AI chatbot development services

The work starts with choosing the queue worth automating. We then connect the chatbot to your knowledge sources and systems (RAG over documents, CRM and helpdesk data), set guardrails and refusal rules, build human handoff with the full conversation attached, integrate the channels and evaluate the result on real conversations. The engagement ends with ownership transfer: code, runbooks and prompts your team can change without us.

An off-the-shelf platform is enough when questions are simple, the answers sit in one FAQ and nothing has to be looked up in your systems. A custom chatbot is worth building when answers depend on your data and tools, when you must control where data is processed, or when a wrong answer has a real cost. We tell you which case applies during scoping, before any build.

The price depends on the number of channels, the integrations, the volume of knowledge the chatbot answers from and your compliance requirements. For that reason the first step is a Proof of Value on your real tickets and documents. You see answer quality, handoff behavior and running costs before you commit to the full build.

Websites, mobile apps, messaging platforms such as WhatsApp and Slack, social channels and internal tools (intranet, HR, IT helpdesk). One grounded agent can serve both customers and employees, provided the knowledge sources and guardrails are scoped for each group.

Security is designed in from the first sprint. Processing is set up to align with GDPR: encryption in transit and at rest, role-based access, audit logging and clear data-retention rules. When you require it, retrieval and inference stay in your VPC or an approved cloud tenancy.

The chatbot takes the high-volume requests that have documented answers (status checks, policy questions, FAQ, order lookups) and escalates edge cases to a person with the full conversation attached. Your team spends its time on the exceptions.

A custom chatbot grows with traffic and knowledge volume. We design retrieval, evaluation and handoff so you can add channels, languages and product lines without rewriting the core agent or depending on a closed vendor platform.

Any operation with repeatable questions and documented answers: customer support, sales enablement, internal helpdesk, training and field operations. Whether a chatbot fits depends more on the quality and ownership of your data than on the industry.

Yes, and both are published as case studies. For ARIJ Network we built a bilingual English/Arabic agent inside their Moodle environment that answers journalists only from ARIJ's own knowledge base, in English or Arabic. For Mixam, a three-agent product advisor guides customers through print-order configuration, and conversion to a final sale rose from about 20% to about 40%.

Ask three things before you sign: will they test the chatbot on your real tickets before the full build, who owns the code, prompts and evaluation sets afterwards, and how the bot hands a conversation to a person. Our checklist is in the article How to choose an AI chatbot developer, linked below the FAQ.

A chatbot answers in a conversation. An agent can also act in your systems, for example look up an order or update a ticket. The chatbots we build work as grounded agents: they answer from your sources, look up what the task needs in your CRM or helpdesk, and hand the rest to a person.

Customer support chatbots on your website, in your app or on WhatsApp, which handle status checks, policy questions and order lookups. Internal helpdesk chatbots for HR, IT and intranet questions in Slack or internal tools. Product advisors that guide customers through a configuration and act in your systems through tools, like the one we built for Mixam. All of them retrieve from your documents and systems, so answers come with sources.