AlekSystem Workflow Detail

Estimate construction costs from text, photos and PDFs with Telegram, GPT4/Gemini and DDC CWICR Workflow Solution

Estimate construction costs from text, photos and PDFs with Telegram, GPTโ€‘4/Gemini and DDC CWICR

A full-featured Telegram bot that accepts text descriptions, photos, or PDF floor plans and returns detailed cost estimates with work breakdown.

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Useful for software delivery and engineering operations. Helpful for business development and pipeline building.

A full-featured Telegram bot that accepts text descriptions, photos, or PDF floor plans and returns detailed cost estimates with work breakdown. Powered by GPT-4 Vision / Gemini 2.0, vector search, and the open-source DDC CWICR database (55,000+ construction rates). Who's it for Contractors & Estimators** who need estimates from any input format Construction managers** evaluating scope from site photos or drawings Architects** getting quick cost feedback on floor plans Real estate professionals** assessing renovation costs Project managers** doing rapid feasibility checks via mobile What it does Receives text / photo / PDF via Telegram Analyzes input with AI (Gemini 2.0 Flash or GPT-4 Vision) Extracts work items with quantities and units Searches DDC CWICR vector database for matching rates Generates professional HTML report with full cost breakdown Exports results as Excel or PDF Supports 9 languages: ๐Ÿ‡ฉ๐Ÿ‡ช DE ยท ๐Ÿ‡ฌ๐Ÿ‡ง EN ยท ๐Ÿ‡ท๐Ÿ‡บ RU ยท ๐Ÿ‡ช๐Ÿ‡ธ ES ยท ๐Ÿ‡ซ๐Ÿ‡ท FR ยท ๐Ÿ‡ฎ๐Ÿ‡น IT ยท ๐Ÿ‡ต๐Ÿ‡ฑ PL ยท ๐Ÿ‡ง๐Ÿ‡ท PT ยท ๐Ÿ‡บ๐Ÿ‡ฆ UK How it works 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”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ TELEGRAM INPUT โ”‚ โ”‚ ๐Ÿ“ Text Description โ”‚ ๐Ÿ“ท Construction Photo โ”‚ ๐Ÿ“„ PDF Floor Plan 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”€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”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ MAIN ROUTER โ”‚ โ”‚ Parse message โ†’ Detect content type โ†’ Route to handler (17 actions) 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”€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”ผโ”€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”Œโ”€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”‚ Text LLM โ”‚ โ”‚ Vision API โ”‚ โ”‚ Vision PDF โ”‚ โ”‚ Parse works โ”‚ โ”‚ Analyze photo โ”‚ โ”‚ Read floor planโ”‚ โ”‚ from text โ”‚ โ”‚ GPT-4/Gemini โ”‚ โ”‚ Gemini 2.0 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”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€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”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ CALCULATION LOOP โ”‚ โ”‚ For each work item: โ”‚ โ”‚ 1๏ธโƒฃ Transform query โ†’ 2๏ธโƒฃ Optimize search โ†’ 3๏ธโƒฃ Get embedding โ”‚ โ”‚ 4๏ธโƒฃ Qdrant search โ†’ 5๏ธโƒฃ Score results โ†’ 6๏ธโƒฃ AI rerank โ†’ 7๏ธโƒฃ Calculate 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”€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”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ OUTPUT