About This Architecture

Multi-stage estimating automation platform on Azure combines deterministic rule engines with Azure OpenAI for intelligent job classification and cost calculation. Email Ingestion and UI Submission flow through LLM Extraction using Azure OpenAI, Schema Validation, and Artwork Categorisation before entering the Deterministic GS Rule Engine for Technician Allocation and Case Cost Calculation. Historical Retrieval Engine embeds past jobs via Azure OpenAI and queries Azure Cognitive Search vector database to inform Price Code Selection alongside Catalogue Validation and LLM Advisory Reasoning. Python Formula Engine on Azure VM computes case size and integrated costs, while Draft Estimate Generation enforces Margin Logic and Line-Item Formatting with Confidence Scoring and Audit Logging to Log Analytics. Fork this architecture on Diagrams.so to customize intake layers, swap vector stores, or integrate your own catalogue validation logic for regulated industries.

People also ask

How do I architect an Azure platform that combines Azure OpenAI with deterministic rule engines for regulated estimating workflows?

This Azure architecture separates probabilistic AI (Azure OpenAI for extraction, classification, advisory reasoning) from authoritative deterministic engines (Technician Allocation, Case Cost Calculation, Catalogue Validation). Historical jobs embedded via Azure OpenAI inform Price Code Selection through Azure Cognitive Search vector retrieval, while Python Formula Engine on Azure VM enforces cost