AI Chatbot Development

Chatbots that read your documents, ground every answer, and ship with the engineering rigor you'd put behind any production system. Built on GPT-4, Claude, or open-weight models. Deployed to your cloud.

Start a project Gabbex case study

What we mean by "real AI chatbot"

Strip away the marketing layer and there are two kinds of chatbot vendors. The first sells a flowchart builder dressed up with the word "AI." The second builds a retrieval-augmented system that reasons about your content. We are the second kind.

A real AI chatbot answers questions it has never seen before, using documents you control, in your brand voice, and tells you exactly where the answer came from. The difference is most visible the first time a customer asks something off-script and the bot answers correctly.

Where chatbots earn their keep

Customer support is the obvious one. A well-tuned bot deflects 30 to 60 percent of tier-one tickets and hands the rest to a human with the full context already loaded. That alone usually pays for itself within a quarter.

The less obvious wins: internal help desks for HR and IT, where one bot replaces a Confluence search that nobody trusts. Sales qualification, where the bot books the call and prefills the CRM. Documentation, where a chatbot makes a 400-page knowledge base actually usable.

What we build into every chatbot

Retrieval-augmented generation (RAG)

We chunk your documents, embed them, and store them in pgvector or a managed vector DB. At query time, we retrieve the relevant chunks and pass them as context to the LLM. The model only answers from what we gave it. Hallucination drops sharply.

Citations on every answer

Every claim in the response is linked to the source chunk. Users see footnotes. Trust goes up. We covered the implementation pattern in a separate writeup linked below.

Lead capture and handoff

The bot recognizes intent ("this person wants a demo, not an FAQ") and either books the call directly or hands off to a human in your inbox with the conversation transcript attached.

Channel parity

Same brain on web, Messenger, WhatsApp, Slack, Teams. Conversation state follows the user. Identity resolves across channels.

Gabbex: what a production chatbot platform looks like

We built and run Gabbex, a multi-tenant AI chatbot platform for small businesses and online stores. It serves customers across e-commerce, services, and education. What we learned running our own product in production is what we bring to bespoke chatbot work. The case study below has the details.

Related reading

Frequently asked questions

What's the difference between a real AI chatbot and a no-code one?

A no-code chatbot is a decision tree wearing a hoodie. It follows rules you draw on a canvas. A real AI chatbot uses an LLM to understand intent, retrieves from your own documents (RAG), and writes the answer from scratch. The first feels like 2014. The second feels like a knowledgeable colleague who actually read your docs.

How do you stop AI chatbots from hallucinating?

Two things. First, RAG with proper retrieval, so the model can only answer from documents you've approved. Second, citation-extraction at the answer level — every claim in the response maps back to a source document, and the UI shows that source. If a claim has no citation, we filter or flag it. The pattern is described in detail in our RAG-with-citations writeup.

Can the chatbot live on my website, WhatsApp, and Messenger?

Yes. We deploy across web (iframe widget or embedded), Messenger, WhatsApp Business API, Slack, and Microsoft Teams from a single backend. The conversation state, identity, and knowledge base are shared. Switching channels mid-conversation works.

Who owns the chatbot and the data?

You do. We deploy to DigitalOcean, AWS, or Render, with a managed database on DigitalOcean or Supabase. The prompts, the knowledge base, and the conversation logs all sit in your infrastructure. We don't lock you into a Jeva-hosted platform. The only third parties involved are whatever LLM vendor you choose (OpenAI, Anthropic, or self-hosted).

How much does AI chatbot development cost?

Production chatbots with RAG, citations, and multi-channel deployment land between $8,000 and $35,000 depending on the knowledge base size and channel count. A simple website chatbot with one document source can land closer to $5,000. That's senior SE Asia pricing in USD — a US or Western European agency typically quotes 2.5 to 4 times these numbers for the same scope. We never quote without a scoping call.

Need a chatbot that actually works?

One scoping call. We'll tell you whether RAG is the right answer or whether you need something simpler.

[email protected]