Knowledge Systems & RAG
Answers from your own documents — cited, access-controlled, and never made up.
In one line
Key takeaways
- RAG grounds answers in your own documents and cites the source, so staff and customers get your knowledge, not the model's guesswork.
- It respects who's allowed to see what — answers honor your existing access controls.
- When the documents don't contain the answer, a well-built system says so instead of inventing one.
- It's private to you: your documents are never used to train someone else's model.
Your business already knows the answer to most questions people ask it. It's just buried — in policy PDFs, contracts, a wiki nobody updates, years of support tickets, and a few people's heads. So staff interrupt each other to look things up, customers wait, and the same question gets answered from scratch a hundred times. A knowledge system fixes that by letting people simply ask, and getting back an answer drawn from your own documents — with a citation to the exact source, so they can check it. Plenaura builds that system end to end.
The obvious objection is: why not just use ChatGPT? Because a general chatbot answers from what it read on the public internet, and it will confidently invent details about your pricing, your policies, and your process — because it has never seen them. It has no idea what's in your contracts or which customer is allowed to know what. A retrieval-augmented (RAG) knowledge system is different by design: it first retrieves the relevant passages from your documents, then answers only from those passages, and shows you where each answer came from. Your knowledge, grounded and checkable — not a model's best guess.
Three properties make it safe to actually rely on. It's cited, so every answer points back to the source paragraph and a wrong or outdated one is easy to catch. It's honest — when your documents don't cover a question, a well-built system says "I don't have that" instead of inventing a plausible-sounding reply. And it respects who's allowed to see what: it honors your existing access controls, so a user only ever gets answers from documents they're already permitted to read, and sensitive material never leaks into the wrong person's answer.
It's also private and yours. Your documents are used to answer your questions — never to train a shared or third-party model — and the whole system runs on infrastructure you control, on-premise or in your own cloud account. You own the code and the configuration, with no per-seat platform tax and no vendor you can't leave. The outcome is simple: the knowledge scattered across your drives, wikis, and tools becomes something anyone can just ask, with an answer grounded in the real source and a citation to prove it. Work is scoped and quoted per project, on a clear timeline agreed up front.
What we can build for you
Internal staff knowledge assistant
A private assistant your team can ask instead of interrupting a colleague or digging through the wiki — HR policy, IT runbooks, product specs, SOPs. It answers from your own documents with a citation, so people get a fast, checkable answer rather than someone's half-remembered version.
Customer-facing support assistant
A support assistant grounded strictly in your help docs, policies, and knowledge base — so it answers customer questions accurately and points to the source, rather than improvising. When the docs don't cover something, it's built to hand off or say so, not to invent a policy you'll have to honor later.
Document Q&A over contracts and case files
Ask questions across a large set of contracts, case files, reports, or research and get an answer that quotes the relevant clause or passage. Instead of reading a hundred documents to find the one that matters, your team asks in plain language and jumps straight to the cited source.
Policy and compliance lookup
A lookup layer over your policies, regulations, and internal controls that returns the exact governing clause with its citation — so staff act on what the document actually says, not a paraphrase. Built to say "not covered" rather than guess, which is exactly the behavior compliance work needs.
Connecting your real knowledge sources
We connect the places your knowledge actually lives — shared drives, wikis, ticketing systems, databases, intranets — into one place people can ask, rather than searching each tool separately. As sources change, the index is kept in step so answers reflect the current documents, not last quarter's.
Citation and source verification
Every answer links back to the passage it came from, so a reader can verify it in one click and spot anything outdated or wrong. This turns the system from a black box into something auditable — an answer without a visible source is visibly an answer to distrust, which keeps everyone honest.
Access-control-aware answering
The system honors your existing permissions, so retrieval only ever draws from documents a given user is allowed to see. Two people can ask the identical question and correctly get different answers based on their access — confidential material never surfaces in a reply for someone who shouldn't have it.
How we deliver it
Map the sources and the real questions
We start by mapping where your knowledge actually lives and, just as importantly, the questions people really ask — from staff and customers. That tells us which sources matter, where answers currently get stuck, and what "a good answer" looks like, so we build for real use rather than a demo.
