Reference Architecture for an Enterprise AI Platform
A practical architecture that connects business problems, knowledge, RAG, agents, security, and observability into one enterprise AI platform.
Yevhen Stuzhuk - Personal Portfolio
I am a Senior Software Engineer
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rag
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Secure Enterprise AI Development: Releasing Change Without Losing Control
A practical lifecycle for changing enterprise AI safely: requirements, testing, security review, controlled release, rollback, and monitoring.
Enterprise AI Observability: Seeing the Whole System
How to operate enterprise AI with observability across infrastructure, RAG, answer quality, security, and business metrics.
Evaluating Enterprise AI: Metrics You Can Trust
A practical framework for measuring enterprise AI across retrieval, answers, security, and business outcomes.
Enterprise RAG Architecture: From Search to a Knowledge Platform
How to design scalable RAG for many data sources, millions of documents, and access-controlled enterprise knowledge.
Preparing Data for RAG: Turning Archives into a Knowledge Base
How to clean, describe, chunk, and safely index enterprise documents for reliable AI answers.
How to Select an Enterprise LLM: Criteria Over Rankings
How to select a language model for enterprise AI using scenarios, security, cost, and infrastructure requirements.
The First Enterprise AI Pilot: From Knowledge Map to Measurable Value
How to select a bounded, secure enterprise AI pilot, define success criteria, and prepare for responsible scaling.
The Enterprise Knowledge Map: From Information Chaos to AI Foundation
How to discover, classify, and safely connect enterprise knowledge for RAG and enterprise search.
ERP as a Knowledge Source, Not a Corporate AI Competitor
How to connect corporate AI to ERP while preserving data authority, access roles, and operational controls.
Why Enterprise AI Needs Agents
How agent systems connect language models to enterprise services and automate processes while keeping people in control.
When Fine-Tuning a Model Is Actually Needed
How to separate enterprise knowledge from model behavior and decide when fine-tuning is worth the operational cost.
Security as an Architectural Principle for Enterprise AI
Why data protection, access controls, answer traceability, and audit must shape enterprise AI architecture from day one.
Enterprise AI Infrastructure: Design for Workload, Not for GPUs
How to design reliable, scalable enterprise AI infrastructure from business scenarios, security constraints, and service-level requirements.
Vector Databases: The Foundation of Enterprise Semantic Search
How vector databases make enterprise knowledge searchable at scale while preserving source systems and access controls.
Why Embeddings Changed Enterprise Search
How vector representations retrieve enterprise knowledge by meaning while preserving exact search and security requirements.
Why Enterprises Need RAG Instead of LLM Memory
How RAG separates enterprise knowledge from the model, keeps documents current, and preserves verifiable, permission-aware access.