--- title: "AI Chatbot Development for Customer Support" canonical: https://wavect.io/services/ai-chatbot-development/ language: en description: "Wavect builds customer-facing AI chatbots for websites, shops and messaging: answers grounded in your content and systems, order and returns support, human handoff and evaluation." image: "https://wavect.io/img/general/bak/open_graph_preview.jpg" --- AI Chatbots # AI chatbots that answer your customers from your data, and hand over when they should. A support chatbot is only useful if its answers are true and its limits are clear. We ground it in your help content, catalog and order data, connect it to your helpdesk for human handoff, and measure what it actually resolves before it goes to every visitor. THE ENGAGEMENT ## Ground the answers. Hand over cleanly. Measure what it resolves. 1. **Pick the conversations** The questions it answers, the ones it hands over, the ones it refuses. 2. **Build and connect** Sources, shop and helpdesk integrations, channel and guardrails. 3. **Evaluate and launch** A rerunnable test set, a staged rollout and handover notes. DIRECT ANSWER AI chatbot development builds a customer-facing assistant for your website, shop or messaging channels that answers from your own content and systems. This engagement covers conversation design, grounding, order and returns lookups, human handoff, guardrails and an evaluation you can rerun after every change. Best for - Shops and service businesses whose support team answers the same order, returns and product questions every day - Teams with an existing chatbot that gives wrong answers or never hands over to a person Consider another approach - An assistant for employees that answers from internal company knowledge - An AI agent product that plans and acts across tools as its core function ## A chatbot for your customers, not an internal assistant This service builds the assistant your customers talk to. An assistant that answers employees from internal documents belongs to [Internal AI Assistants](/services/internal-ai-assistant/). An AI product whose core job is planning and acting across tools belongs to [AI Agents & Products](/services/artificial-intelligence/), and company-wide AI adoption to [AI Enablement](/services/ai-enablement/). Building or extending the store itself is [E-commerce Development](/services/ecommerce-development/); an independent abuse and permission test of a finished chatbot is [AI Security](/services/ai-security/). ## E-commerce support: orders, returns and product questions In an online shop, most support conversations repeat: where is my order, how do I return this, does this fit, is it back in stock. These are the conversations a chatbot can take when it reads live shop data instead of guessing. Order status The customer identifies the order, the bot reads status and tracking from the shop or fulfillment system and answers with the current state. No order data without a matching check. Returns and exchanges The bot explains your actual return policy, checks eligibility against the order date and items, and starts the return flow your shop already uses. Exceptions go to a person. Product questions Answers come from your catalog, product data and help content. Prices and stock are read live from the shop, never written from the model's memory. Handoff to a human agent When the bot is unsure, the customer asks for a person or the topic is sensitive, the conversation moves to your helpdesk with the transcript and order context, so nobody asks twice. ## Answers from your sources, actions within the limits you set The model writes the sentence; your systems supply the facts. We connect help articles, policies, catalog and order data, keep track of which source and version an answer used, and make the bot say so when the evidence is missing instead of inventing a plausible reply. Reading data and changing it are separate: looking up an order is one permission, cancelling it or issuing a refund is another, and we only wire actions you approve, with a confirmation step. If you are still deciding between retrieval and other architectures, [MCP vs RAG vs Agent Skills](/blog/mcp-vs-rag-vs-agent-skills-vs-custom-gpts/) compares the options. ## Guardrails, disclosure and what the bot will not do Customers will test the bot, some on purpose. We define the topics it handles, the ones it declines and how it reacts to attempts to change its instructions or pull other customers’ data. Under Article 50 of the EU AI Act, people must be told they are talking to an AI system unless that is obvious; we build the disclosure into the chat interface, and [EU AI Act Compliance](/services/eu-ai-act-compliance/) covers the wider technical readiness. Whether a rule applies to you is for your counsel to decide. Conversation logs follow a retention period you agree, with personal data kept to what the support case needs. ## Illustrative evaluation: measure resolution, not fluency Illustrative, not a client result. Before launch we agree a test set built from real question types with synthetic or approved examples. A customer asking for an order status without matching details gets no order data. A return outside the policy window gets the policy and a handoff option, not an invented exception. A product question with no answer in the catalog says so. The engagement measures how many test conversations end correctly, how often handoff happens and why, unsupported statements, response time and model cost per conversation. After launch the same set reruns on every prompt, model or source change, and the thresholds for a wider rollout are agreed with you, not promised in advance. ## Design, build and roll out in stages Each phase is scoped and quoted before work starts. You can take the design to your own team or commission the agreed build. 1. **Conversation and source design** - The question types in scope, sources and owners, systems to connect, handoff rules, guardrails and the evaluation set. 2. **Build, evaluate and launch** - The chatbot in your channel, connected to your shop and helpdesk, tested against the agreed set and released to a share of traffic first. 3. **Continued development** - New topics, sources, channels and model updates, agreed separately. This does not create 24/7 coverage or an uptime SLA. // proof ## Adjacent AI product engineering We have no published client case for a customer-support chatbot, and we do not claim one. Hyperstate AI documents a user-facing, producer-style assistant that kept context across sessions; Wavect split its agent, audio and generation workloads into separately scaling services. Twinsoft AI documents LLM integration and production hardening of an AI product before enterprise pilots. PromptID documents a model-agnostic LLM setup with an asynchronous evaluation queue. None of them is a promise of the same result for your chatbot. ### [Hyperstate AI](/case-studies/hyperstate-ai/) - **Status:** Wind Down - **Outcome:** GPU Monolith - → Microservices - **Summary:** Split a GPU-heavy monolith into orchestrated services and swapped self-hosted ML libs for scalable alternatives. Latency and cost … - **Stack:** Python, Django, OpenAI API ### [Twinsoft AI](/case-studies/twinsoft-ai/) - **Status:** Live - **Outcome:** 2 weeks - To Sellable MVP - **Summary:** Took a vibe-coded prototype to enterprise pilot-ready, no shortcuts. - **Stack:** Python, Flask, LLMs ### [PromptID](/case-studies/promptid/) - **Status:** Live - **Outcome:** 6 weeks - 0 to Production - **Summary:** AI-native assessment platform, 0→production in 6 weeks under compressed market pressure. - **Stack:** NestJS, Next.js, LangChain These are selected projects, not our full portfolio. We have shipped 100+ products since 2018. ## Questions before you start ### Will the chatbot replace our support team? No. It takes repeated questions within the agreed scope and hands the rest to people with context. How many conversations it resolves is something the pilot measures for your traffic, not a number we promise up front. ### Which channels can it run on? A widget on your website or shop is the usual start. Messaging channels such as WhatsApp or Instagram depend on the provider’s business API and account approval, so we confirm them during scoping. ### Can it connect to Shopify and our helpdesk? Usually, if your shop platform and helpdesk offer API access we can use. We confirm interfaces, test data and who owns each account before the build starts. ### What stops it from inventing prices or policies? Prices, stock and order data are read live from your systems, policies come from your own documents, and the bot is tested on questions it should decline. When evidence is missing it says so and offers a person. ### Which AI model do you use, and who owns the data? The model runs under your own provider account, and the code lives in your repository. We keep the model behind a layer that can be swapped, and conversation data stays in systems you control under the retention you set. ## Choose your next step - [Build an assistant for your employees](/services/internal-ai-assistant/) - [Build or extend your online store](/services/ecommerce-development/) - [Test your chatbot for abuse and data leaks](/services/ai-security/) ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", 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