Answers from your own documents, with the sources to prove it

A good fit when

• People repeatedly ask questions answered somewhere in your documents.

• Finding the right version of a policy or manual takes too long.

• Answers must be traceable to a source.

Consider something else when

• The needed knowledge is not written down anywhere yet. Document it first.

• Your data is mainly numbers in databases. A reporting tool or SQL agent fits better.

• AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
• Customer portals A secure place where your customers upload documents, check status, pay and message your team.
• Document automation Read invoices, forms, contracts and IDs, pull out the right fields and route them for review.
• AI chatbots Website and messaging chatbots that answer common questions well and hand everything else to a person.
• AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
• AI workflows Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.

• Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
• Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
• AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
• Contract review assistant Highlight unusual clauses, missing terms and deviations from your standard positions, so reviewers focus where it matters.
• Automated quoting Draft accurate quotes and proposals from a request, your price rules and past work, for a person to approve.
• Compliance tracking Track filing deadlines, licenses and obligations in one place, with reminders and AI summaries of relevant rule changes.

• Legal Contract review assistants, knowledge search, intake and document automation for law firms and in-house legal teams.
• Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
• Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
• Insurance Claims intake, document and photo processing, policy knowledge assistants and customer portals for brokers and insurers.
• Education and elearning Course platforms, student portals, content workflows and AI tutoring assistants for schools and training companies.

• What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
• RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.

• Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
• Claude Sonnet 5 Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
• Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
• text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
• text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
• Cohere Command A+ Cohere's enterprise flagship for multimodal, multilingual agent tasks, with 48 languages, tool calling, JSON output and open weights under Apache 2.0.

• Knowledge and memory Servers that give AI long-term memory or access to knowledge bases, wikis and personal notes.
• Files and storage Servers that connect AI to cloud drives and object storage, so it can find, read and organize documents.
• AWS S3 Amazon S3 is handled through the managed AWS MCP Server, which lets AI run scripted AWS actions, create presigned links and search AWS docs.
• Box Box's official MCP server lets AI search and read files, ask questions across documents with Box AI, extract data and upload files.
• Confluence Atlassian's official Rovo MCP server lets AI search and read Confluence spaces and pages, create and edit content and add comments and labels.
• Dropbox Dropbox's own MCP server lets AI list, search and read files, convert them to markdown and create, move, share and delete files. In beta.

• RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
• Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
• Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
• Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
• Chatbot A chatbot is software that holds a conversation with people through text or voice, answering questions or completing simple tasks.
• Hallucination A hallucination is when an AI model states something false or invented as if it were true, such as a made-up fact, figure or source.