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Before launching an AI support bot, decide what it must NOT invent.
A useful acceptance test is not just whether it answers your FAQ. Try these five situations with synthetic customer questions:
Missing price: ask for a service absent from the approved price list. The bot should ask for clarification or refer to a person, not make up a quote.
Conflicting information: give the test knowledge base two different cancellation rules. Define which source is authoritative and when the bot must escalate.
Unapproved promise: ask for a guaranteed delivery date or a special discount. The bot must stay within the permissions you actually granted.
Human handoff: ask to speak to a manager. Check that the manager receives the question and relevant context, not just an empty notification.
Unavailable system: simulate a failed booking or CRM connection. The bot must not say an action succeeded unless the connected system confirms it.
Write the expected response before running each test. Keep a simple pass/fail log and retest after changing the knowledge base or integrations. These tests reduce avoidable mistakes; they do not prove a bot is error-free.
ROVELK builds AI assistants and business automations around agreed sources, actions and handoff rules.
Planning a bot? Message me with your channel, one repetitive customer question and the action you want to automate. Please leave out customer personal data. We can discuss a focused first scope; price and scope are agreed before work. Project discussions: Russian / Ukrainian.
the unavailable system test is the one everyone skips. a bot saying booked while the crm is down is the worst kind of wrong
Exactly. I'd make the customer-facing states explicit: request received, confirmation pending, booking confirmed. If the connected system cannot confirm it, the bot should say that and route the pending request for a human check—not invent success. That distinction belongs in the acceptance criteria, not only in the prompt.
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My AI assistant runs all day. The expensive model only does the thinking.
Most requests need no AI at all. "Call me in 30 minutes" is handled by plain code. Reminders, schedules and limits are code too.
Short tasks like sorting and scoring go to fast models on a free tier.
The big model only gets planning and long conversations, where it actually has to think.
If it hits a limit, a backup model answers instead of the app going silent.
If your AI app's bill keeps growing, check what really needs the big model.
What does your app send to the most expensive model?
Same pattern holds in outbound tooling. Filtering, dedupe and enrichment lookups are plain code. The model only writes the first line once the row is already verified. Cuts cost and the weird outputs.
Most agencies think AI SEO is just about adding Schema markup.
They are missing the main part: How AI actually connects brands to topics.
Here is how ChatGPT and Perplexity decide which business to recommend:
Brand Topic Connection: AI builds a mental map. If your brand isn't constantly mentioned next to your core service online, AI won't trust you.
Entity Sources: AI checks structured sources like Wikidata, Crunchbase, and niche directories to verify if your business is real and authoritative.
Co-mentions: If your brand is mentioned alongside top industry leaders across blogs and forums, AI treats you as a top choice.
Traditional SEO helps you rank on Google.
Entity optimization helps you get recommended by AI.
Want my AI Brand Visibility Checklist to see if AI trusts your brand?
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Agree on the topic connection part. We saw the same with B2B lists: a brand gets named when third party pages describe it consistently, not when the markup is perfect.
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