AI support answer drafting from your own knowledge base

What shipped

Customer service teams now have a practical option that sits between a full chatbot and the old habit of searching folders by hand. An AI assistant can search approved help articles, policy notes, product documents and past resolved tickets, then draft a proposed reply for a support agent. The important point is that the draft is not sent automatically. It is decision support, reviewed by a person before operational use.

This matters because many Irish SMEs already have the raw material for better support, but it is scattered. One answer may live in a PDF. Another may be in an email from operations. A newer version may be in a help article, while the most useful wording may be buried in an old ticket. Agents waste time finding the right version, and customers get slightly different answers depending on who is on shift.

The better workflow is grounded retrieval plus structured drafting. The assistant searches only the company material you approve, pulls out the most relevant snippets, and produces a reply with citations or source notes for the agent. The named human review step should be “Agent review and approval”. The agent checks the cited sources, edits the wording, and sends the reply through the normal support tool.

For more general examples of where this pattern fits, see our related AI workflow notes on Artellis insights. If you want this designed around your current tools rather than bought as a platform project, it sits well within Artellis AI workflow design and implementation support.

Who should care

This is most useful for SMEs with 10 to 250 staff where customer service is handled by a small team, sometimes with help from sales, operations or technical staff. It fits companies with recurring questions, policy based answers, product configuration details, booking rules, warranty queries, delivery information, account questions, or service terms that change from time to time.

A good sign is that your support agents often say, “I know we answered this before, but I need to find it.” Another sign is that new team members take months to learn where the useful answers live. If a manager is regularly asked to confirm routine replies, the workflow is probably carrying too much memory in people’s heads.

This is not a replacement for a support agent. It is not the same as turning on a public chatbot and hoping it behaves. It is also not a reason to pour every old file into a model. The value comes from choosing trusted material, setting boundaries, and making the review step clear.

A support manager should care because it can reduce time spent looking up answers. An owner should care because it makes service quality less dependent on who happens to be available. A compliance or operations lead should care because cited sources make it easier to see why a reply was suggested.

The useful measures are simple. Track average handle time for the selected query types. Count how many AI drafted replies are accepted after agent review. Record repeated questions where no approved answer exists. That last measure is often the most valuable, because it shows where the knowledge base is weak.

A worked example

Take an Irish equipment supplier with 35 staff. It sells to trade customers, handles warranty questions, and receives product fit queries by email and web form. The support team uses a shared inbox, a small ticketing tool and a folder of PDFs from suppliers. They also have years of resolved tickets with good answers, but nobody has time to search them properly.

The current process is familiar. A customer asks whether a part is suitable for a particular model and whether the warranty still applies if it is fitted by a third party. The agent searches the shared drive, checks an old ticket, asks a colleague, opens a product sheet, then writes a reply. The customer gets an answer, but the process takes 15 minutes and the wording may differ from last week’s answer.

In the redesigned workflow, the incoming ticket is labelled as a product and warranty query. The assistant searches an approved set of documents: the top help articles, the latest warranty policy, selected supplier notes, and a small set of resolved tickets marked as reliable. It returns three things to the agent.

First, it gives a short answer summary in plain English. Second, it drafts a customer reply in the company’s usual tone. Third, it shows cited snippets from the warranty policy and product note that support the answer. If the sources are weak or conflicting, it says so and asks for human judgement rather than pretending to be certain.

The agent then performs the “Agent review and approval” step. They check the snippets, compare the draft with the customer’s question, edit the answer if needed, and send it. If the draft is wrong, missing context, or based on an outdated source, the agent marks the problem. That mark creates a small knowledge base task: update the article, remove an old note, or add a missing answer.

After two weeks, the manager reviews the numbers. For the selected query types, average handle time has fallen from 15 minutes to 9 minutes. Agents accepted 62 per cent of drafts with light edits, rejected 20 per cent, and needed a manager for the rest. The team also found 18 repeated questions with no approved answer. That is not a failure. It is a useful map of where the company’s support knowledge needs cleaning.

Where I'd start

Start small and keep it boring. Pick one category of support question where the risk is low, the volume is high, and the answers depend on written material. Delivery status wording, warranty rules, booking changes, returns, account setup, and product compatibility are common starting points.

Load the top 20 help documents and 50 resolved tickets. Do not load everything. Ask the support manager and two experienced agents to choose material they trust. Remove documents that are out of date, vague, duplicated, or not approved for customer use.

Then run a side by side test before anything is sent to customers. Give agents real closed tickets and ask them to rate the AI draft against the answer that was actually sent. Use a simple scorecard: correct answer, useful sources, clear wording, missing information, and whether they would send it after edits.

Only after that should you test it on live incoming tickets, still with no automatic sending. The assistant should draft, cite, and flag uncertainty. The agent should review, edit and approve. If the workflow cannot show its sources, it should not be used for customer replies.

The first version does not need deep integration. A lightweight internal tool, a controlled document store, and a clear review process are usually enough to prove value. The bigger work is deciding what counts as approved knowledge and how missing answers get fixed.

For an Irish SME, the next step is not to buy a large customer service platform because AI is on the feature list. The next step is to redesign the support answer workflow around your own trusted material, your agents’ judgement, and a feedback loop that improves the knowledge base every week.