Live communication and event production
satis&fy: AI-assisted quotation from weeks to under 2 minutes
Complex quotation work was translated into a structured AI flow that prepares a high-quality draft quickly while keeping commercial approval with the team. The system does not replace judgment; it reduces the repeated assembly work that slows down experienced teams.
Operating flow
Before
Offer preparation depends on scattered scope, assumptions, modules, and experience.
AI preparation
The assistant guides intake and assembles a structured first draft.
Human control
Commercial review checks assumptions, pricing, wording, and client readiness.
Useful result
Teams spend less effort assembling repeatable material and more time on judgment.
Operating challenge
Large event and production offers combine scope, equipment, logistics, timing, assumptions, supplier input, and risk buffers. Preparation could stretch across weeks when information arrived in fragments, because every offer required the team to reconstruct context, dependencies, and open questions.
System shape
Quotation assistant with guided intake, draft generation, assumption log, review checklist, and human approval path. The assistant asks for missing inputs, proposes reusable modules, and keeps assumptions visible before anything becomes client-facing.
What changed
Converted historic offer structure, assumptions, required fields, and approval rules into a reusable intake and drafting workflow that guides the team before a draft is generated.
Designed AI drafting for scope text, missing-information questions, package logic, risk notes, and internal review comments, so the first version already contains the structure a reviewer needs.
Kept final pricing, contractual wording, and client communication under human review.
Estimated value
Estimated value: a structured offer draft can be prepared in under 2 minutes when required inputs are available; final approval stays with the responsible team. The largest gain comes from turning scattered knowledge into a repeatable preparation flow.
- Potential saving: days of repeated assembly work when scope, modules, and assumptions are reusable.
- Quality effect: more complete first drafts, clearer missing-information questions, and less dependence on individual memory.
These estimates describe plausible operating value for the described pattern. They are not a universal promise and should be validated against volume, data quality, adoption, and decision rights.
Review and safeguards
- No automatic client-facing offer without commercial review.
- Assumptions and missing inputs are shown explicitly before approval.
Useful questions before implementing a similar system
- Which decision may AI prepare, and which decision must stay with a person?
- Which data sources are reliable enough for the first production workflow?
- Where does the process need evidence, escalation, and auditability before speed?