AI chatbot development
Build an AI chatbot that can answer from the right information and hand off when it should
A useful business chatbot needs more than a model and a chat box. It needs trusted source content, permissions, conversation rules, integrations and a clear escalation path when the answer is uncertain or the user needs a person.
AI Creative develops customer-facing and internal AI chatbots around those requirements.
Example interface
Support assistant
What is the warranty period on installed equipment?
Installed equipment carries a 24-month parts and labour warranty from the commissioning date. Consumable items are excluded.
- Service terms v4.2, section 6
- Commissioning handover checklist
Types
Customer-facing and internal assistants
The aim is not to block access to support. It is to reduce repetitive work while making escalation cleaner.
Customer service chatbots
Answer routine questions, collect information, retrieve account-aware status where authorized and hand off with useful context.
Read moreInternal knowledge chatbots
Search policies, procedures, product information and technical documentation, with the supporting source shown.
Read moreWorkflow-connected assistants
Collect a service request, book through an existing system, open a case or prepare a structured handoff.
Read more
For internal use, employees can search policies, procedures, product information and technical documentation through a conversational interface. The answer should point back to the supporting sources so users can verify important information rather than treating generated text as an authority by itself.
Permissions
Permissions still apply in conversation
A user should not receive information through a chatbot that they would not be allowed to access in the underlying system.
Authentication, group membership, record ownership and document permissions may need to shape retrieval before an answer is generated.
Example system view
Public knowledge only. No identity, so no account data is eligible.
- Published support articlesEligible
- Product and service documentationEligible
- Account recordsBlocked
- Internal proceduresBlocked
Action
Connect the bot to workflow when it adds value
A chatbot can do more than answer questions when the use case requires it.
It may collect a service request, schedule an appointment through an existing system, open a case, retrieve order status or prepare a structured handoff. Any write action should use controlled tools with validation and explicit permissions.
Where the job needs multi-step tool use, that is agent territory →
Public knowledge and account data are different problems
Escalation
Design the escalation path
The bot needs to know when to stop.
Low confidence, sensitive topics, account exceptions, billing disputes, contractual questions or repeated failure can trigger a human handoff. The receiving person should get the relevant transcript and structured context rather than making the customer start over.
Example system view
Conversation
A question arrives with whatever identity and context is available.
Confidence and policy gate
Evidence quality, permissions, topic sensitivity and conversation history decide the path.
- Grounded answerEvidence found in an approved source the user is allowed to see.
- Cited responseThe answer points back to the source so it can be verified.
- Feedback capturedA bad answer can be reported and routed to the content owner.
- Human handoffLow confidence, sensitive topic, billing dispute or repeated failure.
- Context transferredTranscript, account reference and the reason for escalation.
- No restartThe customer does not repeat what they already explained.
Measurement
Measure whether the chatbot is helping
The goal is not to maximize the percentage of conversations handled by AI if that degrades the experience or creates risk.
| Measure | What it tells you |
|---|---|
| Containment for eligible requests | Only counted where the request was one the assistant should handle. |
| Escalation quality | Whether the receiving person got usable context or had to start again. |
| Answer accuracy | Checked against approved sources on a representative question set. |
| Unresolved conversations | Conversations that ended without an answer or a handoff. |
| Customer effort | How much work the person had to do to get to the right answer. |
| Support time saved | Measured against the handling time of the same request types before launch. |
Knowledge
Manage knowledge as part of the product
Chatbot quality degrades when source content becomes stale or contradictory.
- Who owns each body of source content.
- How updates are indexed and how quickly.
- Which sources outrank others when they disagree.
- How a user reports a bad answer, and who acts on it.
FAQ
Frequently asked questions
Yes through a permission-aware knowledge and retrieval layer where the source content is suitable for the use case.
Yes, where integration access exists and the actions are defined safely.
Yes. The same underlying knowledge architecture can support different interfaces and permission levels where appropriate.
By constraining the use case, grounding answers in approved sources, using citations where useful, testing representative questions and providing a fallback when evidence is insufficient.
Give users a faster path to the right answer without hiding uncertainty
Bring the questions your team answers most often. We will define the sources, the permissions and the point at which a person takes over.