back_office_ops · realestate · workflow
Tradestack builds an AI-powered quoting assistant for trades businesses using LangGraph Cloud
Trades businesses face an extensive administrative burden when creating project quotes, a process that typically consumes 3.5 to 10 hours per project and involves analyzing floor plans, reviewing images, estimating effort, and calculating material prices.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits via WhatsApp
Users submit quote-related inputs via WhatsApp as the primary interface.
Tools used
LangGraphLangGraph CloudLangGraph StudioLangGraph TemplatesLangSmithLCELSlackgpt-4-0125-previewgpt-4o
Outcome
Tradestack launched their MVP in 6 weeks to a community of 28,000+ users, secured their first paying customers, improved end-to-end performance from 36% to 85% through rapid iteration, and saved two weeks of internal testing time.
What failed first
Building an AI agent system that consistently performed at high quality with diverse user inputs was not straightforward, with explicit failure modes including input ambiguity, variable workflow paths between users, and inconsistent LLM planning and routing.
Results
Time saved6 weeks
Volumefrom 36% to 85%
Grounding & classification
Source type: vendor customer story
37 fields verified against source quotes.
agentic workflowcontent generationdocument aimulti agent workflowform submissionfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedreal estateaccuracy improvementcycle time reductiontime savedvendor customer storyback office opsfinance opsagentic task executionintake to triage