Which AI model do you use for a knowledge base?

We pick the model that fits the task (ChatGPT, Claude, GigaChat, YandexGPT, or others) depending on your document languages, data sensitivity, and where the data can live. A knowledge base over Russian-language docs on-premise has different constraints than English docs in the cloud. You're not locked to one provider.

How does it avoid making things up?

The AI is set up for retrieval-grounded answers: it only answers from passages it actually retrieved from your docs, and it cites the source. When the answer isn't in the documents, it says so instead of guessing. This is the core design choice that makes an internal knowledge base safe to trust.

Which sources can it search: Notion, Google Drive, Confluence?

Notion, Google Drive, and Confluence have native or API paths. SharePoint, local file shares, PDFs, and past support tickets work too. We confirm which sources to connect on the intro call and index the highest-value one first.

What about confidential documents and access control?

We scope retrieval to respect your existing permissions so the AI never surfaces a document to someone who couldn't already open it. Sensitive sources can be excluded entirely or restricted to specific teams. Access is least-privilege by default and logged.

What's the cost for a knowledge base setup?

It's priced on brief, scoped to how many sources you connect and how sensitive the data is. Ongoing tuning (re-indexing, gap fixes, new sources) runs on a monthly retainer by arrangement. Provider credit usage is a predictable monthly amount that scales with how many questions get asked. Request an estimate and we'll scope it to your setup.

AI Setup for Internal Knowledge Base and Search

Every company already has the answers written down somewhere. The problem is they're spread across Notion, Google Drive, Confluence, old email threads, and one Slack channel three people remember. Employees ping a colleague instead of searching, and that colleague loses 20 minutes to a question they've answered ten times. An AI knowledge base collapses this: it reads your documents, answers questions in natural language, and cites where the answer came from. We cover which sources to index first, how to keep answers grounded in real docs, and how to roll it out so people actually trust it.

Where AI earns its keep on a knowledge base & internal search team

Ask-in-Slack Q&A over company docs

An employee asks a question in Slack ('what's the parental leave policy for contractors?'). The AI retrieves the relevant passages from your policy docs, answers in plain language, and cites the source document and section. No more pinging HR or ops for the same 20 questions. First-answer time drops from hours to seconds.

Grounded answers with citations, not guesses

Every answer links back to the exact document and line it came from, so the reader can verify. When the docs don't contain the answer, the AI responds '[needs-human]' with a one-sentence summary of the gap instead of inventing something. A confident wrong answer about a policy is worse than a flagged unknown.

New-hire onboarding self-service

New hires ask the AI where things live: the VPN setup guide, the expense process, who owns billing, the deploy runbook. They get answers on day one without booking time with five people. Onboarding load on the team drops and new hires ramp faster.

Stale-doc and gap detection

The AI logs every question it couldn't answer and every doc that contradicts another. You get a weekly list of the top unanswered questions and the outdated pages. Your knowledge base improves from real usage instead of a once-a-year cleanup nobody schedules.

Rollout playbook

01
Index one source first, not all of them
Pick the single source people ask about most (usually the HR/ops policy space or the engineering wiki). Index that one, get answers grounded and cited, and prove the loop works before adding Drive, Confluence, and the rest. Breadth without accuracy just produces confident nonsense.
02
Audit and clean the docs before going live
The AI is only as good as what it retrieves. Spend the first few days finding what's missing, what's outdated, and what answers only live in someone's head or a Slack thread. Cleaning this up is useful even if you never ship the AI, and it's the difference between trusted answers and garbage.
03
Set access controls before opening it up
Not every employee should retrieve from every doc. We scope retrieval to respect existing permissions so the AI never surfaces a salary sheet or a legal draft to someone who shouldn't see it. Least-privilege access is configured before the first employee asks a question.
04
Launch to one team, then expand by source
Start with the team that owns the indexed source, since they can spot wrong answers instantly. Once they trust it, open it to the wider org and add the next document source. Each new source is a configuration step, not a rebuild.

AI we set up for knowledge base & internal search teams

Questions

Plan a knowledge base & internal search-team rollout

20-min intro call. We'll sanity-check your stack and give you a sequencing recommendation.

Book a 20-min intro call Or send a note

For other teams

free tools

Turn this into your next step

Read the full setup guide →