The Kapa blog

Smart search on a docs site is AI-powered search that returns a direct, cited answer instead of a list of links. This guide explains how semantic smart search differs from keyword search like Algolia, how it works, and how kapa.ai adds it to any documentation platform.

A working AI assistant demo takes an afternoon; a production one takes many months, and most teams never make it. Here are the six common mistakes that make building a RAG assistant in-house so hard - and why buying a purpose-built platform like kapa.ai is the smarter move for most teams.

The best alternatives to building an AI assistant from scratch, for teams who want one grounded in their own docs, product, or support content. Compares managed RAG platforms, no-code builders, open-source frameworks, and cloud services, with a build-vs-buy decision framework.

What an AI support platform is, why technical products need a documentation-first one, and how kapa.ai grounds every answer in your docs and code - with citations, an "I don't know" guardrail, safe tool-calling agents, and proof points like Logitech's audited 99% accuracy.

There is no single most accurate enterprise AI assistant - it depends on your knowledge type. This guide explains what actually drives answer accuracy, compares horizontal assistants, knowledge management tools, and general LLMs, and shows why a purpose-built RAG platform wins on technical documentation.

How AI is changing enterprise documentation search in 2026: moving from keyword-matched document lists to retrieval-augmented answers that unify internal and external sources, cite their sources, respect permissions, and power both employee search and AI agents.

How much it costs to build and maintain an AI assistant or RAG system in-house, the hidden costs, when it is worth it, and how kapa.ai compares

The best AI documentation search tools for 2026, from Algolia to purpose-built kapa.ai, compared on answer accuracy, citations, and how they search your docs.