Question 1

How do you stop the chatbot from making things up?

Accepted Answer

Grounding is the main defense: with RAG, the model answers only from retrieved passages of your own content and cites its sources. We add guardrails that make the bot say "I don't know" and offer a human instead of guessing when retrieval finds nothing relevant. Pre-launch evaluation against real customer questions measures the hallucination rate before customers ever see the bot.

Question 2

What content do we need to provide?

Accepted Answer

Whatever already answers customer questions: help center articles, product documentation, policy pages, internal macros, even past ticket resolutions. A few dozen well-written articles are enough to start — coverage matters more than volume. The knowledge audit in week one shows exactly where the gaps are.

Question 3

How much does it cost to run per month?

Accepted Answer

Model usage is usually the smallest line item: a typical grounded answer costs a fraction of a cent to a few cents depending on the model, so even thousands of conversations a month typically stay in the tens of dollars range. Vector database hosting adds a modest fixed cost. Compare that against the loaded cost of a human answering the same repetitive questions.

Question 4

GPT or Claude — which is better for chatbots?

Accepted Answer

Both are excellent, and the honest answer is: it depends on your content and tone requirements, so we benchmark both on your actual questions. Claude tends to follow nuanced instructions and refusal policies closely; GPT-4-class models have broad ecosystem support. The architecture we build is model-agnostic, so you can switch later without rebuilding.

Question 5

Can it hand over to a human agent?

Accepted Answer

Yes, and it should — handoff is a feature, not a failure. We configure triggers: explicit customer request, detected frustration, low-confidence answers, or sensitive topics like billing disputes. The human agent receives the full conversation transcript so the customer never repeats themselves.

Question 6

Does it work in multiple languages?

Accepted Answer

Modern LLMs handle major languages well, including Turkish, German, Arabic, and Urdu — the bot can answer in the customer's language even if your knowledge base is in English. For your most important non-English markets we recommend evaluating answer quality per language and translating the highest-traffic articles for best accuracy.

Question 7

How does the bot stay up to date when our docs change?

Accepted Answer

The ingestion pipeline re-syncs your sources on a schedule or via webhooks, so published changes reach the bot within minutes to hours without manual work. Deleted or outdated pages drop out of the index automatically. We also flag questions the bot answered from stale content during weekly reviews.

Question 8

What about data privacy and where our data goes?

Accepted Answer

Your knowledge base lives in your own vector database, and API providers like OpenAI and Anthropic do not train on business API data under their standard terms. We can add PII redaction before messages reach the model, EU data residency options, and self-hosted open-source models where regulation demands full control.

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