AiNex Research & Whitepapers
In-depth analysis, architectural patterns, and security best practices for Enterprise AI.
The State of Enterprise Autonomous Agents 2026
Analyzing the shift from static LLM chains to dynamic multi-agent systems. This report covers architectural patterns (LangGraph, CrewAI), adoption barriers, and security governance frameworks for large-scale deployments.
Technical Whitepapers
Secure RAG Optimization for Financial Data
Methods for implementing document-level access control and PII masking within vector retrieval systems.
Migrating Legacy Workflows to LangGraph
A practical guide for refactoring linear chains into cyclic state graphs for improved error handling/human-in-the-loop.
On-Premise LLM Deployment Strategies
Comparing throughput and latency of vLLM, TGI, and TensorRT-LLM on various GPU configurations.
Building Effective AI Guardrails: A Comprehensive Guide
Strategies for implementing content filtering, output validation, and behavioral constraints in production LLM systems.
Fine-Tuning Strategies for Domain-Specific LLMs
Comparing LoRA, QLoRA, and full fine-tuning approaches for creating specialized enterprise language models.
Prompt Engineering Patterns for Enterprise Applications
Battle-tested prompt patterns for common enterprise use cases including summarization, extraction, and analysis.
Vector Database Selection Guide for RAG Systems
Detailed comparison of Pinecone, Weaviate, Milvus, Qdrant, and pgvector for enterprise RAG deployments.
Implementing ISO 42001 for AI Management Systems
Step-by-step guide to achieving ISO 42001 certification for responsible AI development and deployment.
Observability for LLM Applications in Production
Building comprehensive monitoring, logging, and debugging infrastructure for production LLM systems.