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Keeplore vs RAG
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RAG finds answers in documents that already exist. Keeplore creates the documents that don't, by interviewing the people who know.
They are not rivals. You keep your RAG. Keeplore is knowledge capture for AI agents: it adds the rules that were never written down.
What does RAG do well?
RAG (retrieval-augmented generation) lets an agent look up written sources before it answers. For anything that is documented, it is the right tool.
If your refund policy, product specs and setup guides are written and current, retrieval over them works. It scales with your documents. It needs no interviews.
RAG is a technique, not one product. Setups vary, so this page compares Keeplore with a typical setup and makes no claims about any vendor's.
Where does RAG run out?
Retrieval cannot find what nobody wrote down. Two cases come up.
1. The exception lives in someone's head. Say Acme's help centre says annual plans can be refunded within 30 days. Its senior support person knows that after the window they offer a prorated credit, and that anything over €500 goes to finance first. No document says so, so there is nothing to retrieve. The agent quotes the policy or escalates.
2. The documents disagree. Two articles give different answers and retrieval returns both. Keeplore does not fix this. It flags a new rule that contradicts a rule you have already approved, which covers Keeplore's own rules only. It does not audit your help centre. If conflicting articles are your problem, fix the articles first.
What does Keeplore do instead?
Keeplore interviews the person. An AI interviewer chats with them for 30 to 45 minutes and keeps asking about exceptions, approvals, clients and workarounds. Their answers become rules. Each rule has a condition (when), an action (do), a reason (why) and the exact quote it came from. Keeplore drops any rule whose quote does not appear in what the employee typed. A manager approves, edits or rejects each rule. Your agent then calls POST /api/v1/query and gets an answer plus the approved rules it used, with the name and role of the person each came from.
For the Acme case above, the captured rule is: when a customer on an annual plan asks for a refund after the 30-day window, offer a prorated credit for the months left, and if it is over €500, hand over to a person for finance approval. See How Keeplore works for the full walk-through.
Does Keeplore's query step also retrieve?
Yes. Keeplore's query step also retrieves over approved rules.
When your agent asks a question, Keeplore looks up your approved rules. A model then writes a short answer from them. For a small set of rules, all of them go to the model. For a larger set, Keeplore narrows them with a full-text search first.
So the difference is not "retrieval versus no retrieval". The difference is:
- Where the source material comes from. RAG retrieves from documents you already have. Keeplore retrieves from rules it created by interviewing your people.
- What happens when nothing matches. If no approved rule applies, the answer is empty and so is the list of rules. Your agent can treat that as "hand over to a person". When a rule does apply, the rules the model used come back with the answer so your agent, or you, can check. The answer text itself is model-written and is not checked against the rules.
How do they compare?
Columns describe a typical RAG setup, not any vendor.
| Typical RAG setup | Keeplore | |
|---|---|---|
| Source material | Existing documents | Interviews with people |
| Unit of knowledge | A chunk of a document | A rule: when, do, why, and the exact quote |
| Who checks it | Whoever owns the document, when it is written. Chunks are not reviewed one by one | A manager approves each rule before an agent can see it |
| When it doesn't know | Varies by setup | The answer is empty if no approved rule applies |
| Best for | Documented, stable, factual answers | Exceptions, approvals, client quirks and workarounds |
How do I use both?
Keep your RAG over the help centre. Have your agent call Keeplore's query endpoint for the exceptions.
Or take the rules to where your agent already looks. GET /api/v1/export returns every approved rule as JSON or Markdown. Load it into your agent's guidance or knowledge feature, or add it to the sources your RAG already searches.
There is no pre-built integration with agent platforms today. Export is manual: when you approve new rules, export again. Rules you paste into another tool follow that tool's behaviour, so the empty-answer and approved-only guarantees apply only when your agent calls Keeplore's query API.
When don't you need Keeplore?
- Your work is fully documented and the documents are current. Retrieval is enough.
- Nobody leaves, retires or changes role, and nobody holds exceptions in their head. There is nothing to capture.
- Your agent's wrong answers come from outdated or conflicting articles. Fix the articles first. Keeplore will not repair them.
Related
01Can I use Keeplore with my existing RAG system?
Yes. Your agent can call the query API, or you can export the approved rules as JSON or Markdown and add them to the sources it already searches. There is no pre-built integration, and the export is manual.
02Does Keeplore use RAG itself?
Its query step looks up approved rules and has a model write a short answer from them. The rules come from interviews, not from your documents.
03What does Keeplore return when there is no matching rule?
An empty answer and an empty list of rules. Your agent can treat that as "hand over to a person".
Try it on one exception.
Pick one exception your agent gets wrong. Interview the person who handles it. Read the rule that comes back.