The hard part is not the model. It is everything it has to talk to.

AI integration connects an AI capability to the systems a business already runs, so it can read the customer record, write back what it learned, and hand off to a person. Buying an AI tool is not integration. Without the connecting work the tool sits beside the business and someone re-enters the information manually.

A tool gives you capability. Integration gives you capability that knows who the customer is, updates the record other people rely on, and behaves predictably when something fails. Most of the disappointment with AI purchases traces back to the connecting work never being scoped.

Usually not. The starting assumption is that the systems your team already knows stay where they are. Quiet Coyote checks what each vendor supports through APIs, webhooks, exports, and permissions, then works within that. Replacement is only worth discussing when a system genuinely blocks a reliable workflow.

It depends almost entirely on how many systems are involved and what condition the data is in, which is why an honest estimate follows a look at the actual records rather than preceding it. A single connected path between two systems is a much smaller project than a rebuild of how information moves through the business, and starting with the former is usually the right call.

It should fail loudly. That means retries for transient problems, logging that a person can read, an alert to a named owner, and a defined fallback so work continues manually rather than disappearing. Silent failure is the most expensive outcome and it is entirely preventable at build time.

A single well-understood path between two systems is typically a matter of weeks. The timeline stretches when the process being automated was never documented, or when the underlying records turn out to be inconsistent. Both are common, and both are better discovered during scoping than during the build.