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I Built a Value Pricing Bot to Help Me Sell a $15K Assessment

I recently built a custom GPT to help me think through value pricing on a real client opportunity.

Not because I wanted AI to give me a number. Because sometimes the hardest part of pricing is not convincing the client. It is convincing yourself.

I have written before about value pricing by committee. The idea is that pricing gets better when you stop doing it alone and bring in other perspectives to challenge your assumptions, test your framing, and help shape the offer. This time, I tried a different version of that same idea. Instead of bringing in a room full of people, I built a custom GPT to serve as a value-pricing sounding board.

That is especially useful when the work does not fit neatly into a standard box. You know there is real value on the table. You know the client’s problem is meaningful. But translating that into a fee you can confidently say out loud is where many firm owners, myself included, hesitate.

So I built a custom GPT to act like a value pricing expert.

I created the instructions using ideas from Ron Baker, Alex Hormozi, and my own previous writing and thinking around CAS pricing. Then I fed it the details of a current client situation and used it to help me pressure-test the offer.

What I used it for

The client reached out looking for help reviewing tax returns and financials, with a specific focus on margin compression, compensation structure, and profitability. On the surface, that can sound like analysis work.

But the bot helped sharpen the real issue.

This was not just a request to review numbers. It was a business owner asking why strong revenue was no longer translating into healthy profit, and what to do about it. The GPT kept pulling me back to the same point: do not sell financial analysis; sell clarity on a margin problem that is already costing the client over six figures.

That shift matters. Because once you frame the problem correctly, the fee starts to make more sense.

Where the bot helped most

The most useful part was not that it generated pricing ideas. It was what helped me bounce around the logic behind the pricing.

It helped me think through questions like:

What is the real business problem here?
What is the likely upside if the client fixes it?
Is the client buying information, or are they buying better decisions?
Where does implementation support create more value than analysis alone?
How should the options be framed so that the pricing feels logical rather than arbitrary?

That is the kind of thinking a lot of firm owners already have in their heads. The difference here was having a tool that could push back, reorganize the problem, and help me see the offer more clearly.

In other words, it helped me build conviction.

What the pricing conversation turned into

One of the strongest ideas from the bot was to clearly separate two kinds of value.

The first was a standalone diagnostic or assessment. That meant clarity: identifying what was driving margin compression, evaluating compensation and cost structure, and giving the client a clearer view of what healthy profitability should look like.

The second was the larger implementation-oriented engagement, where the value comes from staying involved long enough to help the changes stick. That included the logic of working through reporting changes, helping the bookkeeper, monitoring results, and making adjustments over time.

In this case, the client selected the standalone assessment for $15K.

And honestly, that is part of why I think this is a useful AI pricing story. The win was not that the bot talked the client into a bigger package. The win was that it helped me land on an assessment fee I believed in and could explain with confidence.

Why this matters for CAS firm leaders

A lot of pricing advice focuses on tactics for handling objections or getting the client to say yes.

That matters, but there is a step before that. You need enough confidence in the value to hold the fee in the first place.

That is where I think a custom pricing bot can be surprisingly helpful. Not as a replacement for judgment, but as a sounding board for it.

It can help you pressure-test weak framing. It can help you separate deliverables from outcomes. It can help you see whether you are underpricing because the work is low-value or because you have not yet fully articulated its value.

That is a much better use of AI than asking it to spit out a random proposal fee.

The next use case

What makes this even more interesting is that the story is not over.

The client chose the standalone assessment, which means the next challenge is using that work to tee up the larger recurring opportunity later.

That is where I expect this value-pricing bot to be useful again, not just for setting the initial fee, but for helping think through how to resell the client into ongoing advisory support once the assessment creates clarity.

That is a more realistic picture of CAS pricing anyway.

Sometimes the first engagement is not the full win. Sometimes the first engagement earns the right to the next one.

Final takeaway

The biggest benefit of this custom GPT was not automation.

It was confidence.

It helped me think more clearly about the problem, the value, the offer's structure, and the fee I wanted to present to the client.

And in this case, that process helped turn a vague pricing decision into a standalone $15K assessment I could defend.

That is why I think the real opportunity with AI in pricing is not replacing judgment. It is helping you strengthen it.

Thanks for reading, Luke Templin!