Use case
Internal company search
One 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.
- 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
- high
- Entry
- editorial · reviewed 25 Aug 2026
01What this is
Internal search indexes existing repositories, splits documents into passages, embeds them, and retrieves the passages that match a question before a model writes an answer over them. Connectors and permissions decide whether it works: the system must see exactly what the asking user is allowed to see.
A good deployment cites the source file and page, re-checks permissions on every query rather than at index time only, refreshes when documents change, and says "nothing found" instead of improvising. Retrieval quality is measured against a labelled question set before rollout.
The usual failures are permission leakage through one shared index, stale drafts presented confidently, chunking that cuts tables and clauses in half, and a corpus so noisy that the best match is a superseded file. Most of the effort goes into connectors, access control and evaluation — not the model.
- Typical data
- client documents, internal documents, wiki pages, email, contracts
- 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-hosted22
- 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.
- HybridauthentikIdentity provider with OpenID Connect, SAML and proxy-based authentication, application-level policies and a forward-auth outpost for services that have no login of their own.
- HybridChromaEmbedded and server-mode vector store with a small API surface, often used for prototypes and single-node retrieval before a larger engine is justified.
- HybridDifyPlatform for building LLM applications: visual workflow editor, retrieval pipelines, agent tools, prompt management and an API layer. Available self-hosted or as a managed cloud service.
- LibraryDoclingDocument conversion toolkit that parses PDF, Office and image files into structured Markdown or JSON, preserving reading order, tables and figures for downstream retrieval.
- HybridFlowiseVisual builder for LLM chains and agents, with retrieval nodes, tool calling and an API or embeddable chat widget for the finished flow.
- Self-hostedKeycloakIdentity and access management server providing OpenID Connect and SAML single sign-on, user federation, groups and roles for self-hosted applications.
- Self-hostedKotaemonDocument question-answering application with a configurable retrieval pipeline, inline citations that highlight the source passage, and support for local or hosted models.
- FrameworkLangChainLibrary for composing model calls, retrieval and tools into applications, with adapters for most providers and vector stores. A building block, not a deployable product.
- HybridLangflowVisual environment for composing LLM pipelines and agents from components, with export to a Python application or an API endpoint.
- HybridLangfuseTracing, evaluation and prompt management for LLM applications, self-hostable so prompts and responses can stay inside the network being audited.
- FrameworkLlamaIndexData framework for retrieval applications: loaders for many document types, indexing and query pipelines, and evaluation helpers for retrieval quality.
- HybridMilvusDistributed vector database designed for large collections, with several index types, GPU indexing options and a separated storage and compute architecture.
- 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-hostedOpen WebUISelf-hosted chat interface for local and hosted models, with user accounts, groups, document upload and built-in retrieval. Runs in Docker against Ollama, vLLM or any OpenAI-compatible endpoint.
- 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.
- Self-hostedPostgreSQLRelational database used here as the system of record for documents, metadata and job queues, and — with pgvector — for embeddings as well.
- 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.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
- Model servervLLMHigh-throughput GPU inference server using paged attention and continuous batching, with an OpenAI-compatible API. Built for many concurrent users rather than single-session use.
- 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 cloud22
- PlatformAmazon BedrockAWS service offering models from several providers behind one API in a chosen region, with VPC endpoints, IAM control, guardrails and knowledge bases for retrieval.
- 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.
- HybridauthentikIdentity provider with OpenID Connect, SAML and proxy-based authentication, application-level policies and a forward-auth outpost for services that have no login of their own.
- PlatformAzure OpenAI ServiceOpenAI models served from a customer-selected Azure region under Azure commercial terms, with private networking, content filtering and Entra ID integration.
- HybridDifyPlatform for building LLM applications: visual workflow editor, retrieval pipelines, agent tools, prompt management and an API layer. Available self-hosted or as a managed cloud service.
- LibraryDoclingDocument conversion toolkit that parses PDF, Office and image files into structured Markdown or JSON, preserving reading order, tables and figures for downstream retrieval.
- SaaSGleanEnterprise search and assistant across company SaaS systems, with permission-aware indexing, a knowledge graph of people and content, and an agent-building layer.
- PlatformGoogle Vertex AIGoogle Cloud platform for Gemini and third-party models with regional endpoints, VPC Service Controls, grounding against your own data and enterprise IAM.
- Self-hostedKeycloakIdentity and access management server providing OpenID Connect and SAML single sign-on, user federation, groups and roles for self-hosted applications.
- FrameworkLangChainLibrary for composing model calls, retrieval and tools into applications, with adapters for most providers and vector stores. A building block, not a deployable product.
- HybridLangfuseTracing, evaluation and prompt management for LLM applications, self-hostable so prompts and responses can stay inside the network being audited.
