ZENKEI
Custom AI chatbots grounded in your own documents and connected to your tools, deployed on your website, messaging apps and voice, with private options.
Rated 4.9 out of 5 by clients across AI, software and quantitative projects.
Custom AI chatbot development is building a chatbot around a language model and your own content, so it answers in your voice, from your documents, rather than from a fixed script or generic knowledge. ZenkeiX builds AI chatbots that are grounded in your data, connected to your tools, and deployed wherever your customers already are. To keep answers accurate, we lean on retrieval: see how to build a RAG pipeline.
Whether you need a support assistant that deflects repetitive tickets, a lead-generation bot on your website, or an internal assistant over your knowledge base, we scope it around a metric that matters and ship it, with source citations, guardrails against hallucinations, and a private deployment when your data cannot leave the building.
Not a scripted widget. Chatbots that know your business and act on it.
Chatbots grounded in your help centre and docs that answer customer questions accurately, cite sources, and hand off to a human when needed.
On-site assistants that qualify visitors, answer product questions and book calls, turning traffic into conversations and leads.
A private chatbot over your policies, wikis and databases, so your team finds answers in seconds instead of digging through documents.
The same chatbot on web, WhatsApp, Slack and Telegram, plus voice assistants, so customers reach it on the channel they already use.
One working session on your questions and content. We leave with the use case a chatbot should own and the metric it should move.
We connect the model to your documents and tools with RAG, tune the tone, add guardrails and citations, and wire up the channels you need.
Deployed, monitored and refined from real conversations: resolution rate, deflection and satisfaction, not vanity metrics.
Custom AI chatbot development is building a chatbot around a large language model and your own content, so it answers in your voice, from your documents and data, rather than from generic knowledge or a fixed script. It is tailored to your use case and connected to your systems.
A rule-based chatbot follows a fixed decision tree and only handles the questions it was scripted for. An AI chatbot uses a language model to understand free-form questions and generate answers, and when grounded in your documents with RAG it can respond accurately across a much wider range of topics.
Yes. We ground the chatbot in your content using retrieval-augmented generation, so it answers from your documents, help centre, product data and databases, and can cite its sources. This is what keeps answers accurate and specific to your business.
On your website, and on channels like WhatsApp, Slack, Messenger and Telegram, plus voice assistants when needed. The same underlying chatbot can serve several channels at once, so customers reach it wherever they already are.
We are model-agnostic and choose per project: Claude, GPT, Gemini, and open models such as Mistral, Llama and Qwen. Open models can be self-hosted for privacy or cost, and we select the model that fits your accuracy, latency and budget needs.
Yes. We can deploy a chatbot on open-weight models hosted on your own infrastructure or private cloud, so conversations and documents never leave your environment, which matters for regulated or sensitive data. It pairs naturally with custom LLM development.
Thirty minutes on your use case. No slides, no fluff.