Ep 20: Using AI to Fine-Tune Your Design Services and Pricing Strategy

Written By Jenna Gaidusek

Listen to the Podcast Episode for a deeper dive

Using AI to Fine-Tune Your Design Services and Pricing Strategy | AI for Interior Designers™
AI for Interior Designers™ Podcast

Using AI to Fine-Tune Your Design Services and Pricing Strategy

Jenna runs the same service packaging prompt through four different AI models — ChatGPT, Google Gemini, Microsoft Copilot, and Perplexity — and compares the results. The outputs are different in every useful way, and the comparison reveals exactly how to use AI as a pricing strategy tool.

Ep. 20 Solo Episode ✦ Jenna Gaidusek July 2024
This blog was written using AI as a recap from the recording, then edited by the author for accuracy and details.
Key Takeaways
  • Different AI models produce meaningfully different pricing and packaging suggestions from the same prompt — comparing outputs across models gives a broader range of options to evaluate than using a single tool.
  • ChatGPT and Perplexity aligned on similar mid-to-high pricing tiers; Google Gemini went more budget-conscious; Microsoft Copilot came in lowest across all tiers. The variation reflects different training emphases and design philosophy assumptions baked into each model.
  • The quality of AI pricing output depends entirely on the quality of the input. Providing specific information about your current services, rates, and deliverables produces more useful suggestions than a generic request.
  • AI-generated pricing is a starting point, not a final answer. The outputs need to be evaluated against your specific market, your ideal client profile, and your actual cost structure — which only you know.
  • This exercise is a practical demonstration of AI as a business strategy tool — not just a creative or administrative tool. LLMs can help designers think through service packaging, market positioning, and pricing structure in ways that used to require a business coach or consultant.

The Experiment — One Prompt, Four Models

The setup is simple and replicable: Jenna took a detailed, specific prompt describing her current virtual design services and hourly rate, and ran it through four different AI models simultaneously. The goal was to see what each model would suggest for three new tiered service packages — and how differently they would approach the same problem.

This is important methodology: the same prompt, run through different models, produces meaningfully different outputs. That variation is not noise — it is signal about the different assumptions, training emphases, and market perspectives each model brings to the question. Comparing across models gives you more raw material to evaluate than any single model can provide.

The Prompt — Run Through All Four Models
"I want to create three new virtual interior design services. I currently offer design boards for concepts with furniture linked to purchase, optional renderings and VR walkthrough tours, and virtual consultations. I charge $150 an hour. Create three different tiers of services, list out deliverables, and price them for me."

The key to this prompt working well: it includes specific information about current offerings and the existing hourly rate. Giving the AI context about where you are starting from produces outputs calibrated to your actual practice rather than generic pricing suggestions that assume nothing about your current positioning.

What Each Model Suggested — The Full Comparison

ChatGPT Plus Mid-to-high range · Complete deliverable sets
Basic Design Package
1-hour virtual consultation · design board · curated color scheme · 2 revisions
$500
Standard Design Package
90-min consultation · design board · color palette · material selection · floor plan · 3D renderings · 3 revisions
$1,200
Premium Design Package
2-hour consultation · design board · color palette · material selection · floor plan · 3D renderings · VR walkthrough · 4 revisions · virtual assistance
$2,500
Google Gemini Budget-conscious · Accessible entry point
Design Essentials
Design board with shopping links · 1 round of revisions
$350
Design Enhancement
Design board · 2 revisions · detailed color palette · material board · basic floor plan · optional 1-hour consultation
$650
Design Immersion
Design board · unlimited revisions · detailed color palette · floor plan · high-quality renderings · VR walkthrough · 2 consultations
$1,200
Microsoft Copilot Lowest pricing overall · Lean deliverable sets
Basic Design Package
1-hour consultation · design board · 1 revision
$300
Standard Design Package
1-hour consultation · design board · 3D renderings · 2 revisions · shopping list
$750
Premium Design Package
1-hour consultation · design board · 3D renderings · VR walkthrough · 3 revisions · follow-up consultation
$1,500
Perplexity Comprehensive deliverables · Aligned with ChatGPT on pricing
Basic Design Package
1-hour meeting · design concept · 1 round of revisions · implementation guide
$500
Standard Design Package
Design concept · renderings · design board · virtual support · implementation guide · 2 rounds of revisions
$1,200
Premium Design Package
1-hour consultation · design concept · 3D renderings · VR walkthrough · unlimited revisions · implementation guide · virtual support · exclusive vendor discounts
$2,500

What the Comparison Reveals — Reading the Outputs

The four models did not produce four versions of the same answer. They produced four meaningfully different perspectives on how to package and price virtual design services — each with its own market positioning assumptions, deliverable philosophies, and price-to-value calibrations.

