Use case
Knowledge management
Capturing what the organisation knows — process notes, decisions, answers given once already — and keeping it findable and current. AI helps by drafting entries, spotting duplicates and flagging pages that contradict each other.
- Source
- Editorial ontology entry — no fetched document behind this page
- Evidence
- none on this page — it links to the pages that hold it
- Category
- Knowledge and search
- Typical risk
- medium
- Entry
- editorial · reviewed 25 Aug 2026
01What this is
Knowledge management is an editorial problem first and a retrieval problem second. The system holds a curated corpus with owners and review dates; AI drafts new entries from tickets, meetings and chat threads, suggests merges, and surfaces pages whose review date has passed.
A good deployment shows provenance for every generated entry, requires a human owner to accept it, tracks which pages answer real questions, and retires pages nobody uses. Search and authoring live in the same place, so an answer that was missing becomes a page rather than a second Slack thread.
The failure mode is a wiki that grows faster than it is reviewed: generated summaries pile up, two pages disagree, and staff stop trusting all of them. Measure staleness and contradiction, not page count.
- Typical data
- wiki pages, internal documents, support tickets, meeting notes
- Solution classes it admits
- Enterprise SaaS, Private cloud, Self-hosted
02Deployment options
- ASSESSMENT
On a neutral reading of this use case, Self-hosted is a strong alternative, Private cloud is a strong alternative and Enterprise SaaS is conditional.
- ASSESSMENT
Read as a generic reading of this page, not a recommendation: no organisation, size, jurisdiction, budget or technical capability has been supplied, so wherever an option depends on one of those, it says "unknown". Ask your own question to get a verdict that accounts for them.
Private cloud
STRONG ALTERNATIVEManaged model API or private model deployment in your cloud account
- ASSESSMENT
Two patterns fit inside one account in a region you name: call a managed foundation-model API such as Amazon Bedrock, Microsoft Foundry / Azure OpenAI, or Vertex AI; or deploy an open-weight model on GPU compute you control. Your application, retrieval layer, storage, identity and logs remain in your cloud boundary in both patterns.
- ASSESSMENT
The managed-API pattern can use closed-source frontier models without buying or operating GPUs. It is usually the fastest way to build a custom workflow, but prompts and retrieved context are processed by the managed service, so model availability, retention, abuse monitoring and regional routing must be checked for the exact feature and endpoint.
- ASSESSMENT
The private-model pattern gives more control over weights, serving and network paths, and can use managed endpoints or your own containers. It also makes your team responsible for capacity, patches, model upgrades, evaluation and failover.
- ASSESSMENT
The cloud provider becomes a data processor in either pattern: you need a DPA, a documented region, and an answer on cross-region routing and where support staff can access the environment from.
- RECOMMENDATION
Start with the managed-API pattern when the workflow is custom but model operations are not the source of competitive advantage; move to private model serving only if evaluation, volume, portability or the data boundary justifies the extra operations. Your stated technical capability is "unknown".
Self-hosted
STRONG ALTERNATIVEOpen-weight models on infrastructure you operate
- ASSESSMENT
Documents, queries and embeddings stay on machines you own, using an open-weight model whose licence you review. For a brief that involves confidential documents and personal data, that removes a model-API vendor from the data path rather than governing that transfer by contract.
- ASSESSMENT
It costs you the operational work instead: a GPU server, Docker, Linux, backups and a patching routine. Your stated technical capability is "unknown", which is the attribute this option most depends on.
- ASSESSMENT
No processor agreement, subprocessor list or cross-border transfer assessment is needed for the model itself, because no third party processes the content.
- RECOMMENDATION
Recommended where local processing is preferred (you did not say so) and the content is sensitive (confidential documents and personal data).
Enterprise SaaS
CONSIDER IFFinished closed-source cloud product with enterprise controls
- ASSESSMENT
This is a complete vendor application, not a model API: examples include an enterprise assistant, coding copilot or document product with the workflow, interface, connectors and administration already built. It can use closed-source cloud models while requiring no model hosting or application engineering from your team.
