A support assistant should reduce the time spent finding context, not create a faster way to send confident mistakes. In a sound ticket flow, AI classifies the request, retrieves current approved guidance, drafts a reply with source references, and recommends a route. A person or an explicit policy decides whether that reply is sent.
Start with the evidence
- Ticket text, product area, account tier, language, and relevant conversation history.
- Approved help articles, release notes, support policies, and known-issue records, each with an owner and review date.
- Permission rules that determine which account data a support worker may see.
The retrieval layer should use the same access boundaries as the support team. Ask the model to distinguish a sourced answer from an inference, and show the article or policy behind each factual claim. If no current source supports an answer, the system should ask a clarifying question or route the ticket instead of filling the gap with plausible wording.
- New ticket
- Find approved evidence
- Draft and classify
- Review or resolve
- Escalate when unsure
AI can prepare a grounded response; policy and people retain control of sensitive decisions.
A hypothetical example
Imagine a twelve-person software company receiving repeated questions about invoice dates. Its assistant could identify billing tickets, retrieve the current billing policy, draft a reply that links to the relevant paragraph, and send unusual account-specific cases to billing staff.
Limits and useful measures
A retrieved page can be outdated, a ticket can omit key details, and an account may have an exception that is not in the knowledge base. Do not let a language model promise refunds, change access, disclose another user's information, or close a dispute on its own. Keep escalation easy and retain the source, proposed answer, decision, and final disposition for review.
Measure grounded-answer accuracy on a reviewed sample, correct routing, missed escalations, reopen rate, time to first useful response, and staff corrections. Track these by topic and language; a single average can hide a weak workflow. Test new policies and product changes before broadening automation, and keep a way to turn off a failing answer path.
Integrations without a fixed catalog
Slack, Telegram, WhatsApp, email, Jira, CRM, ERP and your own software: we build custom integrations with no fixed list or predefined number of connections. The scope depends on available APIs, permissions and each provider’s conditions.
Collect support requests from Slack, Telegram, WhatsApp and email; create or update Jira tickets with context, draft replies and escalation to your team.
For a team evaluating AI solutions, begin with one ticket category and agree on access, review, and escalation rules. If tickets contain sensitive account data, private computing may fit when retrieval, logs, and backups stay in the agreed boundary. Discuss a scoped project: custom AI solutions start at €10,000, or scoped rental starts at €1,000 per month. Taxes may apply; the quote specifies scope, capacity, setup, hardware, and third-party costs.
FAQ
How much does an AI project cost?
The starting prices are €10,000 for a project and €1,000/month for monthly capacity rental. Your quote depends on scope, capacity, setup, hardware, external provider costs and applicable taxes.
What kind of company or team is this for?
It suits teams with a specific product or operational task, access to representative examples and a person who can approve the result. We first check whether AI is useful for that task; a conventional rule or software change may be simpler.
Will our data be sent to an external AI provider?
That depends on the agreed setup. We document data paths and access before choosing an external model or a private local setup.
How do you decide whether the AI is good enough?
We agree on representative inputs, acceptable outputs, failure limits and the human approval points before the pilot. Results depend on your data and workflow, so we measure against that agreed sample instead of guaranteeing a general accuracy figure.