Assistants that search your knowledge base, cite their sources and say so when they do not know.
Discipline: AI Agents
Working term: Senior people you meet are the people doing the work
Formats: ongoing monthly work or a single audit
Who you talk to: the senior lead, on every weekly call
The service
Our approach to RAG Knowledge Assistants
We build the retrieval pipeline carefully: chunking and embeddings tuned to your content, hybrid search, re-ranking and answers that cite their sources. Accuracy is tested on a fixed set of real questions, so you can prove it works before customers or staff rely on it.
Before work starts, we agree on the number that matters most to you. Each sprint is planned against it. You get a short recorded update every week and a dashboard you can open any time, and the senior lead joins every call.
A senior lead runs your account from day one
A recorded walkthrough each week and a shared reporting dashboard
We work inside the tools and CMS your team already uses
Target ranges written into the agreement before we begin
Our Process
Four stages of RAG Knowledge Assistants
The same order every time: find the constraint, plan, deliver, then review. Here is what each stage involves.
01
Find the constraint
We record a baseline for RAG Knowledge Assistants, look for what is holding results back, and write down the numbers you will judge us by.
02
Put the work in order
A senior lead drafts a 90-day plan. Fast fixes come first; the slower, harder-to-copy work is scoped to follow them.
03
Deliver every week
Work goes live in weekly batches. Tracking and reporting are kept current against the targets you approved at the start.
04
Review and adjust
Once a month we compare results to the plan and change course where the data says to. You can see where each dollar went.
Retrieval-augmented generation, or RAG, solves two problems at once. Public models do not know your business, and they guess when unsure. A RAG assistant first searches your own documents and data, then answers from what it found and shows the source. Staff can click through to check the policy, manual or contract behind the answer.
That traceability is what makes the assistant trustworthy on your own subject matter.
Retrieval over your own documents and data
Answers based on what was retrieved
Links to the source for every answer
Up-to-date, business-specific responses
02
How progress tends to build
Commercial terms usually move later than technical fixes
Surface buried knowledge
Make scattered information easy to find
Most organizations keep knowledge in many places: shared drives, SharePoint, Confluence, help desks and email threads. We ingest and index those sources so people can ask a plain-language question and get the right answer in seconds. Bilingual organizations can query in English or French.
That everyday time saving is where these assistants prove their value fastest.
Ingestion and indexing across your sources
Plain-language questions and answers
Quick answers over scattered content
English and French queries supported
03
Content velocity
Briefed, drafted, edited, shipped
Secure & permission-aware
Show each person only what they may see
An internal assistant touches sensitive material, so access control is essential. Retrieval respects your existing permissions, meaning people only get answers from documents they could already open. Data is handled securely, can be hosted in Canada when required and is protected by guardrails around personal information covered by PIPEDA or provincial privacy law.
That makes the assistant safe to run on real internal data.
Retrieval that follows existing permissions
Secure data handling and Canadian hosting options
Guardrails for personal and sensitive data
Safe use on internal knowledge
Our working terms
What a RAG Knowledge Assistants engagement includes
These describe how we staff, report on and scope the work. They hold from the first week, before any result exists to point at.
A named senior specialist leads each account
Progress shown in live Looker Studio dashboards
Measured on leads and revenue
Senior
People you meet are the people doing the work
Written
Scope and price agreed before work starts
Weekly
Progress updates in plain language
CAD
Scoped and invoiced in Canadian dollars
FAQs
Before you sign: common questions
Straight answers on scope, timing and how we report.
What is a RAG knowledge assistant?
An AI assistant that searches your own documents and data before answering, then cites where the answer came from. It makes scattered knowledge searchable in plain language.
How does it avoid making things up?
Each answer is built from content retrieved from your sources, with a link to the source, so people can verify it. When nothing relevant is found, it says so.
Will it respect our access permissions?
Yes. It only answers from documents the person asking is allowed to see, and sensitive data is protected by guardrails.
What can we use it for?
Helping staff find policies and procedures, customer self-service built on your help content, or expert assistants over technical manuals. Anywhere people waste time searching.
Tell us about the business and a senior specialist will get back to you within one business day. If starting with AI Agents looks like the wrong first step, we will say so and suggest what to fix first.