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ChatGPT and Perplexity converged on similar pricing. Both suggested $500 / $1,200 / $2,500 tiers, which suggests this price range reflects a reasonable professional market consensus at the $150/hour starting point. Convergence across independent models on the same numbers is a useful signal.
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Gemini positioned for a more accessible market. The $350 / $650 / $1,200 range targets a different client segment — designers who serve budget-conscious clients or are entering a competitive market may find Gemini's structure more relevant to their positioning.
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Copilot came in significantly lower overall. The $300 / $750 / $1,500 structure suggests either a more conservative market assumption or a different philosophy about what these packages should include. Worth understanding as a data point but likely underprice for a designer charging $150/hour.
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Perplexity uniquely included vendor discounts in the premium tier. The addition of "exclusive discounts to partnered vendors" as a premium deliverable is a packaging idea that neither ChatGPT nor Gemini surfaced — a good example of how different models generate different ideas worth considering.
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Revision limits vary significantly and matter. Unlimited revisions in Gemini's premium vs. 4 revisions in ChatGPT's premium is a major scope difference that would dramatically affect profitability. This is exactly the kind of deliverable detail that requires designer judgment to evaluate rather than accepting any model's suggestion wholesale.

"The key is to provide comprehensive information about your services to receive the most accurate and helpful responses. Each AI model provided unique insights, underscoring the importance of experimenting with different tools."

— Jenna Gaidusek
Frequently Asked Questions
Use them as a starting framework and reality-check them against your specific situation. The process: take the AI suggestions and evaluate each deliverable against your actual cost — how long does it take you to produce 3D renderings? What does the VR walkthrough software cost? How many revision rounds can you absorb before a package becomes unprofitable at the stated price? Then evaluate the price points against your specific market — what are comparable designers in your market charging? What is your ideal client willing and able to pay? The AI outputs give you a range of structured options to react to; your business knowledge and market understanding determine which elements are appropriate for your practice.
Different AI models are trained on different data sets with different emphases, and they make different assumptions about market context when no specific market information is provided. ChatGPT and Perplexity may have more exposure to pricing data from higher-end design markets or professional service contexts; Copilot may reflect more conservative general market assumptions. None of them knows your specific market, your specific client base, or your specific cost structure — so they are all making assumptions. The variation is not error; it is the natural result of different models filling the same information gaps differently. Providing more specific context (your city, your client demographic, what comparable designers charge in your market) would narrow the variation and produce more targeted outputs.
Generally no — and this is a good example of why AI pricing suggestions require professional evaluation before adoption. Unlimited revisions sounds like a compelling premium offer, but in practice it creates an unbounded time commitment that can make even a high-priced package unprofitable. Experienced designers consistently recommend defined revision rounds (typically 2–4 depending on scope) with clearly stated scope for what constitutes a revision versus a change order. This protects your time, trains clients to make thoughtful decisions rather than treating revisions as unlimited iteration, and makes project scoping and profitability predictable. "Unlimited revisions" is a marketing language choice that creates operational problems — the AI models that include it are optimizing for client appeal rather than business sustainability.
Yes — and you should. The methodology Jenna demonstrates works for any service packaging question: describe your current offerings in detail, include your current rate, specify the context (in-person residential, commercial, hybrid, etc.), and ask for tiered package suggestions. For in-person services, you would also want to include information about your geographic market, travel radius, and any overhead costs that affect your pricing structure. Running it through multiple models and comparing is always more useful than running it through one — the variation between models surfaces more options and different packaging philosophies worth considering.
Several are useful: "Explain the reasoning behind the price points you chose and what market assumptions you are making" — this surfaces the logic you can then evaluate. "What deliverables in the premium tier are most likely to justify the price increase over the standard tier from a client's perspective?" — this helps prioritize what actually drives upgrade decisions. "What are the most common objections clients have to these price points and how would you address them?" — useful for sales preparation. "How would you adjust these packages for a designer specifically targeting [luxury / budget-conscious / millennial first-time homeowner] clients?" — useful for market-specific positioning. The initial package suggestions are a starting point; the follow-up conversation is where the strategic value deepens.
 
 

Disclaimer: This blog was written using AI as a recap from the recording then edited by the author for accuracy and details.

 
 
Jenna Gaidusek
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Ep 19: How to talk to AI models