Technical Showcase
Enterprise GenAI
Architecture Patterns
Production-grade AI architecture patterns for enterprise systems — from RAG pipelines and multi-agent orchestration to LLMOps, AI safety, and evaluation frameworks. Designed for scale, reliability, and enterprise compliance.
Architecture Pattern
Enterprise RAG Architecture
Production-grade Retrieval-Augmented Generation architecture for enterprise knowledge systems. Designed for accuracy, scalability, and auditability.
Ingestion Pipeline
Documents
PDF, Docs, Web
Chunking
Semantic splits
Embedding
Vector model
Vector DB
Pinecone / Weaviate
Query Pipeline
User Query
Natural language
Query Embed
Same model
Retrieval
Hybrid search
Re-ranking
Cross-encoder
LLM + Context
GPT-4 / Claude
Response
Grounded answer
Accuracy
Precision@k, RAGAS evaluation
Latency
Sub-second retrieval with caching
Security
PII filtering, access controls
Scalability
Millions of documents, distributed
Architecture Pattern
Multi-Agent System Architecture
Enterprise multi-agent orchestration pattern for complex, long-horizon tasks. Features hierarchical agent control, tool use, and human-in-the-loop checkpoints.
Agent Topology
Orchestrator
Planner / Router
Research Agent
Web + RAG
Code Agent
Code execution
Analysis Agent
Data + Reasoning
Output Agent
Response synthesis
Tool Integrations
Key Design Patterns
Deep Expertise
AI Engineering Domains
LLMOps
Production ML operations for LLM-based systems — model versioning, A/B testing, prompt management, cost optimization, continuous evaluation, and responsible AI governance.
AI Observability
End-to-end tracing, logging, and monitoring for AI systems. Track latency, token costs, hallucination rates, and user satisfaction across your AI pipeline.
AI Safety & Alignment
Enterprise AI safety practices: output filtering, content moderation, PII detection, prompt injection prevention, and responsible AI governance frameworks.
Responsible AI & AI Governance
Enterprise AI governance frameworks including ethical AI guidelines, bias detection and mitigation, compliance with regulations (EU AI Act, GDPR), transparency requirements, and accountability structures.
AI Evaluation Pipelines
Automated evaluation frameworks for LLM-based applications. RAGAS, G-Eval, LLM-as-judge, and custom domain-specific evaluation metrics.
AI Infrastructure
Scalable AI serving infrastructure on cloud platforms. GPU optimization, model caching, inference optimization, and cost-effective deployment patterns.
Prompt Engineering Systems
Systematic prompt management for enterprise AI systems — prompt versioning, testing frameworks, template libraries, and prompt optimization workflows.