AI customer service automation
AI customer service is a setup that reads incoming support requests on every channel, classifies and prioritises them, drafts replies with sources for your agents and closes simple requests on its own. Corrplus builds it on top of the helpdesk you already use and hands the conversation to a person wherever the decision should stay human.
Book a free callTiming
The first version of triage and agent assist usually ships within 4–8 weeks
Pricing
Fixed-scope project quoted after the discovery call; model usage billed to your own account; optional monthly operation.
Tools we use
Zendesk, Freshdesk, HubSpot Service Hub, WhatsApp Business Platform, OpenAI, Anthropic Claude and Google Gemini models, n8n
Who it is for
- Support teams that receive requests by email, WhatsApp, web form and phone and track each channel separately
- Companies on Zendesk, Freshdesk or HubSpot Service Hub that still tag and route tickets by hand
- E-commerce, subscription and travel businesses whose response times stretch during campaigns and peak season
- Contact centres where new agents need fast access to product and procedure knowledge
When it is not a fit
- Businesses with low request volume on a single channel. A website or WhatsApp chatbot is enough.
- Anyone looking to remove human contact entirely. This setup takes routine work off agents; it does not replace them.
What you get
- Triage and routingEach incoming request gets a topic, urgency and tone, and is assigned to the right team and priority.
- Agent assistA draft reply in the ticket view, grounded in your knowledge base with its source shown; the agent edits and sends.
- Self-service flowsFlows that read your systems and close clear-cut requests such as order status, invoice copies or appointment changes.
- Handover rulesComplaints, legal language, high amounts or a customer asking for a person move the conversation to an agent with a summary.
- Support knowledge baseContent compiled from your procedures and saved replies, kept current, with gaps reported.
- ReportingTopic breakdown, how many requests were closed automatically or handed over, and recurring issues.
How we work
Discover
We review recent tickets and messages channel by channel and map topics and repeat requests.
Design
We write rules for which requests close automatically, which reach an agent with a draft and which go straight to a person, and review data handling under GDPR and Türkiye's data protection law (KVKK).
Build
Triage and agent assist go live first. As agents approve drafts, we widen the set of topics that close automatically.
Run
We track misclassifications and edited drafts and keep rules and the knowledge base up to date.
Where does your support team’s time go?
In most support teams, much of an agent’s time goes into work before the reply: reading the request, routing it, finding the customer’s order on another screen, searching for how a similar question was answered last time. When requests arrive by email, WhatsApp, web form and phone, that work multiplies.
AI takes over this groundwork. It reads the request, sets its topic and urgency, pulls the relevant customer data and offers the agent a draft with its source. Clear-cut, low-risk requests it closes on its own from start to finish.
Which requests go to AI, and which to people?
Not every request gets the same treatment. Writing this split down is the core of the setup.
| Request type | Example | Who resolves it |
|---|---|---|
| Information question | Return window, opening hours | AI, from the knowledge base |
| Status from a system | Where is my order, invoice copy | AI, after verifying the customer |
| Standard change | Address change, rescheduling | AI prepares; closes automatically or with agent approval, by rule |
| Complaint or exception | Damaged item, double charge | Agent, with an AI summary and draft |
| Legal or sensitive matter | Consumer dispute, personal data request | Straight to the responsible person |
How is it different from a chatbot or a RAG assistant?
A chatbot is the front end that talks to customers on one or two channels, usually owned by marketing or e-commerce. AI customer service is the structure behind the support operation: it triages requests from every channel, connects to your helpdesk and works inside the agent’s screen. A chatbot can be one of its channels.
A RAG knowledge assistant is a general tool for employees to query internal documents. Here the same technique is applied to the support knowledge base, and in most cases the reply passes through an agent before it reaches the customer.
How do we start?
In the first call we map the topic breakdown from an anonymised sample of recent tickets. That breakdown shows what can close automatically and where agent assist will help most. If sales conversations also land in your support channels, we plan alongside the AI sales assistant. For a fuller roadmap, start with the AI discovery sprint.
How would this work in your business?
Tell us where you are in a free first call; we will say plainly where it makes sense to start.
Related services and use cases
Further reading
AI assistants on WhatsApp BusinessRunning an AI assistant on the WhatsApp Business Platform: templates, the 24-hour window, opt-in, human hand-off and data rules.
Chatbot, agent assist or full automation?Chatbot, agent assist or an action-taking agent: request types, hand-off rules, AI Act disclosure and quality metrics.
From WordPress plugin to SaaS productThe packaging, licensing, billing, support and measurement decisions behind turning a WordPress plugin into a subscription product.Our own products
We use the tools and checklists from client projects on our own products too.
All products →Key terms
- ChatbotA chatbot is software that converses with users in text or voice on channels such as a website, WhatsApp or in-app messaging, answering questions and handling simple tasks.
- RAG (Retrieval-Augmented Generation)RAG (Retrieval-Augmented Generation) is an AI architecture that lets a language model retrieve relevant passages from a chosen document collection, such as a company's own documents, and use them as context before it writes an answer.
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on very large amounts of text that answers questions, summarises, translates and writes by predicting what text should come next.
- Workflow automationWorkflow automation is an approach in which software carries out the repetitive steps of a business process, such as data transfer, approvals, notifications and reporting, according to defined rules.
- AI governanceAI governance is the set of policies, roles and processes that determine how an organisation selects, uses, oversees and manages the risks of its AI systems.
Frequently asked questions
Will AI replace our agents?
Not in the setup we build. The aim is to take manual tagging and routine requests such as order status off your agents. Complaints, exceptions and anything that needs judgement stay with people, who get more time for them.
Do we need to change our helpdesk?
No. We work through the APIs of tools such as Zendesk, Freshdesk and HubSpot, so tags, drafts and notes appear in your own system. If you run an in-house tool, we can connect to that too.
When is a conversation handed to a person?
We write the rules together. Typical triggers: the customer asks for an agent, a complaint or legal language appears, a high-value refund is involved, or the AI is not confident in its answer. The agent takes over with a summary and the full history.
Does it work for phone calls?
Call recordings can be transcribed for triage, summaries and quality review. A voice bot that talks to callers live is a separate scope; we assess whether it fits your needs during discovery.
How is customer data protected?
Only the fields needed go to the model, and identity and card data are masked. Retention of conversation logs and the processing location are set during design under GDPR and KVKK, and privacy notices are updated with your legal counsel.
How do we measure success?
We measure the baseline before we start: first response time, resolution time, misrouted tickets. We repeat the same measurements after launch. We do not promise a target number up front; you see the comparison on your own data.
AI customer service automation: let’s take the first step together
Tell us where you are in one call; we will share a scope and roadmap in writing.
How we start
- A free first callWe pin down the problem and the goal together.
- A 2-week discovery sprintWe look at the data, the process and the tools, and decide where to start.
- A written proposalScope, architecture and timeline, in writing.