Generic chatbots hallucinate because they don't know your business. Our AI chatbot development service fixes that with RAG (retrieval-augmented generation): your docs, help center, product data, and knowledge base become the chatbot's source of truth, so every answer is grounded in your real information — with citations users can trust.
What our AI chatbot development includes
- Data ingestion & embedding — docs, PDFs, databases, help center, websites
- RAG pipeline with evals — measured accuracy, not vibes
- Guardrails & citations — safe, sourced answers that reduce hallucination
- Chat widget or API — embedded on your site or wired into your product, on your branding
- Monitoring & deployment — live, observable, and maintained
Where AI chatbots pay off
Customer support that deflects repetitive tickets, internal copilots that answer staff questions instantly, sales assistants that qualify leads, and documentation search that actually works. If your team keeps answering the same questions, a RAG chatbot earns its keep fast.
Do you need RAG or fine-tuning?
For chatbots that need to know your facts, the answer is almost always RAG — it's cheaper, updates instantly, and cites sources. We explain the trade-offs in RAG vs fine-tuning.
Frequently asked questions
How long does it take to build an AI chatbot?
A production RAG chatbot typically ships in about 30 days, depending on how messy the source data is.
What does it cost?
RAG chatbot builds typically start around $8,000 — see how much it costs to build an AI app for the full breakdown.
Which models do you use?
OpenAI, Claude, Gemini, or open-source models — we pick what best fits your accuracy, privacy, and cost needs.
Related services: AI development · AI MVP development · data pipeline services.