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OpenAI · Claude · WhatsApp API
AdwebX develops RAG-based, multilingual support and sales chatbots trained on your own data. Website, WhatsApp, and Instagram integration reduces your support load.
A RAG-based chatbot answers from your own documents, reducing hallucination risk. By automatically handling most FAQs, it frees your support team for high-value work.
Every month you wait, the gap between you and competitors widens.
73% of visitors who can't get instant support leave for a competitor — no second chance.
Manually answering WhatsApp and chat messages costs 40+ team hours per month — that's real payroll.
Without a chatbot, you lose an average of 15-30 potential leads per month to after-hours silence.
Real business outcomes with industry-standard tools.
First-generation chatbots operate on decision trees: if the user says A, respond with B; if C, then D. This approach works for limited, predictable questions — but real customer conversations are not predictable. A product question, a complaint, a price negotiation and a return request can all arrive in the same conversation at the same time.
An LLM-based chatbot uses natural-language understanding to interpret context, reads the intent behind the question and produces a coherent response. It is not bound to a fixed script; that flexibility fundamentally separates the customer experience from a rule-based bot.
A general LLM has no way of knowing your product catalogue, pricing, policies or company-specific information. That gap is closed in one of two ways: you continuously retrain the model (expensive, slow), or you use Retrieval-Augmented Generation.
RAG works as follows: when a user question arrives, the system first searches your real documents in a vector database, finds the most relevant passages and supplies them to the LLM as context. The result: responses are grounded in real sources, hallucination rates fall sharply, and when you update your document base the chatbot's knowledge updates too.
Activates the moment a visitor lands on your site. Product questions, pricing information, appointment requests or form completion — the chatbot handles these tasks in your brand voice, 24/7. Conversation data can be sent to your CRM or another defined system.
In Turkey, WhatsApp is the dominant customer communication channel. The system we build operates via the official WhatsApp Business API; messages are recorded, responses are delivered in your brand voice, and complex requests are handed to the live team with a summary.
Automatic responses to direct messages from Instagram, product information and routing. Particularly useful for e-commerce and consumer brands managing high inbound message volumes.
Turkish and English are our standard starting point. Arabic, German or other languages can be added depending on the use case. Language detection is automatic; the chatbot responds in whichever language the user writes in.
The greatest concern in LLM-based systems is the model presenting something it has fabricated with confident fluency. RAG reduces this risk but does not eliminate it. Additional measures:
Every chatbot must hand over to a human team at some point. In the system we build, this handoff is transparent and summarised: the conversation history and the understood topic are passed to the live agent. The agent does not need to reconstruct context. Handoff triggers are customisable: high-value customer signal, a specific topic, a low confidence score, or an explicit request from the user.
The opening question for chatbot deployment is: which questions do you receive most often, and who is currently answering them? The answer shapes the first version of the knowledge base. Fill in the form at /en/analysis for a free assessment, or message us on WhatsApp.
We build guardrails to stop the system when it would otherwise produce a wrong response: questions below the confidence threshold are routed to the human team and the topic fence prevents scope creep. Regular evaluation rounds monitor response quality.
Yes, this is the most common starting point. FAQs, product pages, support tickets and policy documents are added to the vector database to form the knowledge base. The fuller and more current the content, the more reliable the chatbot responses.
Meta has an approval process, but it moves faster through direct business partners. We manage this process on your behalf.
Conversation data is stored in the system you specify (your own database, CRM or secure cloud). For KVKK compliance, data minimisation and retention periods are defined at the design stage.
Yes. When the product catalogue, price list or policy document changes, the knowledge base is updated; no model retraining is needed. This is one of the core advantages of RAG architecture.
Basic chatbot integrations range from ₺8,000 to ₺20,000; full multi-channel AI assistants (web + WhatsApp + CRM) start at ₺40,000+. We scope the details in your free call.
We don't publish fixed price lists — quoting without scoping the project doesn't serve you. A free discovery call lets us understand your needs and present a project-specific proposal.
Let's define the project-specific investment together in a free discovery call.