Use case
Document Q&A
Ask 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.
- 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
Document Q&A puts one contract, report, policy or filing in front of a model together with the question, either whole (long context) or as retrieved passages. Because the corpus is small and chosen by the user, the retrieval problem shrinks and the reading-comprehension problem grows.
Good implementations quote the exact passage, give page or clause references, refuse when the document does not contain the answer, and handle scanned pages by running OCR first rather than silently reading an empty layer. Tables, footnotes and appendices are treated as content, not decoration.
Pitfalls: answers assembled from two documents without saying which is which; long-context models that lose the middle of a large file; and confident summaries of a scanned PDF whose text layer was never extracted. Version and retention rules matter — uploaded documents often outlive the question.
- Typical data
- client documents, contracts, reports, policies, scanned documents
- 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, 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).
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
- Self-hostedDocument classification and OCR pipelineDocling (or Unstructured) converts and OCRs, embeddings plus rules classify, a local model reads only the documents that need reading, and Paperless-ngx gives the business a place to search and file. Starts CPU-only; a GPU is added when the volume justifies it.
- 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.
- Self-hostedContract review and clause extractionDocling converts the contract to structured text with layout preserved; a 32B open-weight model on vLLM fills a JSON schema of clause fields under constrained decoding; results land in PostgreSQL with pgvector for search and precedent lookup; a reviewer approves in the UI.
04Tools by hosting option
Self-hosted20
- 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.
- 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-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.
- Self-hostedLibreChatSelf-hosted multi-model chat application with authentication, per-conversation model switching, plugins, file upload and an admin configuration file. Familiar interface for staff moving off consumer tools.
- FrameworkLlamaIndexData framework for retrieval applications: loaders for many document types, indexing and query pipelines, and evaluation helpers for retrieval quality.
- HybridLM StudioDesktop application for downloading and running open-weight models locally, with a chat interface and a local OpenAI-compatible server. Windows, macOS and Linux.
- 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.
- 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 cloud20
- 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.
- 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.
- 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.
- SaaSHarveyDomain platform for law firms and legal departments covering document review, drafting and research workflows over a firm’s own material, sold with enterprise security commitments.
- 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.
- Self-hostedLibreChatSelf-hosted multi-model chat application with authentication, per-conversation model switching, plugins, file upload and an admin configuration file. Familiar interface for staff moving off consumer tools.
- 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.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
- 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.
- 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 cloud19
- 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.
- 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.
- SaaSClaudeAnthropic’s assistant, available as a team and enterprise product with SSO, audit logs, project workspaces and commercial terms, and as an API for building applications.
- SaaSDeepLTranslation service for text and documents with glossary support, a document API that preserves formatting, and business plans that state how submitted text is handled.
- 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.
- 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.
- SaaSHarveyDomain platform for law firms and legal departments covering document review, drafting and research workflows over a firm’s own material, sold with enterprise security commitments.
- HybridLangflowVisual environment for composing LLM pipelines and agents from components, with export to a Python application or an API endpoint.
- HybridMilvusDistributed vector database designed for large collections, with several index types, GPU indexing options and a separated storage and compute architecture.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
- 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.
- 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)6
- SaaSDeepLTranslation service for text and documents with glossary support, a document API that preserves formatting, and business plans that state how submitted text is handled.
- Self-hostedLibreChatSelf-hosted multi-model chat application with authentication, per-conversation model switching, plugins, file upload and an admin configuration file. Familiar interface for staff moving off consumer tools.
- SaaSMistral Le ChatAssistant and API platform from a European vendor, offered as a hosted service and, for enterprise customers, as a deployment inside the customer’s own infrastructure.
- 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.
05Compliance hot spots
This use case usually raises confidentiality, personal data, retention, model training, data residency, prompt leakage, automated decision-making, cross-border transfers, data processing agreement, logging, security, transparency, vendor jurisdiction.
- Retention
- Hong Kong
- Model training
- European UnionSouth Korea
- Data residency
- No published jurisdiction page names this topic yet
- Prompt leakage
- No published jurisdiction page names this topic yet
- Automated decision-making
- European UnionUnited KingdomSouth Korea
- Cross-border transfers
- European UnionUnited KingdomChina (mainland)Hong KongTaiwan
- Data processing agreement
- Japan
- Logging
- European Union
- 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 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 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 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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