Custom AI chatbot development services for websites and business workflows

Cost depends on the knowledge sources, integrations, security requirements, conversation volume, and evaluation scope. After a discovery call, Weblynx provides a project-specific proposal rather than unsupported fixed pricing.

A focused prototype can be completed before a production build, while integrated systems require more time for data preparation, testing, and safeguards. The schedule is confirmed after discovery and depends on scope.

Yes. A RAG chatbot can retrieve from approved website content, documents, and structured data so answers are grounded in current business knowledge without training a foundation model from scratch.

Yes, where those systems provide suitable APIs or integration methods. We scope permissions, data flow, error handling, and human ownership before enabling actions.

We combine curated sources, retrieval controls, answer constraints, evaluation sets, confidence-aware fallbacks, monitoring, and human escalation. No AI system is guaranteed to be error-free.

Data handling depends on the chosen provider and contract. We select appropriate API and deployment settings, minimize shared data, and document retention and access requirements before launch.

A scripted chatbot follows predefined branches. A RAG chatbot retrieves relevant approved knowledge and uses a language model to answer more flexible questions, while still needing guardrails and evaluation.

The right model depends on answer quality, tool use, latency, context size, privacy needs, and cost. We test against your real use case instead of choosing from brand preference alone.

Yes. Human handoff can pass the conversation context to an approved support or sales channel when the user asks, confidence is low, or a business rule requires escalation.

If the bot only needs to answer a fixed set of FAQs and hand off to email, an off-the-shelf tool is the right answer and we will say so on the call. Custom wins the moment the bot has to read your own documents or data, act inside your systems, respect who is asking, or be evaluated against your real questions. Those are the things a widget cannot do.

Yes, on any stack. It needs three things from the site: access to the content it should answer from, an embed point for the chat surface, and somewhere to send a handoff. The site itself does not have to change beyond that.

Yes. A retrieval layer over your documents and data is useful for internal search, agent tools, and reporting, not only for chat. We scope the sources, the chunking and indexing, the access rules, and the evaluation set, and expose it through an API your other tools can call.