Everything your team knows, instantly findable — and a straight answer on which one you actually need.
A curated wiki and RAG solve the same problem two different ways, and one is often cheaper and more trustworthy than the other. We help you figure out which one your business actually needs, then build it. Sometimes that's a wiki, sometimes RAG, and often both.
Two tools for one problem — knowing what you know
Both make your knowledge findable. They just go about it in completely different ways — and that difference is the whole decision.
A wiki
A human-curated source of truth — your team's knowledge written down, organized, and kept current by people. When someone needs an answer, they open the page and read it, exactly as it was written.
RAG
AI that answers questions in plain language by pulling from your own documents. You ask it something, it finds the relevant passages, and it writes back an answer — no hunting through pages to get there.
Wiki vs. RAG, across thirteen dimensions
No thumb on the scale. Some rows favor a wiki, some favor RAG, and plenty genuinely depend on your situation. Here's the whole picture.
| Dimension | WikiHuman-curated source of truth | RAGAI that answers from your docs |
|---|---|---|
| A · Knowledge & Structure | ||
| Knowledge scale | Small to moderate — dozens to a few thousand pages a human can keep organized. | Large to massive — thousands to millions of docs no one wants to hand-curate. Better fit |
| How knowledge is structuredDepends | Human-organized into categories and pages; structure is the product. | Left mostly as-is; software chunks and indexes meaning, so no manual structure is needed. |
| Best query typeDepends | Exact lookup — find the page, read the official text. | Natural-language questions and synthesis — answer in plain English from whatever's relevant. |
| Searchability / UX | Browse a menu or keyword search; fast when structure is good, frustrating when it isn't. | Ask conversationally; great when browsing is slow, but the answer is a summary, not the source page. Better fit |
| B · Accuracy & Trust | ||
| Answer accuracy / authoritativeness | Very high — you read the exact approved words a human wrote. Better fit | Good when the source is there, but answers are paraphrased/generated and can drift from the original wording. |
| Hallucination risk | Essentially none — it shows real pages, it doesn't invent text. Better fit | Real and ongoing — can state something confidently the docs don't support; mitigated by good retrieval + citations, never eliminated. |
| Auditability / traceability | Excellent — versioned pages, edit history, clear who approved this and when. Better fit | Partial — good systems cite chunks used, but the final wording is generated, so it's harder to prove an answer is official. |
| Freshness / updates | Instant once a human edits the page — but stale if nobody updates it. | Fresh automatically after re-indexing new docs — but a lag exists until re-indexing runs. Better fit |
| C · Cost & Effort | ||
| Setup effort / time | Low — pick a platform, create pages; days to a couple of weeks. Better fit | Higher — ingestion, chunking, embeddings, a vector store, and the app; weeks and specialized skills. |
| Ongoing cost | Low and predictable — a flat SaaS/hosting fee; no cost per question. Better fit | Variable — embedding + LLM API costs that rise with every query, plus vector-store hosting. |
| Maintainer & burdenDepends | Non-technical staff editing pages; burden is editorial discipline (keeping content current). | Technical owner managing the pipeline, embeddings, and index; burden is engineering. |
| D · Scale & Failure | ||
| Scale of docs / users | Content scaling limited by human curation; handles many readers cheaply. | Content scales to huge corpora easily; user/query scaling costs money — each question calls the model. Better fit |
| Typical failure modeDepends | Rot — pages go out of date because no one maintains them, and answers become quietly wrong. | Confident wrong answers — the model fills gaps or misreads retrieved text and states it as fact. |
Small to moderate — dozens to a few thousand pages a human can keep organized.
Large to massive — thousands to millions of docs no one wants to hand-curate.
Human-organized into categories and pages; structure is the product.
Left mostly as-is; software chunks and indexes meaning, so no manual structure is needed.
Exact lookup — find the page, read the official text.
Natural-language questions and synthesis — answer in plain English from whatever's relevant.
Browse a menu or keyword search; fast when structure is good, frustrating when it isn't.
Ask conversationally; great when browsing is slow, but the answer is a summary, not the source page.
Very high — you read the exact approved words a human wrote.
Good when the source is there, but answers are paraphrased/generated and can drift from the original wording.
Essentially none — it shows real pages, it doesn't invent text.
Real and ongoing — can state something confidently the docs don't support; mitigated by good retrieval + citations, never eliminated.
Excellent — versioned pages, edit history, clear who approved this and when.
Partial — good systems cite chunks used, but the final wording is generated, so it's harder to prove an answer is official.
Instant once a human edits the page — but stale if nobody updates it.
Fresh automatically after re-indexing new docs — but a lag exists until re-indexing runs.
Low — pick a platform, create pages; days to a couple of weeks.
