Solution: Knowledge assistant (RAG)
Nobody can find the right document when they need it
Ask a question in plain words and get a clear answer from your own documents, with links to the exact sources, respecting who is allowed to see what.
Sounds familiar?
Signs this is costing you time
Most companies have more knowledge than they can find. Procedures, manuals, project files, proposals, product specs and lessons learned sit in shared drives, wikis and email attachments. Search works only if you know the exact file name or keyword. So people ask the colleague who knows, and that colleague becomes a bottleneck.
The cost is invisible but large: time spent searching, work redone because nobody found the earlier version, and decisions made without information that already existed.
A knowledge assistant built with retrieval augmented generation searches your documents by meaning, reads the relevant passages and answers the question, always showing where the answer came from.
Unlike a general chatbot, it does not guess from its training. It answers from your material, and when your documents do not contain the answer, it says so.
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Staff spend a long time searching folders and wikis.
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A few experienced people answer the same questions all day.
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Work is redone because nobody found the previous version.
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New staff take months to learn where things are.
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Several versions of the same document exist, and nobody knows which is current.
Before and after
How it works today, and how it works after
How it works today
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Someone searches the shared drive by file name.
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They open several documents and skim them.
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If that fails, they ask a colleague.
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The colleague answers from memory, or searches too.
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The answer is never captured for next time.
How it works after
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Someone asks the assistant a question in plain words.
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It finds relevant passages across all connected sources.
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It answers briefly, with links to each source.
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It only uses documents the person is allowed to see.
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Unanswered questions show where documentation is missing.
What we build
What we build
Source connectors
Answer interface
Permissions
Quality testing
In practice
What it looks like in practice
An illustrative walk-through, not a client story.
A manufacturing company keeps product specifications, quality procedures and supplier documents across a shared drive and an old wiki. Engineers regularly asked the quality manager which procedure applied to a given part or where a test report was kept.
Now an engineer asks, "What is the inspection procedure for incoming aluminum castings?" and gets a short answer with the three relevant steps, linked to the current procedure and the related supplier requirement. When someone asks about a process that is not documented, the assistant says so and logs the question. The quality manager reviews those gaps monthly and writes the missing procedures.
Models and tools
AI models and MCP servers that usually fit
We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.
How we build it
The services behind this solution
Most solutions combine two or three of our services. These are the ones this one usually needs.
RAG knowledge assistants
Assistants that answer questions from your own documents and show where each answer came from.
MCP integration
Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
AI chatbots
Website and messaging chatbots that answer common questions well and hand everything else to a person.
Industries where it fits best
Honest limits
Limits and human checks
An assistant is only as good as the documents behind it. Outdated, contradictory or missing documents lead to poor answers. We help you identify authoritative sources, exclude old versions and track gaps. Answers always cite sources so people can check them.
AI can still misread a passage or combine information incorrectly. We test with real questions before launch, show sources for every answer and make it easy to flag a wrong answer. For high-stakes topics, such as safety, legal or medical matters, the answer should be treated as a pointer to the right document, not the final word.
Permissions must be respected carefully. We mirror your existing access rules and test that restricted documents never appear in answers for the wrong people.
Keep exploring
Services, industries and case studies
Related services
View all related services- RAG knowledge assistants Assistants that answer questions from your own documents and show where each answer came from.
- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- AI chatbots Website and messaging chatbots that answer common questions well and hand everything else to a person.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- SaaS migration Move a desktop, on-premise or single-customer system to a hosted product that serves many customers.
- Internal tools Custom software for your own team: trackers, approval flows and back-office systems that replace spreadsheets.
Solutions
View all solutions- 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.
- Automated quoting Draft accurate quotes and proposals from a request, your price rules and past work, for a person to approve.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
- AI support agent An assistant that answers routine questions from your own content, checks orders and hands anything else to a person.
- Compliance tracking Track filing deadlines, licenses and obligations in one place, with reminders and AI summaries of relevant rule changes.
- Contract review assistant Highlight unusual clauses, missing terms and deviations from your standard positions, so reviewers focus where it matters.
Industries
View all industries- 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.
- Manufacturing Quality inspection, production dashboards, quoting tools, knowledge assistants and legacy system modernization for manufacturers.
- 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.
Guides and articles
View all guides and articles- 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.
- AI chatbot vs AI agent What separates a chatbot that answers from an agent that acts, and when you need each.
AI models
View all ai models- 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.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Voyage 4 Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
- Open weights Models whose weights you can download and run on your own servers or a cloud of your choice.
- Claude Opus 5.5 Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
MCP servers
View all mcp servers- Knowledge and memory Servers that give AI long-term memory or access to knowledge bases, wikis and personal notes.
- Google Drive Google's own Drive MCP server lets AI search, read and create files in Google Drive, with your Google Workspace sign-in.
- 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.
- Notion Notion's official hosted MCP server lets AI search and read pages, create and update pages and databases, query data and add comments.
- Qdrant Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
- Databases Servers that let AI query and, where allowed, change data in SQL, NoSQL, analytics and vector databases.
Glossary terms
View all glossary terms- 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".
- 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.
- Model Context Protocol The Model Context Protocol, or MCP, is an open standard for connecting AI assistants to external tools and data in a consistent way.
FAQ
Questions about knowledge assistant (RAG)
Have a question that is not here? Ask us directly.
RAG, retrieval augmented generation, means the AI searches your documents first and answers from what it finds. It reduces made-up answers and lets you show sources. See what RAG is.
Yes. Typed PDFs work well; scanned documents are first converted with OCR, with quality depending on the scan.
No, when permissions are set up correctly. The assistant uses each person's existing access rights, and we test this before launch.
Wiki search finds pages by keywords. The assistant understands the question, finds passages across many sources and writes an answer with citations. See RAG knowledge assistants.