- FrameworkLlamaIndexData framework for retrieval applications: loaders for many document types, indexing and query pipelines, and evaluation helpers for retrieval quality.
- HybridMilvusDistributed vector database designed for large collections, with several index types, GPU indexing options and a separated storage and compute architecture.
- 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-hostedOpen WebUISelf-hosted chat interface for local and hosted models, with user accounts, groups, document upload and built-in retrieval. Runs in Docker against Ollama, vLLM or any OpenAI-compatible endpoint.
- 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.
- Self-hostedPostgreSQLRelational database used here as the system of record for documents, metadata and job queues, and — with pgvector — for embeddings as well.
- 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.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
- Model servervLLMHigh-throughput GPU inference server using paged attention and continuous batching, with an OpenAI-compatible API. Built for many concurrent users rather than single-session use.
- 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 cloud22
- PlatformAmazon BedrockAWS service offering models from several providers behind one API in a chosen region, with VPC endpoints, IAM control, guardrails and knowledge bases for retrieval.
- SaaSAtlassian RovoSearch, chat and agents across Atlassian products and connected third-party tools, using Atlassian’s cloud permissions model and admin controls.
- PlatformAzure OpenAI ServiceOpenAI models served from a customer-selected Azure region under Azure commercial terms, with private networking, content filtering and Entra ID integration.
- SaaSChatGPT EnterpriseOpenAI’s administered ChatGPT tier with SSO, workspace controls, retention settings, connectors to company systems and business terms that differ from the consumer product.
- HybridChromaEmbedded and server-mode vector store with a small API surface, often used for prototypes and single-node retrieval before a larger engine is justified.
- HybridDifyPlatform for building LLM applications: visual workflow editor, retrieval pipelines, agent tools, prompt management and an API layer. Available self-hosted or as a managed cloud service.
- HybridFlowiseVisual builder for LLM chains and agents, with retrieval nodes, tool calling and an API or embeddable chat widget for the finished flow.
- SaaSGleanEnterprise search and assistant across company SaaS systems, with permission-aware indexing, a knowledge graph of people and content, and an agent-building layer.
- PlatformGoogle Vertex AIGoogle Cloud platform for Gemini and third-party models with regional endpoints, VPC Service Controls, grounding against your own data and enterprise IAM.
- HybridLangflowVisual environment for composing LLM pipelines and agents from components, with export to a Python application or an API endpoint.
- HybridLangfuseTracing, evaluation and prompt management for LLM applications, self-hostable so prompts and responses can stay inside the network being audited.
- SaaSMicrosoft 365 CopilotAssistant embedded in Word, Excel, Outlook and Teams, grounded in the tenant’s own content through Microsoft Graph and governed by existing Microsoft 365 permissions.
- HybridMilvusDistributed vector database designed for large collections, with several index types, GPU indexing options and a separated storage and compute architecture.
- SaaSMoveworksEmployee support platform that resolves IT, HR and facilities requests through chat, integrating with service-desk and identity systems to complete routine actions.
- 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.
- SaaSPerplexity EnterpriseSearch-first assistant that answers with web citations and can search connected internal files, sold with enterprise administration, SSO and a data-processing addendum.
- 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.
- Self-hostedPostgreSQLRelational database used here as the system of record for documents, metadata and job queues, and — with pgvector — for embeddings as well.
- 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.
- HybridUnstructuredLibrary and hosted API that partition documents of many formats into typed elements for indexing, with connectors to common storage systems and vector databases.
- 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)5
- Self-hostedKeycloakIdentity and access management server providing OpenID Connect and SAML single sign-on, user federation, groups and roles for self-hosted applications.
- Self-hostedOpen WebUISelf-hosted chat interface for local and hosted models, with user accounts, groups, document upload and built-in retrieval. Runs in Docker against Ollama, vLLM or any OpenAI-compatible endpoint.
- 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.
- Self-hostedPostgreSQLRelational database used here as the system of record for documents, metadata and job queues, and — with pgvector — for embeddings as well.
- Model servervLLMHigh-throughput GPU inference server using paged attention and continuous batching, with an OpenAI-compatible API. Built for many concurrent users rather than single-session use.
05Compliance hot spots
This use case usually raises personal data, confidentiality, data residency, model access, logging, retention, security, cross-border transfers, data processing agreement, model training, transparency, vendor jurisdiction.
- Data residency
- No published jurisdiction page names this topic yet
- Model access
- No published jurisdiction page names this topic yet
- Logging
- European Union
- Retention
- Hong Kong
- Security
- China (mainland)
- Cross-border transfers
- European UnionUnited KingdomChina (mainland)Hong KongTaiwan
- Data processing agreement
- Japan
- Model training
- European UnionSouth Korea
- 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 searchKnowledge managementCapturing 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.
- 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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