Knowledge assistants · RAG
RAG assistants for your company’s knowledge.
I build RAG assistants that help your team consult approved manuals, policies and procedures, with references they can check. We begin with a specific task and a set of sources your business can maintain.
When it makes sense
When finding an answer means searching too many documents.
A knowledge assistant can help operations, support or administrative teams that consult the same information repeatedly. RAG stands for retrieval-augmented generation: the application searches connected sources for relevant material and uses that context to prepare an answer.
The conversation is one part of the solution. You also need to establish which version applies, who may access it and how to check that the answer is supported. My work covers those decisions and their implementation within an agreed scope.
If you are comparing approaches, the guide to chatbots, RAG and agents explains the needs each can address.
Situations we can assess
- A service team needs to find return conditions and show the applicable policy.
- Operations staff consult procedures, requirements and exceptions before carrying out a task.
- New team members need current instructions and a clear handoff when information is missing.
- A technical team searches approved manuals and needs to open the passage supporting an answer.
These are possible applications, subject to a review of data, permissions and feasibility. If the information is outdated or exists only in someone’s memory, preparing it comes first.
Scope and deliverables
A pilot you can evaluate and maintain.
We agree on users, question types, initial sources and acceptance criteria. A focused pilot helps reveal whether a difficulty comes from the content, retrieval or interpretation before the system expands.
Connectors, deployment environment, languages and ongoing support are defined for each engagement. You do not need to connect every source at the outset.
What the project can include
- An inventory of documents, owners, versions and permissions by user role.
- Content preparation and a search mechanism for the agreed sources.
- A query interface with references to the material used.
- Behavior for clarification, conflicting sources and questions without supporting information.
- An evaluation question set and an error record for review.
- Documentation for source updates, operation and team handoff.
How we judge whether it works
We check whether the right document was retrieved, whether the answer reflects it and whether the reference allows verification. We also assess review effort, usefulness to the user and usage cost with examples that represent your operation.
How we work
From source material to an informed decision.
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1. Define the task
Choose the users and recurring questions. Establish the expected result and who can validate it.
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2. Prepare the knowledge
Check that sources are current, readable and accessible. Resolve gaps and versions that would affect the answers.
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3. Build and evaluate
Test retrieval, answers and citations. Include ambiguity, restricted content and questions the sources cannot answer.
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4. Plan ongoing use
Review results and remaining work. Assign ownership for updates and define the evidence needed to expand.
The guide to preparing company knowledge for AI helps you gather the initial information.
Frequently asked questions
Sources, permissions and the assistant’s limits.
Does RAG prevent incorrect answers?
Retrieval can supply relevant context, but it does not eliminate errors. I evaluate answers and their references and define the assistant’s behavior when evidence is missing. Sensitive decisions require review by the person responsible.
Do I need to organize every document before starting?
You do not have to resolve the entire company archive. We can start with a limited set of current, approved documents. The initial review helps establish how much additional preparation that task needs.
Will every user see the same information?
That depends on the agreed permissions. The design needs to restrict retrieval and responses according to each role’s access. Those restrictions are tested with specific cases; telling a model to hide information is insufficient.
What happens when a policy changes?
The project defines who approves the source, how content is updated and how an older version is retired. We also need to verify that queries reflect the new information and review the affected evaluation questions.
Can the assistant make changes in my systems?
Consulting knowledge and taking action require different scopes. If you need to prepare requests or coordinate tasks across systems, we can assess an agent workflow with specific permissions and approval points.
Start with the questions your team keeps asking.
Tell me who needs answers, what they need to know and where the information lives today. A description is enough for the first conversation; confidential documents can follow after we agree on information handling.
If you need to choose a priority first, explore AI strategy and roadmap planning or see all services.