Give customers and staff a useful way to ask questions about your business. Shenox builds AI chatbots and knowledge assistants around approved information, connected workflows and a clear route to a person.
Organize approved FAQs, documents and service information into a retrievable knowledge base. Retrieval-augmented generation, or RAG, gives the assistant relevant source material to work from instead of relying only on general model knowledge.
Collect the information needed for a useful follow-up and connect conversations to customer records. Scheduling, shared inboxes and task creation can be included where the chosen providers support them.
Define escalation rules for missing information and requests outside scope. Preserve the conversation context so a team member can pick up the enquiry without asking the customer to start again.
Conflicting policies, outdated prices and missing documents are knowledge problems before they are chatbot problems. We review source ownership and update needs during discovery. Grounding can improve relevance, but it does not guarantee every answer is correct. A release needs a test set, fallback behavior and limits on any action the assistant can take.
These write-ups describe the implementation and scope of each build.
Scope depends on the volume and condition of the knowledge base, channels, languages, integrations and action permissions. Ongoing model, messaging and hosting charges depend on the connected services and usage. These are considered separately from the development work.
Yes. We can scope retrieval over approved documents and structured business information. Access permissions, source updates and how to handle conflicting material need to be defined before release.
A chatbot can connect to customer and lead records when the CRM provides suitable API access. We agree which fields it can read or update and where a person must approve a change.
The channel is selected during discovery. Website chat and supported messaging-provider integrations can be scoped, while account approval, provider access and usage charges depend on the chosen service.
No AI system can promise that. We use approved sources, representative question tests, defined boundaries and human escalation to reduce the chance and impact of unsupported answers.
Send the questions your team answers most often, links to approved information, the intended channel and what should happen after an enquiry. Redact customer details from sample conversations.