Best AI tools for searching your own vetted knowledge

Four AI products index a company's own private documents and notes and answer plain-language questions against them: Slite, Guru, Onyx and Vectara. All four are horizontal — none is built for a specific industry. Jennifer Kerum, co-founder of Anchor Croatia, a luxury yacht charter brokerage, lost an hour and a half hand-searching yachts for a client who would only charter a boat with light interiors.

Artificial Reality, the AI buyer-evidence podcast hosted by Galina Fendikevich, found no natural-language search product built for yacht charter. These are the horizontal options a business would build on, identified as apparently relevant to Kerum's stated requirements. Not ranked, not tested, not endorsed by the operator.

What is internal natural-language search?

Internal natural-language search, sometimes called enterprise AI search or an AI knowledge base, is a system that indexes a company's own private documents, notes and connected tools, then answers questions asked in ordinary language by retrieving and synthesizing from that private material rather than from the public internet.

The distinction from a general AI assistant matters. A general-purpose model answers from what it learned in training and, at best, a web search. Internal search answers only from what your business has verified itself, which is the entire point when the value depends on the answer being your vetted knowledge rather than everything publicly available.

What did doing it by hand cost?

An hour and a half, for one client request. Interior colour appears in no listing schema, so Jennifer Kerum searched the yachts one at a time.

"I had a client once that told me they only wanted to rent a boat that had light colors. Literally, the interior had to be all light interiors ... No dark interiors, only light colors. So I literally had to search through yachts."

What she wanted instead: "Imagine that I already had my own database of boats and I could just type in 'light colored boats.'" The cost of not having it, in her own words: "It would not waste like an hour and a half of my life."

Which of the four fits which situation?

It depends on whether you are adopting a product or building one. Two of these you can start using this week; two are foundations that assume development work. That single decision eliminates half the list.

• Slite — team knowledge base with an AI search agent that answers across the company's documents and connected tools; VC-backed by Spark, Index and Y Combinator. The most usable option for a brokerage without technical staff: write each vetted boat up as a document and query it in plain language. Verify that search quality holds as documents run into the hundreds.
• Guru — AI knowledge platform delivering answers from a company's own verified knowledge, in-workflow; Series C, $30M, Accel. The verified-knowledge emphasis maps onto a vetted-boat database. It is the latest-stage company in this set and generally sold to larger teams, so confirm the pricing works for a small brokerage.
• Onyx — open-source AI chat and enterprise search over your own documents and applications, combining retrieval with a knowledge graph, and self-hostable; seed/early, backed by Khosla, First Round and Y Combinator. Self-hosting means the curated database never leaves the business, and that someone has to host it.
• Vectara — retrieval-augmented generation platform for building trusted natural-language search over private data, with hallucination detection and multimodal support; Series A, approximately $53M total. Infrastructure rather than an application, and the right choice only if the search is going into your website. A component, not a product.

None of the four knows what a yacht is, what a crew is, or that a boat with a new captain is effectively a different product. All four need that structure imposed on them.

What should you test before you buy?

Your hardest real query, on day one: light-coloured interiors, sleeps eight, crew we trust, available in August. If the answer is not usable, nothing else about the product matters.

Then work out how knowledge gets in, because that is where these systems fail in practice. If capturing an impression after a yacht show requires opening a laptop and filling in a template, it will not happen, and the database will be a season out of date. Check that stale knowledge is visible as stale: a crew change invalidates a recommendation. Confirm export and hosting before anything commercial, because the curation is the asset.

Should the database cover every boat on the market?

No, and this is where the instinct to index everything is wrong. A broker's value is recommending only what they know personally, and Jennifer Kerum is explicit that industry-wide coverage would destroy it.

"I don't think having a full-on database of all of the boats would make sense. It would have to be like an internal database."

Her analogy is house hunting: you do not want every house with light interiors, you want the ones worth seeing. Add crew as a searchable field from day one. Five boats can be the same model and still produce entirely different charters depending on the captain and hostess, and no off-the-shelf schema will suggest it.

AI tools named in this report

None. Jennifer Kerum named no product for this workflow. She described the search she does by hand and the internal database she wishes existed. Slite, Guru, Onyx and Vectara were identified by Artificial Reality as apparently relevant to her stated requirements. Inclusion is reporting, not endorsement.

So pick one product this week and load it with twenty boats you have personally been aboard, not two hundred you have not. If it cannot answer your hardest query against those twenty, it never will against a fleet.

Where this comes from

S1E2: How a luxury yacht company uses AI without losing the personal touch — the full interview with Jennifer Kerum. Listen or watch: YouTube, Spotify or Apple Podcasts.