Quote-to-cash: a commercial operating system for an industrial equipment distributor

The business sold well, but every quote was artisanal work: hunting prices across scattered lists, assembling documents by hand, chasing technical specs product by product. Commercial data — clients, pipeline, supplier prices, field expenses — lived in loose files with no reliable backup and no single source of truth, and no one could see the whole business at once.

The answer was a quote-to-cash operating system covering the entire commercial cycle — lead, quote, order, service, after-sales — in one portal, where AI executes real work under human control. The quoting pipeline takes an end customer's email, chat message, or PDF and returns a professional quote ready to send: AI cleanup and auto-fill, human review before anything leaves. In production it turned a real request into a review-ready quote in 42 seconds — work that used to take days. A Telegram quoting bot with request queues gives the field team the same path from their phones.

Around it, a portal of 50+ modules: CRM and sales pipeline, a 368-item enriched catalog with AI-generated spec sheets and 190 AI-enhanced product photos, supplier price lists with 8,000+ integrated products and exchange-rate protection, a costing engine, a price benchmark against the market that feeds pricing decisions, field-service planning, a sales-resources hub, and a value map of what each service delivers to each customer.

Since mid-2026 the system added a finance module that mirrors the client's ERP — sales, expenses, and monthly collections reconciled to the cent — an internal gallery of 95 ad creatives, and a RevOps report that measured the team's inbound capacity at roughly 1,061–1,451 messages per week. Paid media is operated through the Meta Ads API, with a human approving every publication.

Governance was built in, not bolted on: role-based access so each person sees exactly their slice, automated backups, and 900+ automated tests protecting every change. The platform was migrated to an on-premises, low-power server with a tested disaster-recovery procedure. The first release reached production in six weeks, built by a single specialist directing a fleet of specialized AI agents.