A chatbot that says 'I don't know' instead of confidently making something up.

This is the core problem we build against, an AI chatbot given an open-ended general model with no grounding tends to generate a plausible-sounding but potentially wrong answer rather than admitting uncertainty. We build the chatbot to recognize when a question falls outside its actual knowledge base and hand off to a human rather than guess.

It hands off to a human, via a WhatsApp handoff, a contact form submission, or a direct routing to your team, depending on how we've configured the boundary, rather than attempting an answer outside its actual knowledge base. This handoff behavior is a deliberate design choice, not a fallback we consider a failure state.

No, a generic AI chatbot plugin or an unmodified general AI model has no specific grounding in your business's actual content and will answer questions about your business with plausible-sounding guesses. We build a knowledge base specifically from your real content and configure the chatbot to stay within it, which is a meaningfully different and more reliable approach.

The scope and boundary definition stage specifically constrains what the chatbot attempts to answer, and testing before launch includes probing with edge-case and adversarial questions to check how it responds to things outside its intended scope, rather than assuming default behavior will handle every situation appropriately.

AI Automation covers backend business processes (routing, reporting, data workflows) typically without a customer-facing conversation; this service is specifically the customer-facing conversational interface. Some engagements combine both, a chatbot that also triggers backend automation, which we'd scope as a combined project spanning both services.

Cost depends on how much content needs to be compiled into the knowledge base and how complex the chatbot's scope is, a chatbot answering straightforward FAQs costs less than one handling multi-step processes like booking or order tracking. We scope this after the knowledge base assembly stage, once we understand your actual content volume.

Timeline depends on how much content needs compiling into the knowledge base and how complex the scope is, a straightforward FAQ-answering chatbot can be built faster than one handling multi-step processes or integrations. We provide a specific timeline once the knowledge base assembly and scope definition stages are complete.

It depends on your actual inquiry volume and pattern, a business fielding the same handful of questions repeatedly benefits from automating those regardless of size, while a business with low, highly varied inquiry volume may get less value relative to the setup cost. We'll assess this honestly during scoping rather than assuming every business needs one.

Yes, we can integrate through WhatsApp Business, depending on your preferred channel and where your customers actually reach out to you. We'll ask about your current customer contact patterns during scoping rather than defaulting to a website widget regardless of where your actual inquiries come from.

We hand off with documentation on how to update the knowledge base directly, so routine updates (a new FAQ, a policy change) don't require contacting us every time. Larger structural changes to the chatbot's scope or behavior would typically go through us as a scoped update.

This is possible but adds real complexity beyond a knowledge-base FAQ chatbot, integrating booking or transaction handling requires connecting to your actual scheduling or payment systems, which we'd scope as a more involved project, closely related to our AI Automation service for the backend workflow piece.

The knowledge base is designed to be updated directly by you after handoff, specifically so frequent changes (pricing, availability, seasonal information) don't require an ongoing developer relationship for routine updates, we'd flag during scoping if your update frequency suggests a different technical approach would serve you better.

Yes, this can be built into the chatbot's configuration and knowledge base, though it adds scope since content and testing need to cover each supported language properly. We'd discuss during scoping whether this is a priority for your specific customer base before including it in the initial build.

Yes, tracking what questions come through, including ones the chatbot couldn't answer and handed off, provides genuinely useful insight into what your customers actually want to know, which can inform both the knowledge base's ongoing updates and your broader customer service or content strategy.

Data handling and storage get scoped and disclosed clearly as part of the build, since this touches real customer information and privacy considerations, we're not going to gloss over this given how directly it affects trust with your own customers, and we'll discuss your specific requirements and any relevant compliance considerations during scoping.

Ask specifically how the chatbot handles a question outside its knowledge base, an agency that can't describe a clear handoff behavior is likely building on an open-ended general model prone to confidently fabricating answers about your business.

We're a strong fit if you want a chatbot grounded in your real content with an honest 'I don't know, let me connect you with someone' boundary, we're a weaker fit if you're looking for a chatbot that appears to handle absolutely anything, since that's usually where fabrication risk lives.