NOAN vs Build-your-own RAG
RAG and a fact layer answer different questions. A retrieval pipeline, chunking, embeddings, a vector database, and sync jobs, answers what do our documents say. A fact layer answers what has the company approved as true. RAG shines over large corpora where the answer might be anywhere. It cannot make a document true, current, or authorized; retrieval quality is not truth quality. NOAN is deliberately the opposite shape: a small, curated, verified set of operating facts with an approval gate, version history, and an API. Build RAG when your problem is search. Buy the fact layer when your problem is that agents act on wrong information. Serious agent stacks often end up with both.
Side by side
Competitor claims last verified 26 August 2026| Dimension | NOAN | Build-your-own RAG |
|---|---|---|
| What it is | The company brain and API: verified facts, tasks, contacts, notes, and assets behind one REST endpoint | A pipeline you assemble: chunking, embeddings, a vector store, retrieval logic, and sync from your sources |
| Source of truth | Approved, versioned facts with an owner; one source of truth for the whole company | Whatever made it into the index, at whatever freshness your sync achieves |
| Verification | Facts enter through a verify gate: a human approves before anything reads it as true | None inherent; a retrieved chunk may be a draft, an old deck, or a contradicting doc |
| API | Plain REST plus MCP, bearer keys, unlimited API keys on every plan | Whatever you build and maintain |
| Agents | Built for agents: facts to ground on, a task board to control them, any model | Provides context for agents; control plane, approvals, and CRM are separate builds |
| Auditability | Every fact versioned with full history and Time Travel; every agent action lands on the task board | Citations to retrieved chunks if you build them; no approval trail exists to cite |
| Pricing model | Whole-workspace plans from $99/month; unlimited keys, no per-agent or per-task metering | Vector DB, embedding calls, and sync infrastructure, plus the engineering time that dwarfs all three |
| Lock-in | Facts are plain text behind a REST API; one GET exports everything | You own it all, and you maintain it all |
When to choose Build-your-own RAG
- Large document corpora where answers hide in thousands of pages: support archives, research, contracts
- Search and question answering is the product, and ranking quality is worth tuning
- You have the engineering appetite to own chunking, sync, and evaluation forever
When to choose NOAN
- The facts that matter fit in a curated brain: positioning, pricing, policies, product, process, contacts
- Wrong answers are expensive: you need approval before truth, versions after it, and an audit trail always
- You would rather ship agents this week than build retrieval infrastructure this quarter
The deeper comparison
Retrieval quality is not truth quality
A RAG pipeline can be excellent and still betray an acting agent, because its correctness ceiling is the corpus. Perfect retrieval over documents that include a stale deck returns the stale deck perfectly. Chunking can split a policy from its exception, embeddings can rank a confident draft above a boring final version, and none of these are bugs; they are what statistical retrieval over ungoverned text does.
The fact layer's move is to shrink the problem instead of ranking it. The claims an agent asserts in the world, prices, policies, positioning, promises, are few, and each one can afford an owner and an approval. Once they are governed, serving them is not retrieval at all: the agent asks for the pricing fact and receives the pricing fact, deterministically, with its version and approver attached.
Framed that way, the architecture question stops being RAG versus facts and becomes: which claims can you afford to leave probabilistic? Research can. Action cannot. Most serious stacks end up with retrieval for the long tail and a fact layer for the spine.
The build cost nobody budgets
The visible costs of DIY retrieval are manageable: a vector database, embedding calls, some hosting. The invisible cost is that you have signed up to own a pipeline forever: sync jobs that break when a source changes its export, chunking strategies that need re-tuning as documents evolve, evaluation harnesses to notice quality regressions, and re-indexing every time the embedding model improves. Teams routinely spend an engineer-quarter reaching adequate and then a background tax keeping it there.
NOAN's fact layer is a hosted API from day one: $99 a month, unlimited keys, and the grounding call is a GET your coding agent writes in a minute. It deliberately does not try to be your search engine. If you genuinely need semantic search over thousands of documents, build or buy retrieval for that corpus, and let it cite facts rather than compete with them.
The architecture that ends the argument
The pattern we see stabilize: a small verified fact base as the authority layer, retrieval over the big corpus as the research layer, and an explicit rule in every agent about which layer may be asserted to the outside world. The agent searches the corpus to think and reads the facts to speak. Wiring that rule is a paragraph in a system prompt, and it removes the entire class of incident where a confident retrieval became a customer-facing claim.
Common questions
Is NOAN a RAG system?
No. RAG retrieves passages from documents. NOAN serves discrete verified facts over a REST API; a GET returns the approved truth, no embeddings involved. Retrieval answers from likelihood; the fact layer answers from authority.
When is RAG the wrong choice for agents?
When the agent acts. Retrieval is probabilistic: the top chunk may be stale, a draft, or contradicted elsewhere. Fine for research. Dangerous for the agent quoting your price or your policy to a customer. Action needs an authoritative read, which is what a verify gate exists for.
Do teams use RAG and NOAN together?
Yes, and it is the sane architecture: RAG over the large corpus for research and recall, NOAN for the operating truths agents assert and act on. The agent searches one and obeys the other.
Is a vector database necessary for grounding AI agents?
Only if the grounding problem is search-shaped: large corpora where the answer might be anywhere. For asserting company truth, a vector database adds probability where you want authority. A fact layer serves the approved claim deterministically over a plain REST call, no embeddings involved.
What is the best alternative to building RAG in-house?
Split the problem. For the authoritative core, a hosted fact layer like NOAN replaces the build entirely: governed facts behind an API from day one. For genuine large-corpus search, managed retrieval products beat building from scratch. The expensive mistake is building one system and asking it to be both.
How do I stop my RAG agent from hallucinating company facts?
Move the facts out of the corpus. Retrieval cannot promise the top chunk is current or approved, so give the agent a deterministic source for assertable truth, a fact layer, and a rule that outward claims come only from it. Retrieval stays for research, where being probabilistic is acceptable.
Sources: NOAN API spec · All comparisons
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