Stop Manually Validating Product Models — Let AI and Power Automate Do It for You

You wouldn’t manually test every constraint in your model—so why validate module variants against specs by hand? In CPQ modeling, especially with tools like Tacton CPQ, verifying that your module structure covers all specification variants is essential. Yet many teams still do it manually—comparing PDFs or Excel lists line-by-line to ensure nothing was forgotten. That kind of validation shouldn’t take hours. Now it doesn’t have to. Using GPT, Power Automate, and Office Scripts, you can automatically validate that every variant in your specification is accounted for in your CPQ model. Here’s how.

Why It’s a Problem in CPQ Modeling

When modeling a configurable product in Tacton CPQ, your end goal is a clean structure of modules and module variants, fully governed by smart constraints. But how do you make sure your model includes all the real-world variants your customer needs?

You often start with:

• Product specifications from engineering or product management.

• Spreadsheets or PDFs listing equipment models or variant ranges.

Then, during modeling, you need to ensure:

• Every variant in the spec is mapped to a module or module variant.

• Nothing was left out, misnamed, or missed due to suffix confusion.

• Your constraints properly support the allowed combinations.

Manually comparing these two sources is painful—and prone to error.

Step 1: Creating a Validation Endpoint in Power Automate

Using Power Automate, you can expose a simple HTTP endpoint that takes in:

• A raw list of spec model names (like: “SK75, 311–313E/F”)

• A detailed list extracted from your CPQ module structure (like: “SK75SR-7, 311F, 312E”)

Steps to set it up:

1. Start a new flow with "When an HTTP request is received".

2. Use a Compose action to handle the input JSON.

3. Pass the data to OpenAI using an HTTP connector with a prompt tailored for model comparison.

4. Return the result back to the requester—a JSON with missing variants and a short summary.

This gives you a plug-and-play validator for any spec-vs-model list. We can show you how.

Step 2: Crafting the Perfect Prompt for GPT

Here’s where GPT does the heavy lifting: intelligently comparing the raw spec list with your CPQ module variant list, accounting for suffixes, ranges, and formatting quirks.

Key things the prompt should handle:

• Normalize casing and whitespace.

• Split values on commas, semicolons, or line breaks.

• Expand ranges like 311–313E/F to cover all numbers and accepted suffixes.

• Ignore suffixes in the detailed list (LC, CR, -7, F, etc.) unless critical to identification.

• Match if the prefix and numeric part of a model are found, regardless of suffix.

For example:

SK75 matches SK75SR-7 ✅
311 matches 311F ✅
SK28 does NOT match SK75SR-7 ❌

The GPT output looks like:

{
  "missing": "SK26;SK28",
  "comment": "2 models from the raw list are missing in the detailed list."
}

This is easy to log, audit, or visualize downstream.

Step 3: Mass Validation with Office Scripts in Excel

Once your endpoint works, use Office Scripts in Excel Online to apply it at scale:

• Each row contains a raw and detailed list.

• The script loops through rows, sends data to Power Automate, and writes the result back.

• Perfect for spec-vs-module audits.

This works beautifully for:

• Initial modeling of module variants from legacy specs.

• Continuous validation as specs evolve.

• QA checkpoints before deployment.

Best part? You don’t need to open Tacton to validate. This process works from Excel, Teams, or your SharePoint site. But of course this can also be used directly connected Product modeling as well, it's just a matter of preferred tools for efficiency.

Why It Matters

Every module in Tacton CPQ must be relevant. If your model lacks valid variants, users won’t find what they need—or worse, they’ll configure invalid combinations. And that’s not a constraint problem, it’s a missing variant problem.

Automating this validation helps you:

• Reduce risk of incomplete models.

• Catch mistakes early before customer delivery.

• Save time compared to manual checking.

Especially if you're managing hundreds of variants (think Swift Lifts or HMF), this workflow turns something frustrating into something automatic.

If you're in the middle of a CPQ implementation—or revisiting your existing model—you owe it to yourself to validate your module structure against real specs. This method doesn’t require a new tool or a new system. It uses what you already have: Excel, Power Automate, and GPT.

We’ve tested this in real-world scenarios and it works—fast, accurate, and easy to adapt. You don’t need to build a full internal tool; you just need a smart API, a strong prompt, and a connection to your existing spreadsheets.

👉 Book a virtual coffee with Magnus or Patrik and validate your own model