โ”‚ โ”‚ ๐Ÿ“Š Telegram message โ”‚ ๐ŸŒ HTML Report โ”‚ ๐Ÿ“‘ Excel โ”‚ ๐Ÿ“„ PDF 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”€โ”˜ Input Types | Type | Description | AI Used | |------|-------------|---------| | ๐Ÿ“ Text | Work lists, specifications, notes | OpenAI GPT-4 | | ๐Ÿ“ท Photo | Construction site photos (up to 4) | GPT-4 Vision / Gemini | | ๐Ÿ“„ PDF | Floor plans, architectural drawings | Gemini 2.0 Flash | Route Actions (17 total) | # | Action | Description | |---|--------|-------------| | 0 | show_lang | Language selection menu | | 1 | ask_photo | Request photo upload | | 2 | lang_selected | Save language preference | | 3 | show_analyze | Photo analysis options | | 4 | analyze | Run AI vision analysis | | 5 | show_edit_menu | Edit work quantities | | 6 | works_updated | After quantity change | | 7 | ask_new_work | Add manual work item | | 8 | start_calc | Start cost calculation | | 9 | show_help | Display help message | | 10 | view_details | Show resource details | | 11 | export_excel | Generate CSV export | | 12 | export_pdf | Generate PDF export | | 13 | process_pdf | Analyze PDF floor plan | | 14 | analyze_text | Parse text description | | 15 | refine | Re-analyze with context | | 16 | fallback | Handle unknown input | Prerequisites | Component | Requirement | |-----------|-------------| | AlekSystem | v1.30+ with Telegram Trigger | | Telegram Bot | Token from @BotFather | | OpenAI API | For embeddings + text parsing | | Gemini API | For Vision (photos/PDF) โ€” or use GPT-4 Vision | | Qdrant | Vector DB with DDC CWICR collections | | DDC CWICR Data | github.com/datadrivenconstruction/DDC-CWICR | Setup 1. Configure ๐Ÿ”‘ TOKEN Node { "bot_token": "YOUR_TELEGRAM_BOT_TOKEN", "AI_PROVIDER": "gemini", "GEMINI_API_KEY": "YOUR_GEMINI_KEY", "OPENAI_API_KEY": "YOUR_OPENAI_KEY", "QDRANT_URL": "http://localhost:6333", "QDRANT_API_KEY": "YOUR_QDRANT_KEY" } 2. Vision Provider Selection AI_PROVIDER: "gemini" โ†’ Gemini 2.0 Flash (recommended for photos + PDF) AI_PROVIDER: "openai" โ†’ GPT-4 Vision (photos only) 3. AlekSystem Credentials Settings โ†’ Credentials โ†’ Add โ†’ Telegram API Enter bot token, save Select credential in Telegram Trigger node 4. Qdrant Collections Load DDC CWICR embeddings for target languages (example for Russian): RU_STPETERSBURG_workitems_costs_resources_EMBEDDINGS_3072_DDC_CWICR 5. Activate & Test Activate workflow Send /start to your bot Select language โ†’ send photo/text/PDF Features | Feature | Description | |---------|-------------| | ๐Ÿ“ท Photo Analysis | GPT-4 Vision or Gemini 2.0 for site photos | | ๐Ÿ“„ PDF Processing | Floor plan analysis with room extraction | | ๐Ÿ“ Text Parsing | Natural language work lists | | ๐Ÿ” Vector Search | Semantic matching via Qdrant + OpenAI embeddings | | ๐Ÿค– AI Reranking | LLM-based result scoring for accuracy | | โœ๏ธ Inline Editing | Modify quantities via Telegram buttons | | ๐Ÿ“Š HTML Report | Professional expandable report with KPIs | | ๐Ÿ“‘ Excel Export | CSV with full work breakdown | | ๐Ÿ“„ PDF Export | HTML-based PDF document | | ๐ŸŒ 9 Languages | Full UI + database localization | | ๐Ÿ’พ Session State | Multi-turn conversation support | | ๐Ÿ”ง Refine Mode | Re-analyze with additional context | Example Workflow User: /start Bot: Language selection menu (9 options) User: Selects ๐Ÿ‡ท๐Ÿ‡บ Russian Bot: "ะžั‚ะฟั€ะฐะฒัŒั‚ะต ั„ะพั‚ะพ, PDF ะธะปะธ ั‚ะตะบัั‚ะพะฒะพะต ะพะฟะธัะฐะฝะธะต ั€ะฐะฑะพั‚" User: Sends bathroom photo Bot: "๐Ÿ“ท ะะฝะฐะปะธะท ั„ะพั‚ะพ... โณ" Bot: Shows detected works: ๐Ÿ  ะ’ะฐะฝะฝะฐั ะบะพะผะฝะฐั‚ะฐ โ€” 4.5 mยฒ ะะฐะนะดะตะฝะพ 12 ั€ะฐะฑะพั‚: ะ”ะตะผะพะฝั‚ะฐะถ ะฟะปะธั‚ะบะธ ัั‚ะตะฝ โ€” 18 mยฒ ะ”ะตะผะพะฝั‚ะฐะถ ะฟะปะธั‚ะบะธ ะฟะพะปะฐ โ€” 4.5 mยฒ ะ“ะธะดั€ะพะธะทะพะปัั†ะธั ะฟะพะปะฐ โ€” 4.5 mยฒ ะ“ะธะดั€ะพะธะทะพะปัั†ะธั ัั‚ะตะฝ โ€” 8 mยฒ ะกั‚ัะถะบะฐ ะฟะพะปะฐ โ€” 4.5 mยฒ ะฃะบะปะฐะดะบะฐ ะฟะปะธั‚ะบะธ ัั‚ะตะฝั‹ โ€” 18 mยฒ ะฃะบะปะฐะดะบะฐ ะฟะปะธั‚ะบะธ ะฟะพะป โ€” 4.5 mยฒ ะฃัั‚ะฐะฝะพะฒะบะฐ ัƒะฝะธั‚ะฐะทะฐ โ€” 1 ัˆั‚ ะฃัั‚ะฐะฝะพะฒะบะฐ ั€ะฐะบะพะฒะธะฝั‹ โ€” 1 ัˆั‚ ะฃัั‚ะฐะฝะพะฒะบะฐ ัะผะตัะธั‚ะตะปั โ€” 2 ัˆั‚ ... [โœ๏ธ ะ ะตะดะฐะบั‚ะธั€ะพะฒะฐั‚ัŒ] [๐Ÿ“Š ะ ะฐััั‡ะธั‚ะฐั‚ัŒ] User: Taps ๐Ÿ“Š Calculate Bot: Shows progress per item, then final result: โœ… ะกะผะตั‚ะฐ ะณะพั‚ะพะฒะฐ โ€” 12 ะฟะพะทะธั†ะธะน ๐Ÿ’ฐ ะ˜ั‚ะพะณะพ: โ‚ฝ 89,450 ะ ะฐะฑะพั‚ะฐ: โ‚ฝ 35,200 (39%) ะœะฐั‚ะตั€ะธะฐะปั‹: โ‚ฝ 48,750 (55%) ะœะตั…ะฐะฝะธะทะผั‹: โ‚ฝ 5,500 (6%) [๐Ÿ“‹ ะ”ะตั‚ะฐะปะธ] [โ†“ Excel] [โ†“ PDF] [โ†ป ะ—ะฐะฝะพะฒะพ] HTML Report Features KPI Cards:** Total cost, item count, labor days, cost breakdown % Expandable rows:** Click work item to show resources Resource tags:** Color-coded (Labor/Material/Machine) Scope of work:** Expandable detailed descriptions Quality indicators:** Match quality dots (high/medium/low) Responsive design:** Works on mobile and desktop Export buttons:** Expand/Collapse all Notes & Tips Photo tips:** Capture full room, include reference objects (doors, tiles) PDF support:** Works best with clear floor plans and room schedules Text input:** Supports lists, tables, free-form descriptions Rate accuracy:** Depends on DDC CWICR coverage for your region Session timeout:** User sessions persist across messages Extend:** Chain with CRM, project management, or notification tools Categories AI ยท Communication ยท Data Extraction ยท Document Ops Tags telegram-bot, construction, cost-estimation, gpt-4-vision, gemini, pdf-analysis, qdrant, vector-search, multilingual, html-report Author DataDrivenConstruction.io https://DataDrivenConstruction.io info@datadrivenconstruction.io Consulting & Training We help construction, engineering, and technology firms implement: AI-powered estimation systems (text, photo, PDF) Multi-channel bot integrations (Telegram, WhatsApp, Web) Vector database solutions for construction data Multilingual cost database deployment Contact us to test with your data or adapt to your project requirements. Resources DDC CWICR Database:** GitHub Qdrant Documentation:** qdrant.tech/documentation Gemini API:** aistudio.google.com AlekSystem Telegram Trigger:** docs.AlekSystem.io โญ Star us on GitHub! github.com/datadrivenconstruction/DDC-CWICR

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