Technical Deep-Dives from the Field
Practitioner-written technical content based on real production deployments. Real architectures, benchmark data, and the lessons learned building enterprise AI systems at scale.
Featured Articles
Building Production-Grade GraphRAG: Architecture Decisions That Matter
After deploying GraphRAG across multiple enterprise environments, we catalogued the architectural decisions that determine success or failure. This covers entity resolution, graph schema design tradeoffs, hybrid retrieval combining graph traversal with vector similarity, and latency optimization for production workloads.
Article Outline
- Why vector RAG underperforms on compliance and legal documents
- Entity resolution: the most common GraphRAG failure mode
- Graph schema design: three approaches and their tradeoffs
- Hybrid retrieval: combining graph traversal with vector similarity scoring
- +2 more sections
On-Premise Llama 3 Deployment: A Complete Infrastructure Runbook
A practitioner's runbook for deploying Llama 3 in enterprise environments where data cannot leave the building. Covers hardware sizing, quantization tradeoffs, serving framework selection, network isolation for ITAR and HIPAA contexts, and the monitoring stack needed when you can't use external observability services.
Article Outline
- Hardware selection: GPU sizing for each Llama 3 model variant
- Quantization decision tree: INT4 vs INT8 vs FP16 accuracy tradeoffs
- vLLM vs TGI vs Ollama: when to use each in enterprise
- Network isolation patterns for HIPAA and ITAR deployments
- +2 more sections
All Articles
Building Production-Grade GraphRAG: Architecture Decisions That Matter
RAG SystemsAfter deploying GraphRAG across multiple enterprise environments, we catalogued the architectural decisions that determine success or failure. This covers entity resolution, graph schema design tradeoffs, hybrid retrieval combining graph traversal with vector similarity, and latency optimization for production workloads.
On-Premise Llama 3 Deployment: A Complete Infrastructure Runbook
LLM DeploymentA practitioner's runbook for deploying Llama 3 in enterprise environments where data cannot leave the building. Covers hardware sizing, quantization tradeoffs, serving framework selection, network isolation for ITAR and HIPAA contexts, and the monitoring stack needed when you can't use external observability services.
Multi-Agent Orchestration with CrewAI: What Actually Works in Production
Multi-AgentCrewAI is our primary multi-agent framework after evaluating LangGraph, AutoGen, and custom implementations. This covers agent role design principles, tool allocation strategy, shared memory patterns, failure handling to prevent cascade failures, and the orchestration anti-patterns we see most often in real deployments.
RAG Pipeline Latency Optimization: From 12 Seconds to Sub-400ms
PerformanceA specific optimization journey for a financial services RAG pipeline. We took a 12-second end-to-end latency to under 400ms. Every step is documented with measurement methodology and before/after numbers — including two changes that made things worse and had to be reverted.
Threat Modeling LLM Systems: A Practitioner's Security Checklist
SecurityPrompt injection, indirect prompt injection through retrieved documents, data exfiltration via LLM outputs, and model inversion attacks are real threats in enterprise AI deployments. This walks through a complete threat model for an enterprise RAG application, with specific mitigations and the red team methodology we use before production release.
AI-Native Application Architecture: Reliability Patterns for LLM-Backed Services
ArchitectureLLM services fail differently from traditional APIs — non-determinism, token budget exhaustion, provider outages, and latency spikes require different reliability patterns. This covers circuit breakers for LLM providers, multi-provider fallback routing, response validation and retry logic, semantic caching tradeoffs, and cost control through graceful degradation.
How We Write These Articles
Every article is written by the engineers who built and deployed the systems described. All benchmarks include hardware specifications, test conditions, and measurement methodology. We document failures and reversals alongside successes. We do not publish sponsored technical content.