TechnologyComplex automationSeveral jobs working together: multi-stage pipelines with state, branching on rules or AI decisions, approvals or escalations, 4+ systems, AI agents that act, or reconciliation across sources. Usually rolled out in stages.

Hardware quotes assembled from live distributor pricing

For: IT equipment reseller and integrator

Typical impact

Quotes out in 6 minutes

Quoting hardware meant price-list archaeology across distributor portals. Now sales picks products in a form; Make pulls live pricing and stock, applies tier margin rules, swaps in alternatives for out-of-stock items, and a branded quote goes out in six minutes.

Typical results

15/day

Quotes per rep, was 4

0

Quotes sent on stale pricing

+9 pts

Quote win rate

Figures reflect typical results for this kind of automation.

The challenge

Building a quote meant logging into three distributor portals, cross-checking prices against last week's spreadsheet exports, guessing at stock, and applying margin rules from memory. The average quote took four hours to leave the building; 18% went out on stale pricing that erased margin, and slow responses handed time-sensitive deals to faster competitors.

Before and after

Before With the automation

Quote turnaround

−98%
240 min
6 min

Quotes per rep per day

3.8×
4 quotes
15 quotes

Quotes on stale pricing

−100%
18%
0%

The automation

We built a quote engine on Make. Sales picks products in a simple form; the workflow queries distributor feeds for current pricing and stock across suppliers, chooses the best source, and applies margin rules by customer tier and deal size. Out-of-stock items are swapped for comparable alternatives and flagged on the quote. A branded PDF with validity dates generates automatically, logs to the CRM, and reaches the customer minutes after the request.

How it works

  1. 01

    Sales picks products

    A simple form captures the customer, their tier and the product selections.

  2. 02

    Live pricing pulled

    Make queries distributor feeds for current price and stock across suppliers, choosing the best source.

  3. 03

    Margin rules applied

    Pricing adjusts automatically by customer tier and deal size — no mental math.

  4. 04

    Out-of-stock handled

    Unavailable items swap in comparable alternatives, clearly flagged on the quote.

  5. 05

    Branded quote sent

    A polished PDF with validity dates goes out and logs itself to the CRM.

Starts from

  • Sales product picks
  • Live distributor feeds

Delivers

  • Tiered margin pricing
  • Branded quote PDF
  • Sent with alternatives

The impact

Quotes now go out in six minutes instead of four hours, and reps produce fifteen a day where four was the ceiling. Stale-price quotes dropped from 18% to zero, protecting margin on every deal, and the win rate climbed nine points as customers started receiving quotes while still on the phone.

Questions about this automation

Where do prices and stock levels come from?

The workflow queries the distributors' price and stock feeds or APIs at the moment the quote is requested. It compares current price and availability across suppliers for each item and picks the best source. Because pricing is pulled live, the quote never relies on last week's spreadsheet export or a guess about stock.

How are margins applied?

Margin rules are set by customer tier and deal size, and the workflow applies them to every line automatically. Sales reps choose the customer and the products, and the pricing follows the rules without mental math. The rules live in one table, so changing a margin takes effect on the very next quote.

What happens when an item is out of stock?

The workflow looks for a comparable alternative in the same feeds and swaps it in, clearly marked so the customer and the rep can see the change. The finished quote is a branded PDF with validity dates, emailed and logged to the CRM. It is a multi-step build, usually rolled out in stages, starting with one distributor feed.

Technology

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