AI chatbot development services for your website, Slack and Teams

Off-the-shelf chatbot builders are a sensible start when your questions are simple, your content fits on a website, and the vendor's hosting and pricing suit you. Set one up, watch what customers ask, and you will learn a lot in a few weeks.

A custom AI chatbot makes sense when answers depend on private documents or live data, when you need control over where conversations are stored, when the bot has to look things up in your systems or work in several languages, or when per-seat and per-conversation fees grow faster than the value they bring. With a custom build you own the code, choose the model and can move the hosting whenever you need to.

Under the conversation sits a retrieval system, usually called RAG (retrieval-augmented generation). It finds the passages in your documents most likely to answer the question and gives only those to the language model, with instructions to answer from them and to say so when they don't cover it. Our RAG development page explains the pipeline in detail.

Each answer links to the document, page or record it came from, so a reader can check it in one click. In Mavick, our AI operating system for textile factories, every answer also carries a confidence badge and a trace of the tools the agent used. Readers can see how sure the system is before they act on an answer.

Retrieval quality sets the ceiling on answer quality. Much of the work goes into cleaning documents, handling tables and scanned PDFs, and keeping the index current when a policy changes.

Guardrails keep the chatbot on task. We limit it to the topics you approve, stop it from quoting prices or making commitments it can't confirm from your data, remove personal data from logs where required, and route complaints, legal questions and low-confidence answers to a person.

One assistant can serve several channels. A website widget, a WhatsApp number and an internal Slack or Microsoft Teams bot can share a single knowledge base with different permissions, so staff see internal documents and customers see public ones.

Analytics show what people ask, which questions went unanswered, where the bot handed off and how users rated the answers. Those reports tell your team which documents to write or fix next.

Four things move the price most: the volume and condition of your documents, the number of channels, integrations with live systems such as your CRM or order database, and the languages you need. A bot that answers from a clean set of policies on one website is a small project. One that reads scanned manuals, checks order status and runs in three languages across three channels is a larger one.

Running costs come from model usage, hosting and, on WhatsApp, Meta's message fees. The timeline follows the scope. You see a working chatbot on your own documents in the first sprint, and after discovery we commit to milestones and a fixed price in the proposal.