- RECOMMENDATION
Choose it when the product already performs the actual workflow and its controls meet your requirements. Do not choose it only because its underlying model is strong: a finished SaaS product is less flexible than building against a managed API when your process, integrations or review steps are organisation-specific.
- ASSESSMENT
Vendor commitments are treated as unverified until we have fetched the page that makes them. Until then this option carries questions to ask, not assurances: a signed data processing agreement covering the data you will actually put in; a documented data residency commitment naming the region, in the contract rather than a blog post; a written no-training commitment for your content, including uploads and connected sources; stated retention periods and a deletion path you can exercise; an administrative audit log you can export, and SSO with group-based access control.
- RECOMMENDATION
No jurisdiction was named, so this is the check rather than the conclusion: compare the vendor's stated processing locations and subprocessor list against the cross-border transfer rules wherever you operate before uploading anything.
03Deployment stacks
- Enterprise SaaSEnterprise SaaS assistant with governance controlsA business or enterprise plan from an established vendor — ChatGPT Enterprise, Microsoft 365 Copilot, Glean or an equivalent — connected to your identity provider, scoped by existing permissions, covered by a DPA, and rolled out behind a written policy.
- Private cloudPrivate-cloud RAG in a single regionA GPU instance in one region running vLLM and Open WebUI, a managed PostgreSQL with pgvector for chats and embeddings, object storage for the original files, and your existing identity provider for sign-in. Same software as the on-premise recipe; different trust boundary.
- Self-hostedPrivate company knowledge base (self-hosted RAG)Open WebUI as the employee interface, vLLM serving a Qwen2.5-14B-Instruct model on a single 24 GB GPU, PostgreSQL with pgvector for chats and embeddings, and OIDC single sign-on — all in Docker on one server in your office or colocation rack. Ollama replaces vLLM for teams under about 20 users; a 48 GB GPU lets you run a 32B model for better answers.
04Tools by hosting option
Self-hosted7
- HybridAnythingLLMDesktop and server application that turns a document set into a chat workspace, with per-workspace embeddings, multiple model back ends and a built-in vector store.
- HybridOnyxOpen-source enterprise search and chat over company systems, with connectors to common SaaS tools, permission-aware indexing and a self-hosted deployment path.
- Self-hostedpaperless-ngxSelf-hosted document management system that OCRs incoming scans and email, applies tags and correspondents through trainable rules, and keeps a searchable archive.
- LibrarypgvectorPostgreSQL extension adding vector types and HNSW or IVFFlat indexes, so embeddings live in the same database and the same backup as the rest of the application data.
- HybridQdrantVector search engine written in Rust with payload filtering, hybrid search, quantisation and snapshots. Runs as a single container or a cluster, or as a managed cloud service.
- Self-hostedRAGFlowRetrieval engine built around deep document parsing: layout-aware chunking of PDFs, tables and scans, citation-backed answers, and a visual pipeline for building knowledge bases.
- HybridWeaviateVector database with a schema model, hybrid keyword and vector search, and optional built-in vectorisation modules. Self-hosted or managed, single-tenant or multi-tenant collections.
Private cloud7
- HybridAnythingLLMDesktop and server application that turns a document set into a chat workspace, with per-workspace embeddings, multiple model back ends and a built-in vector store.
- SaaSGleanEnterprise search and assistant across company SaaS systems, with permission-aware indexing, a knowledge graph of people and content, and an agent-building layer.
- HybridOnyxOpen-source enterprise search and chat over company systems, with connectors to common SaaS tools, permission-aware indexing and a self-hosted deployment path.
- LibrarypgvectorPostgreSQL extension adding vector types and HNSW or IVFFlat indexes, so embeddings live in the same database and the same backup as the rest of the application data.
