An AI customer support assistant that answers from your own information

Start with routine informational questions: what a service includes, where to find a policy or which approved product details answer a customer’s request. Agree the source for each answer and who keeps that information current. A useful assistant should recognize when a question falls outside those sources instead of inventing a refund rule, delivery commitment or product claim.

A support conversation can also prepare a structured handoff. Collect only the details staff need to investigate the request, explain what happens next and pass the context to the agreed destination. Requests involving complaints, account changes or uncertain information should follow your escalation rules. Staff retain authority over decisions the assistant is not permitted to make.

Channels such as Messenger, web chat, email or Viber are examples to consider during scoping. Their APIs, account settings, message delivery and identity controls differ. We confirm the available connection and handoff behavior before agreeing a supported channel; listing a channel does not mean a ready-made connector or a platform partnership.

Choose a starting channel based on where your team receives the relevant questions. Define whether the assistant can send a reply, prepare a draft or only organize an incoming request. Existing staff should be able to tell which conversations need attention and what information the assistant has already supplied.

We scope English, Filipino and Taglish responses using examples from your customers and staff. Product names, abbreviations and mixed-language questions need review in the context of your business. Your team approves the tone and terminology rather than assuming that a model’s ability to produce a language means it understands every local expression.

Language testing should include unclear questions, spelling variation and requests that change language during a conversation. An assistant may need to ask for clarification or pass the question to staff. Approved policies should keep the same meaning across languages; generating a fluent answer is not evidence that the business rule was applied correctly.

Specify who receives a handoff, when the assistant stops answering and what context goes with the request. That may include the customer’s question, relevant source information and what remains unresolved. Avoid collecting identity documents or payment information when a simple question does not require them.

Your team keeps ownership of complaints, exceptions and final commitments. A handoff should be tested as part of the workflow, including cases where the destination system is unavailable. Related lead follow-up automation can organize sales inquiries separately from support cases, with different permissions and approval rules.

A packaged platform may be enough when its source controls, channels and handoff options match a straightforward support task. Custom implementation becomes relevant when the workflow needs specific permissions, product behavior or connections that the platform cannot provide. Neither approach is automatically safer or more useful simply because of its label.

Compare the actual scope: source ownership, testing, processing, account access and who maintains the workflow. Use the cost guide to distinguish published SaaS plans from custom builds. If you first need to decide whether an assistant is appropriate, begin with an AI Discovery Session.

Every build gets the same prompt-injection and data-access checks we offer as a standalone review. We examine the agreed feature, its source information and the actions it is permitted to take. Access is enforced by the surrounding application and connected tools; telling a model to keep information private is not an authorization check.

A security check does not guarantee that a system cannot fail. We define human approval, handling for uncertain output and a route for unsupported requests. Read about our AI security review and the Data Privacy Act and AI guide before deciding what customer or employee information the workflow should process.

The quotation identifies the workflow, sources, systems, approvals and test environment. Agreed deliverables include implementation, representative tests, access configuration, operating instructions and handover. We document connected accounts, known limits, staff escalation and how to pause the workflow.

Source preparation, AI usage, support and later changes are distinguished in scope. Scoped quote after a free discovery call.

Start with a free discovery call. We work remotely worldwide, with in-person meetings in Metro Manila. Scope English, Filipino and Taglish using approved examples. Describe the task, its owner and confusing cases. Agree permissions and tests before live access, using sample data and a test environment while behavior is developed.

1. Discovery: discuss the current task, source information and people who own the result before choosing an implementation.
2. Scope: agree the workflow, permissions, human approval points and tests your team will use to assess behavior.
3. Build and test: evaluate real examples, missing information, hostile instructions and recovery in the agreed environment.
4. Launch and hand over: document ownership and staff handoff, then agree any optional AI Care Plan coverage.

Scoped quote after a free discovery call. Tell us which task you want to improve, which information staff rely on and where approval is required. Use anonymized examples for the initial discussion. Our published PH pricing guide explains third-party price references and the factors to compare; it does not quote ChuuniX prices.

Post-launch support is available on a retainer covering monitoring, updates, moving to new models as they become available and security updates. The AI Care Plan defines what is included rather than implying unlimited changes or an always-available support team. Knowledge updates, model and provider changes, and security re-checks are agreed against the workflow your team actually operates.

Keep a named owner for source information, connected accounts and human approvals. We agree how incidents are reported, which changes need a new scope and what is re-tested before release. AI usage and support work are distinct parts of the engagement. Scoped quote after a free discovery call.