Support inputs
Use tickets, email threads, chat transcripts, account context, approved knowledge articles, product manuals, policies, and customer attachments.
Customer support RAG
RAG-powered customer support agents with cited answers.
Ground suggested responses in approved product documentation, support policies, troubleshooting guides, and account context.
RAG retrieves the relevant knowledge and presents source citations beside the draft. OCR can capture text from attached PDFs, forms, or screenshots, validation checks ticket context, and a controlled support agent drafts or routes the next step for a specialist to review.
Approvedknowledge only, with owner and review date
Draftprepared, never sent unreviewed
Gapsunanswered questions logged for the KB
OCRscreenshots and PDF attachments read
Grounded support workflow
Give each draft an approved source and a clear review path.
BlueMouse.ai combines enterprise RAG with OCR for ticket attachments so support teams can see what informed each suggested response.
Attachment OCR
Extract configured text and fields from submitted PDFs, forms, receipts, or screenshots so attachments can inform triage and retrieval.
Ticket validation
Check product, issue category, account permissions, required details, and extraction confidence. Ask for missing context rather than guessing.
RAG support retrieval
Search permission-approved product documentation, troubleshooting steps, support policies, and internal playbooks for passages relevant to the case.
Cited support output
Prepare a ticket summary, relevant source excerpts, response draft, missing-information request, or escalation recommendation for the specialist.
Support control
Apply knowledge and account permissions, show citations, record edits, and require specialist approval for sending or escalating defined cases.
Knowledge freshness
Good answers come from a knowledge base that is kept honest.
A support assistant is only as good as the articles behind it. These controls keep the retrieval set approved, current and improving, and they are what separates a useful assistant from a confident one.
Control
What it does
Approved sources only
Retrieval is limited to articles with a named owner and a review date. Drafts, forum posts and old macros are excluded.
Product and plan filter
Answers are filtered by product, version, plan and region before ranking, so enterprise customers do not receive consumer steps.
Stale content
Articles past their review date are flagged in the draft and pushed down the ranking until an owner re-approves them.
Gap logging
Questions the knowledge base could not answer are recorded and grouped, giving the content team a prioritised backlog.
Tone and policy rules
Response rules per channel and customer tier are applied to the draft, with refunds and commitments always routed to a person.
Attachments
Screenshots, PDFs and forms are read by OCR so error messages and order references inform retrieval.
Suitable first use case
Start with cited drafts for one support category.
Choose a recurring issue with a maintained knowledge set and a clear escalation owner. The workflow validates the ticket, searches the approved articles and manuals, and prepares a draft with supporting passages. A specialist can inspect the sources, edit the response, request more information, or escalate the case.
This focused start makes retrieval quality, response rules, and human ownership measurable before adding more categories or tools. Learn how controlled AI agents handle routing and actions, or explore more industry document and knowledge workflows.
Common questions
What support leaders ask before a RAG assistant goes live.
Will the assistant answer customers directly?
Not by default. It prepares a draft with its sources shown, and a support agent edits, sends, or escalates. Sending without review is enabled only for narrow categories you choose, after the draft acceptance rate for that category has been measured on real tickets.
Which knowledge can it use?
Only approved sources: knowledge base articles with a named owner and review date, product manuals, support policies, and internal playbooks. Forum posts, drafts, and retired macros are excluded, and results are filtered by product, version, plan, and region before ranking.
What happens when the knowledge base has no answer?
The assistant says so rather than inventing one. It records the question as a gap, groups it with similar unanswered questions for the content team, and proposes escalation or a request for more information from the customer.
Does it work with our helpdesk?
Drafts and citations appear inside the agent's existing ticket view. We integrate through the helpdesk platform's API, so agents do not switch tools, and ticket fields such as product and plan drive the retrieval filters automatically.
How is customer data protected?
Account context is retrieved only within the permissions of the agent handling the ticket, attachments are processed inside the agreed deployment boundary, and personal data in tickets follows your retention policy. The security and governance page describes the controls stage by stage.
How do we know it is working?
Before go-live we replay historical tickets and measure citation accuracy, how often a draft is accepted with little or no editing, and how precisely escalations are proposed. After go-live the same measures run continuously alongside handle time and the size of the knowledge gap backlog.
Support knowledge
Ground customer support in knowledge your team approves.
Bring one support category, the source documents, escalation rules, and review path for a practical RAG assessment.