If you are searching for Ada conversational AI, you are probably weighing a standalone customer-service AI product against something built into the system where your contacts already live. This article does not review Ada or describe its features. It answers a narrower question for a company of 10 to 250 people: what job are you hiring a conversational AI to do, what should you check before choosing a standalone product, and how do you build the same job on the levelpartner.ai platform if your CRM, calendars and messaging are already there?
The job behind the search
Standalone conversational AI products are usually bought by support teams with high ticket volumes. Their job is deflection: answer the repeated question so a person does not have to. For a firm with flat revenue and a small team, the job is often different. You want the same conversation to answer the question, recognise a sales opportunity, book a meeting and record all of it against the contact.
That difference decides the purchase. If your conversations are mostly support from existing customers, a dedicated support tool may fit. If they are a mix of enquiries, booking requests and client questions, a separate tool means a second database, an integration to keep alive and a gap where context gets lost.
What to check before choosing a standalone product
Ask these questions of any conversational AI vendor, Ada included, and write the answers down:
- Where does the conversation history live, and does it reach your CRM record in full or as a summary?
- Can the AI take actions in your systems, such as booking into your calendar, or only answer?
- Which channels does it cover, and are SMS and WhatsApp priced separately?
- Who maintains the integration when either system changes?
- How do you correct a wrong answer, and how quickly does the correction take effect?
If the honest answers involve an integration project and a second place your team has to check, count that cost before you compare prices.
Before you start on the platform
- Your account, with its contacts, calendars and connected channels.
- Your most common customer questions and approved answers, ideally exported from your inbox or ticket history.
- A clear split between questions the AI may answer and ones that must reach a person.
- The calendar or pipeline stage the AI should use when a conversation turns into a sales opportunity.
How to set it up
- Go to AI Agents > Conversation AI and click + Create Bot.
- Select Prompt Based Bot.
- Choose General Q&A, or Start from Scratch for a mixed role.
- Continue into the bot editor.
- Configure Bot Settings.
- Configure Bot Training with your website pages and approved FAQs.
- Configure Bot Goals using prompts, model settings and actions.
- Open the Agents List tab, select the bot, then click Deploy.
- Turn on the channel you need, starting with web chat.
- Click the channel's edit icon if it has extra settings.
- Turn on Assign channel and choose the widget or number.
- Click Update, then click Save for the bot.
What changes when the AI lives inside the CRM
The main gain is context. The agent knows the contact is an existing client, which adviser they work with and what they bought, because that is on the record it is answering from. A standalone tool only knows what the integration passes it.
The second gain is that answers and actions happen in one place. When a client asks to upgrade, the agent can create the opportunity and notify the account lead in the same conversation. Your team picks it up in Conversations with nothing to copy across.
Say the agency handles 400 client messages a month across email, chat and WhatsApp. A standalone support AI would cover chat well, but WhatsApp would need an extra integration and the CRM a nightly sync. Running the agent inside the platform would put all three channels in one inbox, with every answer on the contact record. The message volume is an assumption picked to make the comparison concrete.
The trade-off
Specialist support products often have deeper reporting on deflection and more mature tooling for very large support teams. If you run a contact centre with dozens of agents, that depth may matter more than having everything in one place. For most firms in the size range this site serves, it does not. For a broader comparison of enterprise options, see enterprise conversational AI.
Buyers often judge conversational AI by how human the demo sounds. Judge it by what happens after the answer: is the contact updated, is the meeting booked, does a person see the hand-off? A fluent answer that leaves no trace in your CRM costs you the follow up.
Weekly running time
Plan on an hour a week for the first month and thirty minutes after that. Read conversations the agent handed over, find the questions it could not answer and add them to the knowledge base. If you migrate from another tool, import your existing answer library first rather than writing from scratch.
Troubleshooting
Answers are accurate but too long. Add a line to the instructions limiting replies to three sentences and one question.
The agent answers sales questions with support answers. Your instructions describe only support. Add a short section on recognising buying intent and what to do with it.
Existing clients are treated as new leads. The contact is not being matched. Ask for an email or phone number at the start and check duplicate settings.
Hand-offs go unanswered. Assign them to a team, not one person, and add a notification in a workflow.
Frequently asked questions
Is Ada conversational AI the same as an AI agent on the platform?
They do a similar job: answering customer conversations with AI. The difference is where they sit. An agent on the platform works inside the CRM and calendars, while a standalone product connects to them through an integration.
Can I move my existing answer library across?
Yes. Export your FAQs or help articles and add them to the knowledge base as documents or written entries. Review them first, since old answers are the most common source of wrong replies.
Which is better for a small support team?
A team of a few people usually benefits more from one inbox than from deeper support analytics. For more on that set-up, read AI customer support and conversational AI. If you want to see what a good handled conversation looks like, the AI customer service example walks through three.