Ingest, chunk, and index — with permissions
We connect your sources and process the documents into retrievable pieces, carrying each document's access permissions through into the index. The result is a searchable knowledge base where who-can-see-what is enforced at retrieval time, not bolted on afterward as an afterthought.
Build grounded answering with citations
We build the retrieval and answer layer so responses are generated only from the passages your documents actually contain, each shown with its source citation — and we deliberately design the "I don't have that" behavior, so the system declines to answer rather than inventing one when retrieval comes up empty.
Evaluate on real questions and tune
We test the system against the real questions from step one, checking whether answers are correct, properly cited, and honest about gaps — then tune retrieval, chunking, and ranking until quality holds up. Accuracy is measured on your questions, not assumed from a polished first demo.
Deploy privately, monitor, and hand over
We deploy on your infrastructure so your documents and answers stay in your control, with monitoring to catch weak or missing answers as they come up in real use. Then we hand over the full system and documentation so your team can maintain and extend it — ongoing support is an option, never a lock-in.
The outcome
The knowledge trapped across your documents, wikis, and drives becomes something people can just ask — with answers grounded in the real source and a citation to prove it.
This is for you if
- Answers live buried in documents, wikis, and people's heads
- Staff or customers wait on someone to look things up
- You need answers that cite a trustworthy source, not a black box
- You want a private assistant that never leaks your documents to train someone else's model
What you get
- A private assistant that answers from your documents, with citations to the source
- Access controls so each user only gets answers from what they're allowed to see
- Honest 'I don't have that' behavior instead of confident invented answers
- Connection to your real document sources — drives, wikis, ticketing, databases
- The system deployed on your infrastructure and owned by you — your data stays yours
However we build it, you own it
Knowledge Systems & RAG — answered
Access control is enforced at the point of retrieval, not patched on at the end. When we ingest your documents, we carry each one's existing permissions into the index, so when a user asks a question the system only ever retrieves — and therefore only ever answers from — documents that specific user is allowed to see. Two people asking the same question can correctly get different answers based on their access, and material someone shouldn't see never makes it into their answer in the first place.
Most of what your knowledge already lives in: PDFs, Word documents, spreadsheets, slide decks, wiki and intranet pages, help-center articles, support tickets, and structured data in databases. On the source side we connect the systems you already use — shared drives, wikis, ticketing tools, internal databases — so people ask in one place instead of searching each tool separately. We scope exactly which sources to connect during discovery, based on where the answers people actually need are kept.
The system's index is kept in step with your source documents, so when a policy is updated or a new document is added, answers start reflecting the current version rather than a stale snapshot. Because every answer carries a citation to its source passage, an outdated or wrong answer is easy to spot and trace back — you can see exactly which document it came from and fix the document, not fight the model. We set the refresh approach to match how often your sources actually change.
Built-in AI search only sees the documents inside that one tool — your wiki's search doesn't know what's in your ticketing system, and vice versa. A knowledge system connects across your sources so people get one place to ask, with answers that honor your access controls and cite where each one came from. It also runs privately on your infrastructure and is owned by you, rather than being a feature of a product you rent and can't take with you. If a single tool's built-in search genuinely covers your need, we'll tell you to use it.
It's built to say so. When retrieval finds nothing relevant in the documents a user is allowed to see, a well-built system responds with an honest "I don't have that" instead of inventing a confident, plausible-sounding answer — which is exactly the failure mode that makes people stop trusting a general chatbot. Depending on the use case we can route those questions to a person, log them so you can see which gaps to fill in your documentation, or both. Declining to guess is a feature, not a limitation.
On your infrastructure, under your control — on-premise or in your own cloud account. Your documents are used to answer your questions and nothing else; they are never used to train a shared or third-party model, and they don't leave your environment to do so. You own the whole system — the code, the configuration, and the index — with no platform fees and no vendor you're locked into. For regulated or sensitive work, we can deploy fully on-premise so data never leaves your network at all.
Related use cases
Explore the rest
Ready to scope it? Let's talk.
A short call, then a clear, agreed scope in writing. No obligation, and an honest no if it isn't a fit.