AI Knowledge Assistants & Retrieval-Augmented Generation
Ask a question in plain English and get the answer from your own documents — with the source attached.
Request a consultationThe knowledge your business runs on is scattered across PDFs, shared drives, wikis, old emails, and the heads of a few long-tenured people. When someone needs an answer, they search, they ask around, or they guess. A knowledge assistant changes that: it reads everything you point it at and answers questions in plain language, citing the exact document and passage it drew from.
The technique behind it is retrieval-augmented generation, or RAG. Rather than trusting a language model to remember facts — which is how models make things up — the system first retrieves the relevant passages from your material and then asks the model to answer using only those. The result is grounded, checkable, and updates the moment you add a new document.
We build these systems end to end: the ingestion pipeline that keeps them current, the retrieval layer that decides what is relevant, the guardrails that keep answers inside your material, and the permissions that make sure people only see what they are allowed to.
This is for you if
- New hires take months to become productive because the knowledge is undocumented or scattered.
- Your support or sales team answers the same questions repeatedly from memory.
- You have thousands of documents — contracts, manuals, policies, research — nobody can search effectively.
- You want the convenience of ChatGPT without your confidential documents leaving your control.
AI Knowledge Assistants (RAG): what's included
Internal knowledge assistant
A private assistant over your policies, procedures, product documentation, and past work. Staff ask questions in chat, Slack, or Teams and get cited answers instead of a search results page.
Customer-facing support assistant
A support assistant on your website or in your product that answers from your help content and hands off to a person with the full conversation when it reaches its limits.
Document ingestion pipelines
Automated intake from SharePoint, Google Drive, Confluence, email, databases, and scanned documents, with parsing, chunking, and re-indexing as sources change.
Search and retrieval tuning
Hybrid keyword and semantic search, reranking, and evaluation sets built from your real questions, so relevance is measured rather than assumed.
Permissions and privacy
Document-level access control that mirrors your existing permissions, so the assistant never shows someone a document they could not open themselves.
Self-hosted deployment
For regulated or confidential material, the entire system — embeddings, vector database, and language model — can run on your own infrastructure with no external API calls.
How an engagement works
Source inventory
We catalog where the knowledge lives, how it is formatted, who may see it, and how often it changes.
Question set
We collect fifty to a hundred real questions your team asks, with the correct answers. This becomes the test the system has to pass.
Build and evaluate
Ingestion, retrieval, and answering are built and scored against the question set. We tune until accuracy is where it needs to be, and show you the numbers.
Rollout and feedback
Users rate answers; low-rated answers are reviewed weekly and the gaps in the source material are fixed at the source.
What you walk away with
- An assistant your team can query in plain English, with every answer citing its source.
- A measured accuracy score on your own question set, before and after tuning.
- An ingestion pipeline that keeps the assistant current without manual re-uploads.
- Permission enforcement that matches your existing document access.
Frequently asked questions
How is this different from just using ChatGPT?
ChatGPT does not know your documents, and if you paste them in they leave your control. A RAG system answers only from your material, cites the passage it used, and can run entirely on infrastructure you own.
Does it make things up?
Far less than a bare model, because it is constrained to answer from retrieved passages and to say when nothing relevant was found. We measure this on your own question set and tune the guardrails until the rate of unsupported answers is acceptable for your use.
What document formats does it handle?
PDF, Word, PowerPoint, Excel, HTML, Markdown, email, and scanned images with OCR. Structured data from databases and spreadsheets can be included as well.
How much does the language model cost to run?
It depends on volume and whether you use a hosted API or a self-hosted model. For most internal assistants the monthly model cost is small compared with the staff time recovered, and we give you the projection before you commit.
Can we start with one department?
That is how we recommend starting. One team, one body of documents, one question set. Once that is working, adding sources and users is incremental.
Related services
Talk to us about AI Knowledge Assistants (RAG)
Start with a free 30-minute discovery call. We'll tell you honestly whether this is the right fit.
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