How do you build assisted quoting without losing control?
AI takes the customer's request (an email, a chat message, or a PDF), cleans the data, and fills in the quote; a person reviews it before it goes out. In the portal of an industrial equipment distributor that works this way, a real request became a review-ready quote in 42 seconds, work that used to take days.
The problem is not typing speed
At the distributor in this case, every quote was artisanal work: hunting prices across scattered lists, assembling documents by hand, and chasing technical specs product by product. Commercial data lived in loose files, with no reliable backup and no single source of truth.
That is why assisted quoting starts before the AI: the catalog, supplier price lists, and costing have to live in one system. Without that foundation, a model only speeds up the mess.
How a request flows
The input is whatever the customer already sends: an email, a chat message, or a PDF. AI cleans and fills the data inside the same portal that holds a 368-item enriched catalog with spec sheets, supplier price lists with 8,000+ integrated products and exchange-rate protection, and a costing engine.
The output is a professional quote ready for review. In production, a real request took 42 seconds end to end. The field team gets the same path from their phones, through a Telegram quoting bot that organizes requests in queues.
- A request comes inA customer's email, chat message, or PDF
- The systemAI cleans and fills inCatalog, supplier lists, and costing in the same portal
- A person approvesA person reviewsNothing leaves without that review
- Quote readyA real request: 42 seconds; previously days
Where control stays
The rule is simple: nothing goes out until a person has reviewed it. AI does the preparation; the decision to send is human. A price benchmark against the market feeds pricing decisions, but does not replace them.
Governance is built into the flow: role-based access so each person sees exactly their slice, automated backups, and 900+ automated tests protecting every change. Privacy-sensitive pipelines use local models, and the platform runs on an on-premises, low-power server with a tested disaster-recovery procedure.
- Modules in one role-gated portal
- 50+
- Catalog items with spec sheets
- 368
- Supplier products integrated
- 8,000+
- Automated tests protecting every change
- 900+
What it does not do
It does not send quotes on its own. Nor does it replace the ERP: the finance module mirrors it (sales, expenses, and monthly collections reconciled to the cent).
How long it takes to exist
The first release of this system reached production in six weeks, built by a single specialist directing a fleet of specialized AI agents. Today the portal has 50+ modules, from lead to after-sales, and is operated with the client team. The first step for a similar case is to find where each piece of data a quote needs lives today.