- HybridQdrantVector search engine written in Rust with payload filtering, hybrid search, quantisation and snapshots. Runs as a single container or a cluster, or as a managed cloud service.
- Self-hostedRAGFlowRetrieval engine built around deep document parsing: layout-aware chunking of PDFs, tables and scans, citation-backed answers, and a visual pipeline for building knowledge bases.
- HybridWeaviateVector database with a schema model, hybrid keyword and vector search, and optional built-in vectorisation modules. Self-hosted or managed, single-tenant or multi-tenant collections.
Vendor cloud7
- SaaSAtlassian RovoSearch, chat and agents across Atlassian products and connected third-party tools, using Atlassian’s cloud permissions model and admin controls.
- SaaSGleanEnterprise search and assistant across company SaaS systems, with permission-aware indexing, a knowledge graph of people and content, and an agent-building layer.
- SaaSNotion AIAI features inside the Notion workspace: search across connected apps, drafting in pages and databases, and meeting notes, governed by Notion workspace permissions.
- HybridOnyxOpen-source enterprise search and chat over company systems, with connectors to common SaaS tools, permission-aware indexing and a self-hosted deployment path.
- LibrarypgvectorPostgreSQL extension adding vector types and HNSW or IVFFlat indexes, so embeddings live in the same database and the same backup as the rest of the application data.
- HybridQdrantVector search engine written in Rust with payload filtering, hybrid search, quantisation and snapshots. Runs as a single container or a cluster, or as a managed cloud service.
- HybridWeaviateVector database with a schema model, hybrid keyword and vector search, and optional built-in vectorisation modules. Self-hosted or managed, single-tenant or multi-tenant collections.
On-premise (enterprise plan)2
- Self-hostedpaperless-ngxSelf-hosted document management system that OCRs incoming scans and email, applies tags and correspondents through trainable rules, and keeps a searchable archive.
- LibrarypgvectorPostgreSQL extension adding vector types and HNSW or IVFFlat indexes, so embeddings live in the same database and the same backup as the rest of the application data.
05Compliance hot spots
This use case usually raises confidentiality, intellectual property, personal data, auditability, retention, cross-border transfers, data processing agreement, logging, model training, security, transparency, vendor jurisdiction.
- Intellectual property
- No published jurisdiction page names this topic yet
- Auditability
- No published jurisdiction page names this topic yet
- Retention
- Hong Kong
- Cross-border transfers
- European UnionUnited KingdomChina (mainland)Hong KongTaiwan
- Data processing agreement
- Japan
- Logging
- European Union
- Model training
- European UnionSouth Korea
- Security
- China (mainland)
- Transparency
- United KingdomJapanSouth Korea
- Vendor jurisdiction
- China (mainland)
06Example questions
Each of these opens the question box with the text already in it. The answer is researched for your organisation, not for this page.
07Related use cases
- Knowledge and searchSales researchPreparing for a sales conversation: what the account does, who the buyer is, what changed recently, what was said last time. Combines public web sources with the CRM record, and personalises outreach from both.
- Knowledge and searchLegal researchFinding the statute, case or precedent that answers a question, and showing the passage. Distinct from contract review: the corpus is law and commentary, the standard for citation accuracy is absolute, and a fabricated citation is a professional risk.
- Knowledge and searchInternal company searchOne search box over the documents a company already has — shared drives, wikis, ticket systems, email attachments — answered by a model that quotes the source. The value is finding the right paragraph in a corpus nobody has read end to end, not writing new text.
- Knowledge and searchDocument Q&AAsk questions of a specific document or a small set of them and get an answer with the passage it came from. Narrower than company-wide search: the user already knows which file matters and wants it read carefully.
- Knowledge and searchCompany researchAssembling a picture of an organisation — ownership, filings, news, sanctions exposure, key people — from public sources, with each statement traceable to the page it came from. Used for onboarding, due diligence and competitor tracking.
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