.0 plus a separate DDC commercial licence, code under Apache-2.0, and you supply your own OpenAI/Gemini keys plus self-hosted Qdrant and n8n.

The repository is organized by source country — folders such as CIS-Russia-GESN-FER-TER/, Asia-China-Dinge/, Europe-Turkey-Birim-Fiyat/ and SouthAmerica-Brazil-SINAPI/ — each holding the national base as parquet and xlsx (native language plus English), a resource catalog as csv/xlsx, a PROVENANCE.md, and a markets/ directory with the base repriced via World Bank PPP and translated for 48 target markets. Qdrant collections are named ddc_{lang}_{city}; a snapshot like DE___DDC_CWICR/DE_BERLIN_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR.snapshot loads collection ddc_de_berlin with 55,719 points. Derived tracks such as ddc_au_sydney (sourced from UK_GBP) keep rate_code, resource_code and all norms bytewise identical to the source track, replacing only prices and translatable text. The bundled n8n workflows read a Telegram message, image or PDF, have an LLM extract work items, call OpenAI text-embedding-3-large to embed them, query Qdrant for matching rates, rerank with an LLM, and write HTML/Excel/PDF estimates. The CAD pipeline runs n8n_4_CAD_(BIM)_Cost_Estimation_Pipeline_4D_5D_with_DDC_CWICR.json through RvtExporter.exe to export a Revit model to XLSX, then processes 10 stages (project detection, phase generation, element assignment, work decomposition, vector search, unit mapping, cost calculation, 7.5 CTO validation, aggregation, report generation). All workflows take credentials from a central 🔑 TOKEN node: bot_token, OPENAI_API_KEY, GEMINI_API_KEY, QDRANT_URL, QDRANT_API_KEY.

1. A cost estimator preparing a tender types "100 m² gypsum partition with studs and skim coat" into the Telegram text bot and gets matched rates, a resource breakdown and a total price in one reply.
2. A site manager on a renovation job photographs a room; the photo estimator web form has GPT-4 Vision identify room type and elements, estimate dimensions from reference objects, and return an HTML cost report.
3. A BIM engineer exports an IFC/Revit model and runs stage 4 decomposition so element types become trades (masonry, mortar, plaster) priced through Qdrant, producing a phased 4D/5D HTML and XLS report.
4. A contractor operating in several countries benchmarks the same rate across Berlin, Toronto, Paris and São Paulo by switching between ddc_de_berlin and ddc_en_toronto collections.
5. An AI developer building a cost-estimation RAG assistant loads a 3072-dimension Qdrant snapshot instead of generating its own embeddings.
6. A data analyst takes the ~55 MB Parquet base into an ETL/BI pipeline to study labor hours and material consumption without running any AI service.

How do you install or deploy this agent?

The database itself needs no install — clone the repo to get the tabular data:

git clone https://github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR.git
cd OpenConstructionEstimate-DDC-CWICR

If you only want the cost data, read the Parquet/XLSX/CSV files directly. For the full AI pipeline you need n8n v1.0+, a Qdrant instance, and OpenAI plus a vision model API key. On n8n 2.0 and later the Execute Command node is disabled by default and must be re-enabled, otherwise the CAD/BIM pipeline will not run:

set NODES_EXCLUDE=[] && npx n8n

Alternatively create C:\Users\YOUR_USER\.n8n\.env containing NODES_EXCLUDE=[]. For Docker, set NODES_EXCLUDE=[] in the container environment. The repository ships the n8n workflow JSON and Qdrant snapshots but no Qdrant installation script — deploy Qdrant separately; a local instance normally listens on http://localhost:6333.

How do you use this agent?

First, upload a snapshot into Qdrant so the collection name matches the language collection used by the workflow:

curl -X POST "http://localhost:6333/collections/ddc_en_toronto/snapshots/upload" \
  -H "Content-Type: multipart/form-data" \
  -F "snapshot=@EN___DDC_CWICR/EN_TORONTO_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR.snapshot"

Second, import a workflow in n8n via Workflows → Import from File, for example n8n_1_Telegram_Bot_Cost_Estimates_and_Rate_Finder_TEXT_DDC_CWICR.json.

Third, fill in the 🔑 TOKEN node:

{
  "bot_token": "YOUR_TELEGRAM_BOT_TOKEN",
  "OPENAI_API_KEY": "YOUR_OPENAI_KEY",
  "GEMINI_API_KEY": "YOUR_GEMINI_KEY",
  "QDRANT_URL": "http://localhost:6333",
  "QDRANT_API_KEY": ""
}

Fourth, activate and test: send /start to the Telegram bot, or open the form URL n8n provides. The CAD/BIM pipeline additionally needs path_to_converter and project_file set, with RvtExporter.exe installed. OpenAI GPT-4o is the default model; switch by enabling one of Anthropic Chat Model2, Google Gemini Chat Model, xAI Grok Chat Model1 or DeepSeek Chat Model and disabling the rest. Reports are written to the project folder as project_YYYY-MM-DD.html and project_YYYY-MM-DD.xls.

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