AI makes quotation work faster when it shortens the distance between a qualified request and a useful first draft. It should not guess a price from a conversation or send a financial commitment on its own. In Lavenity's design, Ven drafts from the service template and the facts already collected, while a named person approves the quotation before it goes anywhere. The speed comes from removing preparation work, not removing accountability.
Start the draft from structured request evidence
The draft begins with structured context. Customer, service address, requested service, equipment, description, attachments, and qualification answers already belong to the service request. AI can map that evidence to an approved quotation template and identify the relevant sections instead of asking an estimator to reread the full conversation. Facts it cannot support remain missing; they do not become confident assumptions.
Grounding in the quotation library is the second boundary. AI should choose only from approved product, labour, condition, and quote templates, preserving their identifiers and current values. It may propose quantities, relevant options, or a customer-facing explanation when the request supports them. It should flag an unusual item for review rather than inventing a product, rate, or term that the business has never approved.
Ground every suggestion in approved quotation data
Calculation must stay deterministic. The quotation engine—not the language model—applies unit prices, costs, markups, discounts, VAT, fixed totals, and deductions. That separation is what makes AI useful at commercial speed: the model handles unstructured language and matching, while the calculator produces the number the business can reproduce and audit. Regenerating wording cannot silently change arithmetic.
A good draft also shows what is missing. Low-confidence service matching, an unknown quantity, contradictory customer details, or a line with no current price should block readiness or create an explicit review item. AI can ask one approved follow-up question or prepare the rest of the quote while that fact is resolved. A visible gap is faster than a polished draft that returns from approval with a hidden mistake.
Keep arithmetic deterministic and approval human
Human approval should focus on exceptions, not reconstruction. The reviewer sees the source facts, proposed template, changed lines, commercial settings, customer preview, and final server-calculated total. They can accept, edit, or reject the draft, and their correction becomes an audit signal. Nothing financially binding is sent autonomously; delivery remains a separate, explicit action after approval.
Expand AI only when corrections stay low
Roll out the workflow in stages. First observe what AI would select, then let it create drafts that always require approval, and widen the allowed templates only after correction rates are stable. Track time to first draft, time in review, missing-field rate, edited lines, rejected drafts, and quotes prepared outside the system. AI earns more scope when those measures show reliable preparation—not when the prose merely looks finished.
Pilot AI drafting on one approved quotation template
Pick a service with a stable template and enough completed requests to recognise the normal variations. Define which request fields may select the template, which catalogue items AI may propose, which quantities require human input, and which missing facts must block readiness. Do not begin with every service at once.
Run the pilot in draft-only mode. Compare the AI draft with the source request and require a named reviewer to approve every line and the customer preview. Keep the quotation engine responsible for all arithmetic and keep delivery outside the AI action, so a plausible sentence cannot become an unreviewed price commitment.
Review time to first draft, review time, edited lines, missing fields, rejected drafts, and the reasons for correction each week. Expand the allowlist only when the same template remains reliable across real exceptions. The useful signal is less reconstruction by reviewers, not more generated text.
Frequently asked question
Can an AI quotation tool send prices without human approval?
It should not. A safe AI quotation workflow uses structured request facts and approved templates to prepare a draft, while a deterministic engine calculates every amount and a named person approves the result. Missing facts, unusual items, and low-confidence matches remain visible review points. Sending the financially binding quotation is a separate explicit action after approval.