# Citation-grounded research and knowledge assistants URL: https://www.getdynamiq.ai/use-cases/research-knowledge > A research assistant that answers from your documents and systems, respects who can see what, and cites its source for every claim it makes. Answer questions across your documents and systems, grounded in retrieval that respects who can see what, with citations back to the source. [Start free](https://app.getdynamiq.ai/signup) · [Talk to the team](https://www.getdynamiq.ai/book-a-demo) _Illustration of a research assistant answering a policy question with numbered citations to two internal documents, each marked with the permissions that apply._ In short A research and knowledge assistant answers questions from your own documents and systems, retrieving only what the asking person is allowed to see and citing the source for every claim. Knowledge graphs let it follow relationships across documents, not just match keywords, for questions that need several steps to answer. Built for analysts, researchers and internal teams who need answers they can check, not just a summary. ## The problem Analysts and internal teams lose hours a week hunting across five systems and a colleague's inbox for something the organization already wrote down. A model answering from memory alone will guess, and guessing is worse than not answering in a regulated business. Teams need an assistant that answers only from retrieved sources, cites what it used, follows relationships between documents when a question needs more than one hop, and never shows a person a document their own permissions would not let them open. ## What it moves Time to answer How long it takes a person to get a sourced answer instead of searching several systems themselves. Retrieval precision Whether the passages the assistant retrieves are the ones that actually answer the question. Answer coverage The share of real questions the knowledge base can answer at all. Citation rate How often an answer includes a source a person can check. Customer story · A B2B accounting platform ## [A B2B accounting platform shipped document search in one week with one of our engineers.](https://www.getdynamiq.ai/case-studies/smart-document-search-for-a-b2b-accounting-platform-built-in-one-week-with-dynamiq) Read the story ## How the agent works Every step is traced. The steps marked for review wait for a person. 1. Step 1 Documents sync in A knowledge base syncs from the systems of record you point it at, kept current on a schedule rather than copied once and forgotten. 2. Step 2 Question arrives An analyst or an internal user asks a question in plain language, through a chat surface or your own application. 3. Step 3 Grounded retrieval The agent retrieves the passages and, where a knowledge graph is enabled, the related entities that answer the question, filtered to what that user can see. 4. Step 4 Cited answer The agent answers from what it retrieved, with an inline citation back to the source document for every claim. 5. Step 5 Analyst checks the source For decisions that matter, the analyst opens the cited source before acting on the answer, and can flag a wrong or thin answer back into the review queue. Human review ## Controls Deploy on Dynamiq Cloud, or self-host in your own AWS, Azure, GCP, IBM Cloud, OpenShift or Kubernetes environment so source documents never leave infrastructure you control. Our engineers scope retrieval permissions and the knowledge graph schema with your team before rollout. Permission-aware retrieval Retrieval is scoped to what the asking person's own account can see, either through a shared connection limited to public content or a per-user authorization. Per-team isolation Each team's knowledge base, connections and workflow live in their own private project, not one shared pool. Citations on every answer Every answer links back to the retrieved passage, so a person can verify a claim instead of trusting it. Full audit trail Every retrieval and every answer is recorded in a trace, so what the assistant looked up to produce an answer is always answerable. ## Systems it connects to - Google Drive, SharePoint, OneDrive, Box and Dropbox - Notion and Confluence - Website and public filings crawls - Neo4j for knowledge graphs - Slack and Microsoft Teams for answers in the flow of work - Internal wikis and policy repositories - Databases (PostgreSQL, MySQL), over SQL ## Built with - [Knowledge · A knowledge base converts your documents into searchable context: ingestion, chunking, embedding and storage, managed for you or pointed at your own vector store.](https://www.getdynamiq.ai/product/knowledge-rag) - [AI Coworker · Hand it a task in plain language. It plans the work, browses, runs code in its own cloud sandbox and uses the apps you connect.](https://www.getdynamiq.ai/product/chat) - [Agent Builder · Design an agent on a visual canvas or in the open-source Python SDK. Both compile to the same engine, so what you build ships either way.](https://www.getdynamiq.ai/product/agents) ## Industries - [Financial services · Back-office automation, customer service by phone and chat, and KYC and AML work, on one governed platform your compliance team can audit.](https://www.getdynamiq.ai/industries/financial-services) - [Insurance · Answer policyholders by phone and chat, prepare claims and underwriting files, and keep an adjuster or underwriter approving every decision.](https://www.getdynamiq.ai/industries/insurance) - [Government and public sector · Citizen services, benefits and permit review, and program reporting, with a person in the loop and your agency in control of the infrastructure.](https://www.getdynamiq.ai/industries/public-sector) - [Telecommunications · Answer subscriber calls and chats in their own language, reconcile billing, and give support and network teams answers with a citation attached.](https://www.getdynamiq.ai/industries/telecommunications) - [Healthcare · Patient intake, referral and document review, and clinical knowledge assistants, with PII and prompt injection screened before a model reads anything.](https://www.getdynamiq.ai/industries/healthcare) ## Questions and answers ### Does the assistant answer from the model's general knowledge? No. It answers only from what it retrieves and cites the source, and says so when the documents do not cover a question. ### How does it respect document permissions? Retrieval can run under a shared connection scoped to content everyone may see, or under each user's own authorization when access needs to match their individual permissions. ### What is a knowledge graph used for here? It stores documents as entities and relationships, not just chunks, so the assistant can follow a connection across documents for questions that need more than one retrieval step. ### Does content stay current? Yes. Connected sources sync in the background rather than being ingested once, so the assistant answers from current documents. ### Can different teams have different assistants? Yes. Each team's documents, connections and assistant can live in their own project, isolated from every other team's. ## See this agent on your data. Talk to our team. We will walk through how it works in your environment, with your systems. [Start free](https://app.getdynamiq.ai/signup) · [Talk to the team](https://www.getdynamiq.ai/book-a-demo)