Most quote requests are pattern-based even when they feel custom: the same handful of questions, the same variables, the same rate logic behind the number. An agent can read an incoming request from a form, an email, or a chat widget, pull out the details that actually change the price, check them against your rules, and draft a reply. If the job fits inside your normal range, it can send that draft right away, often faster than a person would get to it between calls and job sites. Speed matters here: a lot of quote requests go to whichever business answers first, not the best one.
The personal touch survives if the reply sounds like you and the system knows when to stop. That means writing the message in your actual voice and phrasing, sending it from the inbox or number your clients already know, and building in limits: a job outside the normal size, a client asking for a discount, a scope that does not match anything in your rate table. Those cases get flagged for a person instead of guessed at. The agent's job is to handle the repeatable part of the work and hand the rest to someone who can make a judgment call, with the context already gathered so the handoff does not start from zero.
This does not fit every quoting process. If every job genuinely needs a site visit or an expert's eye before you can put a number on it (custom fabrication, structural work, anything where the estimate depends on seeing the thing in person) the system can still handle intake and scheduling, but it should not be the one drafting the price. It also needs something to learn from: a rate table, a set of past quotes, rules an estimator already applies without writing them down. If pricing has never been written down anywhere, there is nothing yet to encode, and that is worth fixing first. A good starting check: look at how many quote requests currently sit unanswered for more than a few hours, and how many of those turn cold. That tells you whether speed or judgment is the actual bottleneck.