Assistant: Hello! How are you today?
User: Of course. My apologies for the misunderstanding. Here is a concise, character-limited explanation of the architecture, perfect for a summary document or a high-level overview.

AI Solutioning Platform: Technical Architecture Overview (1998 Characters)
Our platform is built on a modern, cloud-native microservices architecture, ensuring scalability, security, and agility. The design separates the user experience from the complex AI backend, allowing for independent development and robust performance.

1. Frontend & API Gateway:
The user interacts with a responsive Single-Page Application (SPA). All communication is securely managed through a central API Gateway, which authenticates users and routes requests to the appropriate backend service.

2. Backend Microservices:
The core logic is a set of specialized services that work in concert:

Conversational AI Service: The "brain" that manages the user dialogue, leveraging our use case knowledge base.
Document Ingestion & RAG Service: Asynchronously processes uploaded documents. It uses Retrieval-Augmented Generation (RAG) to parse unstructured text (RFPs) and extracts data from structured files (Excel), making all information searchable and available to the AI.
Simulation & BOM Service: A powerful analytics engine that queries our integrated Intel product and performance database to generate optimized hardware configurations and Bills of Materials.
Supporting Services: Dedicated services handle user management, report generation, and the "Use Case Builder" functionality.
3. AI & Data Layers:
The platform's intelligence is powered by state-of-the-art Large Language Models (LLMs). The knowledge base is stored in a multi-tiered data layer:

Object Storage (S3): Stores all raw documents and the library of use case definition files.
Relational Database (PostgreSQL/Aurora): Holds all structured data, including the Intel SKU catalog and project metrics.
Vector Database (OpenSearch/Pinecone): Enables fast, semantic search across all unstructured document content, powering the RAG capability.
This decoupled, AI-first architecture creates a powerful and flexible "Solution Architect as a Service" platform, capable of delivering expert-level insights and evolving to meet future demands.

 in mermaid js v 10 syntax
User: Of course. My apologies for the misunderstanding. Here is a concise, character-limited explanation of the architecture, perfect for a summary document or a high-level overview.

AI Solutioning Platform: Technical Architecture Overview (1998 Characters)
Our platform is built on a modern, cloud-native microservices architecture, ensuring scalability, security, and agility. The design separates the user experience from the complex AI backend, allowing for independent development and robust performance.

1. Frontend & API Gateway:
The user interacts with a responsive Single-Page Application (SPA). All communication is securely managed through a central API Gateway, which authenticates users and routes requests to the appropriate backend service.

2. Backend Microservices:
The core logic is a set of specialized services that work in concert:

Conversational AI Service: The "brain" that manages the user dialogue, leveraging our use case knowledge base.
Document Ingestion & RAG Service: Asynchronously processes uploaded documents. It uses Retrieval-Augmented Generation (RAG) to parse unstructured text (RFPs) and extracts data from structured files (Excel), making all information searchable and available to the AI.
Simulation & BOM Service: A powerful analytics engine that queries our integrated Intel product and performance database to generate optimized hardware configurations and Bills of Materials.
Supporting Services: Dedicated services handle user management, report generation, and the "Use Case Builder" functionality.
3. AI & Data Layers:
The platform's intelligence is powered by state-of-the-art Large Language Models (LLMs). The knowledge base is stored in a multi-tiered data layer:

Object Storage (S3): Stores all raw documents and the library of use case definition files.
Relational Database (PostgreSQL/Aurora): Holds all structured data, including the Intel SKU catalog and project metrics.
Vector Database (OpenSearch/Pinecone): Enables fast, semantic search across all unstructured document content, powering the RAG capability.
This decoupled, AI-first architecture creates a powerful and flexible "Solution Architect as a Service" platform, capable of delivering expert-level insights and evolving to meet future demands.

 in mermaid js v 10 syntax
Assistant: