Notes from building production AI systems.
Agent readiness, AI search visibility, architecture, evals, and operating patterns for teams shipping agents and RAG systems.
What Is an AI Support Deflection Agent?
An AI support deflection agent resolves customer inquiries autonomously before they reach human support. Learn how these agents work and what to evaluate.
Guardrails for Autonomous AI Agents: A Practical Guide
Learn how to implement guardrails for autonomous AI agents: pre-execution validation, output checks, budget limits, and circuit breakers that prevent costly errors.
SOC 2 Compliance for AI Features: What You Need to Know
SOC 2 compliance for AI features requires documented controls over data handling, model operations, and third-party integrations — with auditable evidence.
RAG Evaluation Metrics: What to Measure and Why
RAG evaluation metrics measure retrieval accuracy, generation quality, and end-to-end correctness. Learn which metrics matter for production systems.
Multi Agent Orchestration in Production: A Complete Guide
Multi agent orchestration in production coordinates autonomous AI agents to handle complex workflows reliably at scale with proper error handling and observability.
What RAG Actually Costs Per Query: Real Numbers From a Production System
Inference is not what makes retrieval-augmented generation expensive. A worked cost breakdown from a running RAG system — per query, per ingest, and the fixed line item that dwarfs both.
Vector Database vs Managed RAG Service: Which to Choose
Vector databases give control but require engineering. Managed RAG services handle infrastructure but limit customization. Compare options and decide.
Human in the Loop Agent Workflows: A Complete Guide
Human in the loop agent workflows combine AI automation with human judgment at critical decision points. Learn when to insert review steps and how to design
Document Q&A with Citations: Implementation Guide
Build document Q&A systems that cite sources using retrieval, LLM generation, and verification layers. Complete implementation guide with accuracy benchmarks.
How to Stop AI Hallucinating on Company Docs
Stop AI hallucinating on company docs with structured chunking, semantic search with reranking, validation layers, and explicit refusal prompts.
RAG for Internal Documentation: Implementation Guide
Learn how to build RAG for internal documentation. Step-by-step guide covering chunking, embedding, retrieval, and generation for company wikis and knowledge bases.
How Much Does AEO Software Cost in 2026?
AEO software costs $500-$5,000+/month depending on volume and automation. Compare pricing tiers and calculate ROI for your content strategy.
Best AEO Software for B2B SaaS in 2026
Compare top AEO platforms for B2B SaaS — autonomous vs writer-assist tools, pricing models, and which teams get the most value from each option.
Answer Engine Optimization Software: What It Is and How to Choose
Answer engine optimization software structures content to get cited in AI Overviews, ChatGPT, and Perplexity. Learn what it does and how to choose the right tool.
Surfer SEO vs Answer Engine Optimization: Which to Use
Surfer SEO optimizes for rankings through keyword density. AEO targets AI citations in ChatGPT and Perplexity. Learn which approach fits your buyer journey.
Content Automation for SaaS Marketing Teams: A Practical Guide
Content automation for SaaS marketing teams scales production without proportional hiring. Learn workflow design, tool selection, and quality control.
FAQ Schema for AI Search: Implementation Guide
FAQ schema marks up Q&A pairs so AI systems extract and cite them. Learn how to implement it correctly for ChatGPT, Perplexity, and AI Overviews.
How to Rank in Perplexity: 5 Steps
Perplexity ranks sources by citation relevance and authority. Structure content answer-first, use question headings, and build topical clusters to get cited.
AEO Checklist for SaaS Websites: 7 Steps
Practical AEO checklist for B2B SaaS: schema markup, content structure, and technical requirements that get your product cited in AI answers.
Automated Blog Content for B2B SaaS: What Works in 2026
Automated blog content for B2B SaaS scales publishing to 20-40 articles monthly with structured workflows, human review gates, and outcome-focused templates.
How to Get Cited by ChatGPT in 2026
Structure content for LLM extraction with answer-first writing, schema markup, and topical authority. Learn the exact steps to get cited by ChatGPT.
What Is Agent Readiness? Why Good Code Still Fails Autonomous Agents
Agent readiness measures how productively a coding agent can work in your repository — configs, docs, tests, and tooling, not code quality. Here is what it covers and how to score it.
How to Prepare a Repository for Coding Agents: AGENTS.md, CLAUDE.md, and MCP
A concrete checklist for making a repo agent-ready: what to put in AGENTS.md, when you need CLAUDE.md, how to configure MCP servers, and the hygiene that makes agent work verifiable.
Why Coding Agents Fail on Real Repos (and What the Session Trace Shows)
Coding agents fail on real repositories because of orientation cost, polluted context, and unverifiable changes — not model weakness. A real session trace shows where the time goes.
AEO vs SEO: What Actually Changes When AI Answers the Query
AEO optimizes for being quoted in AI answers; SEO optimizes for ranking in a list of links. Here is what transfers between them, what doesn't, and how to run both without doubling the work.
How to Measure AI Search Visibility: Share of Answers, Not Rankings
You cannot manage AI search visibility with rank trackers. Measure share of answers instead: which buyer questions cite you, across which engines, and how that share moves over time.
llms.txt, Schema Markup, and AI Citations: Which Signals Actually Matter?
Content structure earns AI citations; llms.txt and schema markup are amplifiers, not substitutes. What each signal does, what it costs, and the order to implement them.
How to Get Cited by AI Search in 2026
Learn how to get cited by AI search engines like ChatGPT, Perplexity, and Google AI Overviews. Structure content for answer extraction and earn citations.
Building Reliable RAG Systems
How to move a retrieval system from a promising demo to a production service that answers from the right context.
Preventing Hallucinations In AI Systems
Hallucinations are usually a systems problem. Fix the context, the decision boundaries, and the user experience before blaming the model.
Designing Production-Grade AI Agents
The jump from a chat demo to a reliable agent usually comes down to workflow control, tool design, and visible state.
Evaluation Methods For AI Systems
The right evaluation setup measures retrieval, generation, and business workflow outcomes separately so teams can improve the right layer.
Retrieval Vs Fine-Tuning
Retrieval and fine-tuning solve different problems. Choosing the right one depends on knowledge freshness, output behavior, and control needs.
AI Observability In Production
If you cannot inspect the context, the prompt, the tool calls, and the final output together, you do not really know how the system behaves.
Architecture Patterns For LLM Systems
Reliable LLM products usually converge on a few core patterns: a request layer, a context layer, an action layer, and a control plane around them.