Chatling

What Chatling is for

Chatling is a no-code platform for customer-support agents and guided chatbots. It connects business knowledge with conversation handling, configured actions and a shared support workflow. A small business can use it for recurring product questions, while a support team can define which requests should reach a person.

The official introduction distinguishes two products. An AI agent interprets the request and chooses among configured actions. A chatbot follows steps and branches designed in a visual builder. Choose the format around the task: collecting a predictable set of details calls for different control from answering varied questions about a service.

Knowledge, actions and human handoff

Chatling can use websites, documents and other business content as knowledge sources. Agents receive instructions and can invoke actions to retrieve or update information through the systems you connect. Adding an action requires defining its intended role; the assistant does not automatically gain access to every business application.

The platform supports website and messaging-channel deployment, with conversations handled in a shared inbox. Its handoff guide explains a key setup requirement: the AI agent's handoff action is disabled by default. Enable it and specify the circumstances for escalation, including what information to gather first. When triggered, the conversation is marked for intervention and a summary helps the team pick it up.

Chatling also documents an answer-correction workflow. A corrected response is added to FAQ sources for processing. This is a way to improve the knowledge used for future questions, not a guarantee that an unsupported answer can never recur.

Example: support a small online course

A course provider could begin with its current enrollment, access and cancellation information. Remove obsolete pages and make clear which policies apply to each course. Configure the agent to answer from those materials and escalate account-specific billing disputes instead of inventing an exception.

Before deployment, try ordinary questions, a request using the wrong course name and a question that the supplied material cannot answer. Check whether the response acknowledges the gap. Enable handoff and confirm that the support team receives the details it needs without asking the customer to repeat the whole exchange.

After launch, review conversations for repeated misunderstandings. Correct the underlying policy source when it is unclear, and use the documented correction control for an inaccurate answer. Recheck affected examples after processing. This keeps maintenance tied to actual questions rather than assuming that an initial content import finishes the work.

Free access, credits and maintenance limits

Chatling lists free and paid plans on its pricing page. Compare seats, knowledge capacity, actions, workflows, channel requirements and synchronization intervals. Model choice affects credit consumption, so a credit allowance is not necessarily the same number of customer conversations.

The credit-exhaustion guide says users can still send messages when credits run out, but the interface displays an insufficient-credit error. Configure usage notifications and an operational response before relying on the agent for support. Human availability, source updates and answer review remain part of the service you provide to customers.