Knowledge asset assessment
We look at the format, quality, update cycle, and duplication of the target documents to set a scope that suits RAG.
Enterprise knowledge search (RAG)
We design an enterprise knowledge search environment that connects scattered work documents and rules and finds answers with sources you can check.
Before any answers, we build a knowledge base you can trust and search.
RAG is a way of having a language model find relevant sources in your organization’s documents and connect them to its answers. We don’t stop at collecting documents. Within your workflow, we also design who can see which information and how the sources of an answer are checked.
LLMs and RAG are use cases KPS proposes. We first validate, in a pilot (proof of concept, PoC) with limited data, whether they apply to your work and how the model should be set up.
The more scattered the materials, the longer it takes to find answers.
If this sounds familiar
When material is plentiful but slow to find
We prepare the rules, manuals, and reports scattered across departments and systems so people can search them by question.
When current and old documents are mixed
We manage each document’s version, creation date, and owning department as metadata and use them to set search priority.
When sources and access permissions matter
We review with you how to show referenced documents in answers and connect them to your existing permission structure.
We design it all, from document cleanup to access and review policies.
Collection & cleansing pipeline
We collect documents and databases, split them into units, and set up the metadata that search needs.
Search & answer experience
We design the search method for work questions, how sources are shown, and the follow-up question and feedback flows, together with the screens.
Permissions & review policy
We define what each user can view, how sensitive information is handled, where answers are limited, and where staff review is needed.
We validate with a small question set, then expand into operations.
We narrow the questions and materials
We start with frequent work questions whose results are easy to check, and with representative documents.
We validate search quality
We evaluate whether the right documents are found, whether the sources are sufficient, and when answers should be limited.
We connect it to work screens
We build the flow of questions, source checks, feedback, and staff review to fit how people actually work.
We set update and operating standards
We define document updates, quality checks, usage log reviews, and the improvement cycle as specific operating tasks.
Project deliverables
- Analysis of knowledge assets and user question types
- Document collection, cleansing, and update structure
- Evaluation criteria for search quality and answer sources
- Policies for permissions, limits, and human review
- PoC screens and a proposal for moving to operations
The exact scope and deliverables are confirmed in early discussions.
This is a proposed use case, not work we have delivered. The video and data analytics projects we have delivered are on the video AI & data analytics page.
View video AI & data analyticsCommon questions before a consultation
View all FAQsA few recurring questions and key documents are enough to start.
Tell us about your current environment and the work you want to improve, and we’ll help define where to start.