Why ItMatters
Strong opinions on AI search — the costs, the trade-offs, and the stuff vendors won't put on a slide.
What AI Search Actually Costs, and Where the Money Goes
AI search has real running costs that rarely show up until the invoice does. Here is the anatomy of those costs, grounded in the providers' own documentation, and how to keep each one predictable.
The invoice is predictable. You just have to know where to look.
The bill always comes due
Search you can't measure is search you're overpaying for.
The Hidden Cost of Bad Site Search
Nobody budgets for bad search. It doesn't show up as a line item. But it quietly costs you money every day — in lost revenue, inflated support costs, wasted content, and invisible churn. Here's how to measure it and what to do about it.
How E-Commerce Sites Are Using AI Search to Increase Conversions
Site search visitors are your highest-intent users. AI search helps them find what they need by understanding natural language queries, generating rich responses, and turning browsing into buying.
What Your Search Analytics Are Telling You (That Pageview Analytics Miss)
Your site search data reveals what visitors actually want, in their own words. Learn how to use search analytics to uncover content gaps, language mismatches, and conversion insights that traditional analytics miss.
Search, chat & other false binaries
Every "X killed Y" headline about AI is selling you something.
You Still Need Search Even If You Have Chat
Chat is the future, search is the past — right? Wrong. Search and chat solve fundamentally different problems. Learn why the best AI experiences use both, and how unified analytics across channels reveals the full picture of customer intent.
Beyond FAQ Bots: What AI Chat Actually Looks Like in 2026
If your mental model of AI chat is still a keyword-matching FAQ bot, it's time to update. Modern AI chat understands natural language, maintains multi-turn context, and delivers rich responses grounded in your actual content.
Deterministic vs. Agentic Mode: When to Let AI Think and When to Keep It on Rails
AI search and chat exist on a spectrum from full autonomy to tight constraints. The right answer is almost always both, depending on the query. Here's how to decide which mode for what.
Vector Search vs. Traditional Search: What Actually Changes for Your Users
Traditional search matches words. Vector search matches meaning. That distinction sounds small, but it changes everything about what your users can find. Learn how hybrid search combines the precision of keywords with the intelligence of semantic understanding.
Hope is not a monitoring strategy.
Trust the machine (then verify)
Putting AI in front of customers is a promise. Here's how to keep it.
Execution Traces: How to Actually Debug and Trust Your AI
If an AI response is wrong, which layer broke? Execution traces give you complete visibility into every step between a user's query and the response they received, turning AI debugging from guesswork into root cause analysis.
Grounding AI in Your Data: How to Prevent Hallucinations in Customer-Facing Search
When you put AI in front of your customers, a wrong answer isn't just annoying — it's a liability. Learn how grounded AI search sharply reduces hallucinations by constraining responses to your indexed data, with source citations and full execution traces.
Building a Multi-Provider AI Strategy (and Why Vendor Lock-In Is the Real Risk)
Most companies start with a single AI provider. The problem isn't starting with one — it's building your entire system around one with no ability to switch. Here's how to architect for flexibility.
Open Source vs. SaaS for AI Search: How to Choose
Deciding between a hosted SaaS search product and an open-source solution you can self-host? Here's how to think through the trade-offs around data, budget, compliance, and engineering capacity.
5 Signs Your Documentation Site Needs AI Search
Your docs content is usually there. Users just can't find it. Here are five signs your documentation site has outgrown keyword search and how AI search closes the gap between how authors organize content and how users look for it.