A conversational AI contact center puts AI agents at the front of every inbound queue, phone and messaging alike, so routine contacts are resolved immediately and only the rest reach your team. On the levelpartner.ai platform, voice and conversation agents answer first, hand over by rule into one inbox and log everything to the CRM. This guide is for firms whose client-facing team handles enough volume to think in queues, and covers how to design the hand-off model so the AI reduces load without hiding problems.
Contact centre thinking for a mid-sized firm
You may not call it a contact centre. But if five or more people spend most of their day answering calls, chats and texts, you run one. The questions are the same as in a large operation: how fast is first response, how many contacts are resolved at first touch, how long do people wait for a person, and what are they contacting you about.
AI changes the shape of the queue. Routine contacts, such as status questions, rescheduling and directions, are handled at once. Your team works a shorter queue of contacts that genuinely need them, with the AI's summary already attached.
Designing the hand-off model
Write down, for each contact type, whether AI resolves it, AI starts and a person finishes, or a person handles it from the start. That table is the core design document.
- AI resolves: opening hours, document checklists, appointment changes, simple status questions.
- AI starts, person finishes: new enquiries needing advice, account questions beyond contact fields.
- Person from the start: complaints, vulnerable clients, disputes, anything regulated.
Before you start
- A count of inbound contacts by channel and type for the last month.
- The hand-off table above, filled in for your business.
- A voice agent and a conversation agent sharing one knowledge base.
- Team members set up as users, with assignment rules decided.
- Reporting agreed: first response time, resolution without hand-off, hand-off wait time.
How to set it up
- Open AI Agents and select the Conversation AI tab.
- Open your bot, or choose Edit from its three-dot menu.
- Select the Bot Goals tab.
- Under Setup Your Actions, click Human Handover.
- Turn on Enable Scenario for Human Requested.
- Add example phrases for your person-from-the-start contact types.
- Turn on Lack of Information and Failed to Resolve Issue.
- Choose the user each handed-over conversation is assigned to.
- Write the final message the bot sends before handing over.
- Set how long the bot stays paused after handover.
- Optionally create a task and a tag on handover.
What changes for your team
The team's work becomes harder on average and less repetitive. That is mostly good, but it changes staffing. You need people who handle the contacts AI cannot, not people who answer the same question forty times. Plan for training and give the team a say in which contact types the AI takes.
Take a client team of six handling about 2,400 contacts a month across phone, email and chat, and assume roughly 40 in every 100 are routine. That is around 960 routine contacts. If AI resolves most of them, the team would work about 1,500 contacts a month, each arriving with a summary attached. Because the AI answers first, first response on every channel would move from hours to under a minute.
Time to run it
Design and build take two to four days for a firm of this size, mostly the categorisation and knowledge work. Ongoing, a team lead should spend about two hours a week on hand-off reviews, knowledge updates and the monthly table review.
Firms measure only containment, the share of contacts AI resolved, and push it up at any cost. Clients then struggle to reach a person. Measure hand-off wait time alongside containment, and treat a long wait as a failure.
When this model does not fit
If your contacts are mostly complex and unique, AI resolves little and mostly adds a step. If your team is two people, a shared inbox and fast replies may be enough. For phone-specific design, the AI call centre and AI call center agent articles go deeper. For a broader look at AI in client service, see artificial intelligence customer care.
Troubleshooting
Hand-offs arrive without context. Conversation summaries are switched off. Turn on Enable Conversation Summary in Bot Goals so the person picking up does not ask the same questions again.
Clients loop between AI and voicemail. Out-of-hours hand-offs have nowhere to go. Offer a callback booking instead of a transfer outside hours.
Reports show volume but not outcomes. Contacts are not tagged by type. Have the agent tag each conversation with its category.
The team overrides the AI on routine contacts. They do not trust its answers. Share weekly samples of good AI resolutions and fix any that were wrong.
Frequently asked questions
Can a conversational AI contact center replace our phone system?
The platform can run numbers, call routing and voice agents. Whether to move fully depends on your current system and contracts. Many firms start by forwarding overflow and after-hours calls.
How do we keep quality high?
Review a sample of AI-resolved contacts weekly, not only the hand-offs. Problems hide in conversations that looked resolved.
What about data protection?
Conversations are stored with the contact in your CRM. Disclose automated handling, keep only what you need and include chat and call data in your privacy notice.
How many contacts can the AI handle at once?
It handles simultaneous calls and chats without a queue. Your bottleneck becomes the hand-off queue, which is why the alert workflow matters. The AI receptionist guide covers the front-desk side of the same set-up.