Managing hundreds of Kubernetes clusters can make incident response a significant challenge for enterprise SRE teams. At Coderise, we built an AI SRE Agent that automatically investigates alerts, gathers logs, metrics, and traces, performs root cause analysis, and provides evidence-backed remediation suggestions. When an alert fires, an SRE engineer shouldn’t have to spend the first...
AI Governance for Software Teams: Practical Controls for Production AI
A practical AI governance guide for software teams building production AI systems, with controls that support delivery instead of blocking it.
How to Build Secure Internal AI Search Over Company Docs
A practical guide to secure internal AI search over company documents with permissions, citations, data boundaries, logging, and evaluation.
LLM Evaluation Framework: How to Test AI Apps Before Launch
A practical LLM evaluation framework for testing AI applications before launch with golden data, automated checks, review, and release gates.
Prompt Engineering for Production Apps: Patterns That Matter
A practical prompt engineering guide for production AI applications, focused on structure, context, evaluation, versioning, and reliability.
Vector Database vs Knowledge Graph: Which Is Better for AI Search?
A practical comparison of vector databases and knowledge graphs for AI search, including RAG retrieval, relationships, permissions, and hybrid designs.
How to Build an Enterprise AI Roadmap That Actually Ships
A practical enterprise AI roadmap for leaders and engineering teams who want pilots that move into production instead of staying in slide decks.
AI App Architecture: Frontend, API, Model Layer, and Data
A practical guide to AI app architecture, covering frontend UX, API design, model orchestration, data controls, retrieval, evals, and monitoring.
MCP Explained: How AI Agents Connect to Tools and Data
A plain-English explanation of Model Context Protocol, how it helps AI agents connect to tools and data, and what teams should secure.
How to Monitor LLM Apps: Logs, Traces, Evals, and Alerts
Learn how to monitor LLM applications with logs, traces, evaluations, feedback loops, cost metrics, latency tracking, and actionable alerts.