Customer support AI that answers, triages and drafts replies inside your helpdesk

It depends on your contact mix, so we measure it from your own tickets rather than quoting a typical figure. We classify a recent sample of tickets and chats by contact reason, then mark which reasons can be answered from approved content or a single system lookup. That share is the realistic ceiling for self-service. Shadow-mode testing then shows how much of it the AI handles correctly, and handover rules keep complaints, refunds and vulnerable customers with agents.

Yes, it works with any helpdesk that offers an API and webhooks, including Zendesk, Freshdesk, HubSpot and custom ticketing systems. Triage writes tags, priorities and assignments back to tickets, drafts appear as internal notes or in a sidebar app for agents, and conversation summaries are stored on the customer record. Your team keeps working in the helpdesk it knows, and reporting stays in one place.

AI triage reads each incoming email or message, identifies its intent, urgency, language and sentiment, links it to the customer record and assigns it to the right queue or workflow. Rules you control decide what happens next, such as escalating any cancellation request from a high-value account. Uncertain classifications go to a general queue instead of a guess. We test triage on historical tickets with known outcomes before it touches live traffic.

Agent assist helps your support staff inside the helpdesk, while a customer-facing bot replies to customers directly. With agent assist, the AI summarises the conversation, finds the relevant articles and order details and drafts a reply that the agent edits and sends. Because a person checks every message, it is the lower-risk place to start, and it often shows which request types are ready for full self-service later.

Refunds, credits, compensation and exceptions are defined as things the AI cannot offer or carry out on its own. Instructions and output checks block replies that commit to them, and any conversation where a customer asks for one is routed to an agent with the context attached. Drafts that mention money or policy terms are flagged for closer review, and automated quality checks scan closed conversations for any commitment that slipped through.

The AI shares account-specific information only after the customer is verified by a method you approve. In a logged-in app or portal, the existing session confirms identity. On WhatsApp, the sending number can be matched to the account, with a one-time code for sensitive details. On email or web chat, the customer confirms details such as order number and postcode. Until verification succeeds, the AI gives general answers only.

Your existing articles and macros become the starting point for the AI's knowledge, after a clean-up. We check them for outdated policies, duplicates and contradictions, split long articles into answerable sections and give each one an owner and a review date. Resolved tickets fill gaps where no article exists. After launch, questions the AI could not answer are reported weekly, so the help centre improves in line with real demand.

The core measures are the share of conversations resolved without an agent, handover rate and reasons, first response and resolution times, satisfaction on AI-handled conversations and reopen rate. For agent assist we also track how often drafts are sent unchanged, edited or discarded. We record a baseline from your helpdesk before launch and report against it weekly, so every change is judged on your own data.

Yes, sensitive values are masked before any text reaches a model, so the model works with a placeholder rather than the number itself. Card numbers, bank details and identity numbers such as CNIC or Iqama are detected by pattern checks, and when a payment is needed the AI sends a secure payment link instead of collecting card details in the conversation. Transcripts are stored in the cloud region you choose, with retention and role-based access you define, under model terms that exclude training on your data.

Yes, we recommend telling customers clearly when they are talking to an AI assistant and always offering a route to a person. Disclosure sets fair expectations, reduces frustration when the AI cannot help and matches what customers and messaging platforms increasingly expect. The greeting, tone and handover wording are agreed with your team, and requests to speak to a person are honoured promptly, with the conversation history passed across.

Agent assist on one channel is usually the first release and is often live within a few weeks of the proposal, with email triage and customer-facing answers on chat or WhatsApp following in stages. The pace depends on the state of your help content, the helpdesk and order systems involved and how many channels and languages are in scope. Each customer-facing stage switches on only after shadow-mode testing shows it is ready for real customers.

Pricing has two parts: a fixed build quote given after discovery, and running costs for hosting and model usage that scale with conversation volume. The build depends on channels, integrations, languages and how much knowledge clean-up is needed. We estimate monthly running costs at both your normal and peak volumes before you commit, and triage and tagging run on smaller models so the cost per conversation stays predictable.