AI ChatbotDevelopment
A website chatbot that answers from your own documents, admits when it does not know, and hands the conversation to a person before the visitor gives up.
AI Chatbot Development: what the work involves
Most small teams answer the same twenty questions all week: opening hours, delivery areas, what a plan includes, how to reset an account. Visitors who message after hours wait until morning, and many never return. Old-style chatbots made this worse with rigid menus that could not understand a normal sentence, so people typed one frustrated message and left.
We build the bot around a retrieval layer. Your policies, product pages, PDFs and past answers are cleaned, split into passages and indexed in a vector store such as pgvector. When a visitor writes, the service finds the relevant passages and asks a language model from OpenAI, Anthropic or Google to answer using only that material, citing the source. A small prompt-injection filter and a scope rule keep it on topic. We launch behind a feature flag, review real transcripts together, and widen its permissions only as the answers prove reliable.
Core features
Answers grounded in your content
Replies are assembled from retrieved passages of your own pages and documents, with the source link shown, rather than from whatever the model remembers from training.
Honest fallback when unsure
When retrieval returns weak matches, the bot says it is not certain and offers a contact form or live agent instead of improvising an answer.
Human handover with context
A visitor can ask for a person at any time, and the whole conversation is passed to your inbox or helpdesk so nobody has to repeat themselves.
Lead and detail capture
The bot can collect a name, phone number and short requirement through natural conversation and push it to your CRM or a spreadsheet.
Website and in-app embedding
A lightweight widget drops into Next.js, WordPress or Shopify sites, and the same API can power a screen inside your mobile app.
Transcript review dashboard
Every conversation is logged so you can mark answers good or bad, spot missing content, and feed corrections back into the knowledge base.
What we get right before launch
Hallucination and scope control
A language model will happily invent a refund rule. We restrict answers to retrieved text, test with a fixed set of tricky questions before launch, and keep a refuse-and-escalate path for pricing, legal and medical topics.
Personal data in chat
Visitors paste phone numbers and order details into the box. We decide what is stored, mask sensitive fields in logs, set a retention period, and tell users plainly that they are talking to an automated assistant.
Running cost per conversation
Each reply costs model tokens, and long histories cost more. We pick a model tier that fits your traffic, cap context length, and cache common answers so usage stays predictable.
Tools and technology
- OpenAI
- Anthropic Claude
- LangChain
- pgvector
- Next.js
- FastAPI
- PostgreSQL
- Redis
Related work · AI Agency Website
TAMx
An AI-powered digital agency website — generative AI, chatbots, LLM integration, computer vision, and automation, plus products like an LMS and CRM, presented for startups and enterprises worldwide.
Common questions, answered
Will the chatbot make things up?
It can if left unconstrained, so we limit it to retrieved passages from your own content, show sources, and make it decline when matches are weak. We also test it against a fixed question set and review real transcripts after launch.
Can it speak Urdu and English?
Yes. Modern models handle English and Urdu script well, and Roman Urdu reasonably so. We test your actual customer phrasing, since spelling varies a lot, and add examples where the model misreads intent.
How long does a first version take?
A focused bot on one knowledge source can be live in a few weeks. Time depends mostly on how organised your content is and how many systems it must connect to, such as a CRM or helpdesk.
What drives the ongoing cost?
Model usage per conversation, hosting for the vector database and API, and any messaging channel fees. Volume of chats and chosen model tier matter most. We give you usage reporting so there are no surprises.
Who owns the data and the bot?
You do. Content, transcripts and configuration live in your accounts or a deployment we hand over, and you can switch model providers without rebuilding the whole system.
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AI Customer Support Agent Development
An agent that actually does support work: checks the order, applies your policy, drafts or sends the reply, and escalates anything it cannot settle.
AI Knowledge Base Development
Gather the answers that live in PDFs, chats and people's heads into one searchable base that explains itself and shows where each answer came from.
Ready to start your AI Chatbot Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.