Dynamic Capacity-Based Pricing Engine

Overview

Most agencies price services off a rate card that never changes — the same hourly rate whether the team is slammed or sitting idle. This engine replaces that with a pricing model that moves with actual capacity: when utilization is high, quoted rates rise; when there’s open capacity, rates flex down to win the next deal instead of leaving a team bench idle.

The problem

Agencies leave money on the table in both directions. They underprice when they’re at full capacity and should be charging a premium for the next slot, and they overprice when they have idle capacity and lose winnable deals to competitors. Both failures come from the same root cause: pricing decisions are disconnected from real-time capacity data.

What it does

• Pulls live utilization data from project management and time-tracking tools (capacity by role, by week)
• Calculates a capacity index per service line and feeds it into a pricing function alongside historicwin-rate data
• Outputs a recommended rate range for new quotes, refreshed daily
• Flags when a quote falls outside the recommended range before it goes out, so sales doesn’t discount blind

Stack

Python service for the pricing model and capacity calculations, containerized with Docker for consistent deployment. n8n handles the data pulls from PM/time-tracking tools and pushes the computed rate ranges into the CRM via webhook, so reps see the number where they already work instead of a separate dashboard.

Why it matters

This turns pricing from a once-a-year rate-card exercise into a live input on every quote — the same logic airlines and hotels use for yield management, applied to service delivery capacity instead of seats or rooms.