Knowledge with structure. Not another folder.
nara Memory is a typed knowledge layer. Every entry is a reviewed object with a defined type, linked into a knowledge graph that agents and people can search.
Every entry has a type.
In most knowledge bases, everything sits side by side as free text: guides, notes, outdated screenshots. nara Memory works differently. Schemas define knowledge types such as error patterns, guides, products, and processes. Every entry is a typed object with clear fields. An error pattern looks different from a guide, and exactly that makes both usable by machines. Tickets, devices, and tools are mirrored into Memory as objects too: your knowledge and your operations live in the same model.
Linked, not filed away.
Knowledge graph
Objects are linked through relationships: symptom to cause, product to guide. Whoever finds an error pattern finds the path to the fix along with it.
Semantic search
Search runs on embeddings. It finds the right knowledge even when the ticket uses completely different words than the article.
Import from your formats
PDF, DOCX, HTML, TXT, CSV, and images: nara extracts typed objects from them. Existing documentation does not have to be rewritten.
Operations in the same model
Tickets, devices, and tools are objects in Memory themselves. Agents see not only what is documented but also what it refers to.
Nothing enters the knowledge unreviewed.
An agent is only as good as what it relies on. That is why at nara a human decides what becomes knowledge.
Import
Documents enter the system as PDF, DOCX, HTML, TXT, CSV, or image.
Extract
nara extracts typed objects from them: error patterns, guides, products, processes.
Review
Every extracted object waits for human review.
Only after approval does it become part of the knowledge.
Link
Approved objects are linked in the knowledge graph and are available in semantic search from that moment on.
nara tells you what is missing.
Most knowledge bases grow where someone had time, not where the demand is. nara compares your knowledge against the real ticket volume and identifies where articles are missing. For the largest gaps, nara generates article drafts that your team only needs to review and approve. In an anonymized customer project, nara checked 85 existing knowledge articles against real demand and generated 20 article drafts for the largest gaps.
Frequently asked questions about nara Memory
Do we have to rewrite our documentation?
No. nara imports PDF, DOCX, HTML, TXT, CSV, and images and extracts typed objects from them. Your team reviews the results instead of starting from zero.
Can the AI create knowledge without review?
No. Extracted objects and generated article drafts are only accepted after human review. No approval, no knowledge.
What happens when the ticket uses different terms than the article?
Semantic search works with embeddings and finds knowledge even when the wording differs. "My computer will not start" also hits the article that talks about boot problems.
How do we know which articles we are missing?
nara compares the existing knowledge against your real ticket volume, shows the gaps, and generates drafts for the most important ones.
How large are your knowledge gaps?
nara compares your existing articles against your real ticket volume and shows where knowledge is missing. The result is yours, whatever happens next.