Higher — ingestion, chunking, embeddings, a vector store, and the app; weeks and specialized skills.
Low and predictable — a flat SaaS/hosting fee; no cost per question.
Variable — embedding + LLM API costs that rise with every query, plus vector-store hosting.
Non-technical staff editing pages; burden is editorial discipline (keeping content current).
Technical owner managing the pipeline, embeddings, and index; burden is engineering.
Content scaling limited by human curation; handles many readers cheaply.
Content scales to huge corpora easily; user/query scaling costs money — each question calls the model.
Rot — pages go out of date because no one maintains them, and answers become quietly wrong.
Confident wrong answers — the model fills gaps or misreads retrieved text and states it as fact.
A quick gut check before you spend a dollar
Read down both lists. If most of one side sounds like your business, you already have your answer — and either answer is one we build.
Choose a wiki when
- Your knowledge set is small and stable
- Answers must be exact and verbatim — pricing, legal, SOPs
- Compliance or an audit trail matters
- The people maintaining it aren't technical
- Tight budget and near-zero run cost
- Query volume is low or predictable
- Your content is already well-structured
- You need one canonical, bookmarkable source of truth
Sounds like you?Good — a great wiki is the smart call, and it's exactly what we build and structure for you (and what we add RAG to when you outgrow it).
Choose RAG when
- Your corpus is large, sprawling, and unstructured
- People ask in plain language, not keywords
- Answers need synthesis across many documents
- Content changes constantly from many contributors
- Nobody will curate structure, but the docs exist
- You want conversational follow-up questions
- Keyword search returns too many hits; browsing is too slow
- Knowledge is buried in long formats nobody reads
Start with a wiki, add RAG later
These two aren't rivals. They're steps in the same journey — and the order matters more than most vendors admit.
Your Wiki
A clean, curated home for everything your team knows — organized, current, and trustworthy.
Layer on RAG
AI retrieval on top of that clean corpus — added once the knowledge is large and busy enough to truly need it.
A wiki isn't RAG's competitor — it's the clean corpus RAG reads from. Get the wiki right and you've already built the foundation good retrieval depends on. Most SMBs with a tidy, stable knowledge set are better served by a great wiki; RAG earns its complexity when the corpus is large, messy, and constantly changing and people ask real questions in plain language. If a well-run wiki would solve it, building RAG instead is paying more for a less trustworthy answer.
We build the right one — and we'll tell you which that is
Whether the answer is a wiki, RAG, or the wiki-then-RAG path, the work is the same shape: find the knowledge, choose honestly, and build something your team actually uses.
We map what you know
Before we build anything, we take inventory — every document, spreadsheet, and bit of knowledge living in someone's head. You can't organize what you haven't found, so we find it first.
We recommend honestly
We tell you which tool fits, and when a well-run wiki beats RAG at a fraction of the cost, we say so plainly. We would rather build you the right thing than the expensive thing.
We build the wiki
Structure, platform, and migration, all handled. We design how it's organized, pick the tool that fits your team, and move your scattered docs into one clean, searchable home people will actually use.
We build RAG
When your knowledge is too large or too fast-moving for pages alone, we build RAG on top — usually starting from the wiki, then layering AI retrieval on once the corpus truly earns it.
What teams ask before they choose
Isn't RAG just better than a wiki?
Not usually. RAG is more powerful, which is a different thing than better. A wiki gives you the same answer every time, word for word, and anyone can see exactly where it came from. RAG earns its place when your knowledge is too big or too fast-changing to browse — but power you don't need is just cost and risk you didn't have to take on.
Can't I just point ChatGPT at my documents?
You can, and for a quick personal lookup it's fine. The trouble starts when the answer matters. Consumer chatbots don't reliably cite their source, they blend your documents with whatever they learned elsewhere, and they'll answer confidently even when they're wrong. Production RAG is built to pull only from your approved content and show its work — that's the gap between a neat demo and something your team can stand behind.
What does each cost to run over time?
A wiki costs mostly discipline. Someone has to keep it current, but its running cost is close to nothing. RAG adds ongoing costs: the AI calls it makes, the infrastructure behind it, and re-indexing as your documents change. Neither is expensive when it fits the job — the waste comes from running RAG where a wiki would have done fine. We put the honest cost of each in front of you before you commit.
We already have a messy wiki — do we start over?
Almost never. A messy wiki usually means good content with bad structure, and structure is fixable. We audit what you have, keep what's worth keeping, reorganize it, and retire the dead pages. Starting from scratch throws away years of your team's knowledge for no reason.
We build both. Let's build the one that's right for you.
Tell us what your team keeps failing to find, and we'll build the one that's actually right for you — even when that's the cheaper one. Wiki, RAG, or wiki-then-RAG: you get a straight recommendation and a system your people actually use.