Drafted, cited answers inside your helpdesk

It is a retrieval-augmented generation system: it looks up the relevant passages first, then writes from those passages only. The weak point in most RAG systems is retrieval, so that is where the build spends its time.

Documents are split along their own headings, so each chunk answers something complete, and each carries its product, audience, version and last-updated date. Retrieval uses hybrid search. Keyword matching catches exact terms such as error codes and plan names, embeddings catch questions phrased differently from the docs, and a reranker orders the top candidates.

Past tickets need different handling. They show how your team phrases things and which questions arrive together, but they also hold one-off exceptions and promises nobody should repeat. So tickets guide tone and point to the right article, while policy answers must cite a help article or policy document.

Each draft cites every claim, and a second check confirms that each cited passage supports its sentence. If nothing supports an answer, the assistant says it could not find one. For the longer version, read how to build a RAG knowledge assistant, or see how we scope RAG knowledge systems.

The build is a fixed price between $20,000 and $60,000, and it takes 6 to 12 weeks. An agent-assist sidebar in one helpdesk, drawing on the help center and one internal wiki, sits in the lower half and the shorter end of that range. Customer-facing answers, several products or languages, permission-aware internal content and ticket history push it toward the top.

It starts above a single-source RAG build ($15,000 to $35,000, 5 to 7 weeks) because it always reads at least 2 sources, redacts past tickets and adds a sidebar app inside the helpdesk. Per-user permissions across several sources follow that service's multi-source timeline of 8 to 12 weeks.

Running costs are billed at cost on your accounts:

For comparison, Intercom prices its Fin AI agent at $0.99 per outcome, with a monthly minimum when used alongside another helpdesk (Intercom pricing (opens in a new tab), checked October 2026). At low volume that is cheaper than a build. A custom assistant makes sense when volume is high, when answers depend on sources outside the helpdesk, or when you need control over citations and access. AI ops monitoring covers the ongoing evaluation work.

If your help center is thin or out of date, the assistant will cite wrong articles with confidence. Fix the docs first. A useful test: take 50 recent tickets and check whether a current article answers each one. If fewer than half are covered, the first project is documentation, and content operations can speed that up.

If your tickets are mostly account actions (refund this order, change my plan, reset my login), retrieval helps little. Those need an agent with scoped tools and permissions, built as a custom AI agent, or plain automations behind a form.

If you handle fewer than about 500 tickets a month and everything lives in one helpdesk, use the AI features your helpdesk already sells and revisit in a year. A build at this size rarely pays back against per-outcome pricing.

And if a wrong answer could cause real harm, such as medical guidance or regulated financial advice, keep the assistant internal, with a qualified person sending every reply. The assist mode is still worth having there: staff find the right policy faster, and every answer they send carries its source.