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Building with AI

She knew AI could help. She just didn't know where to start.

How an executive coach turned her meeting transcripts into three AI-powered workflows: a feedback analyzer, automated summaries, and a client-facing AI assistant.

By Mollie Amkraut Mueller · · 5 min read

Last fall, an executive coach reached out to me. She works with high-profile clients: CEOs of PE/VC firms, startup founders, engineering leaders. She's great at what she does. Her background isn't technical. She's a coach, not an engineer.

She knew AI could help her business. She'd heard the pitches, seen the demos. But she was stuck at the same question I hear from almost everyone: where do I actually start?

A friend had told her to go hire an engineering team on Upwork. Build something custom. But she had a hunch that was overkill. Expensive, and premature. Why commit that kind of money before you even know what to build?

What she actually wanted wasn't an engineer. It was clarity. As she put it: "I didn't know how to evaluate the work. What I actually wanted to know was what I should build."

So she hired me to figure that out with her.

She was already sitting on a goldmine.

This coach had been recording her coaching sessions (with client permission, of course) using an AI meeting note-taker. Every session from the past few months was fully transcribed and saved. She knew there was more she could do with all of it. She just needed help figuring out what to build and how.

If you take one thing from this newsletter, let it be this: if you're recording your meetings, you have raw material for almost everything you need at work. Follow-ups, content, analysis, client support, career development, team management. And if you're not recording yet, start. (I use Granola. Link here.)

That library of transcripts became the foundation for everything we built together.

What we built

We built three things. All from meeting recordings, simple automations, and some careful prompt work. Total cost was a fraction of what an engineering team would have charged.

1. A coaching feedback analyzer

This one was about trust and support. She wanted a way to evaluate her own coaching. Things like her talk-to-listen ratio, the types of questions she was asking, whether she was advising too much versus coaching. The kind of feedback that's hard to get when you work alone.

We started by building a prompt that analyzes her session transcripts against her own coaching framework. She could run it after any session she wanted to review. One click, instant analysis.

Once she trusted the output, we automated it. Now she gets a short coaching report every week. She doesn't have to read every one. But it's always there, running quietly in the background, giving her the kind of honest feedback most leaders never get.

2. Automated session summaries for clients

This was the biggest client experience upgrade. After every coaching session, an automation drafts a warm summary email with the commitments the client made, key insights from the conversation, and a reminder that goes out a week later.

The emails land in her inbox as drafts so she can review before sending. Her clients now get structured follow-up and accountability without her doing any additional work. Before this, follow-ups were inconsistent or nonexistent. Now they're automatic.

If you're in any kind of client-facing work (coaching, consulting, advisory), think about what it would mean if every client got a thoughtful follow-up within hours of your conversation, every single time.

3. A client-facing AI assistant

This was the most ambitious piece. We built a web app where her clients can chat with an AI trained on her coaching methodology and their specific session history. Each client has their own login, their own data. No cross-contamination, which is critical when you're working with sensitive executive conversations.

Now her clients get coaching-style support between sessions. And she has a full admin dashboard to manage everything herself.

The whole project took about six weeks and cost a fraction of what she'd been quoted by engineering teams.

How we approached it

The tools weren't the hard part. Two things mattered much more:

Strategy before building. We started by mapping her business: where her time went, what her clients needed, where AI could actually make a difference versus where it would be a distraction. The first sessions were almost entirely conversation. That's the part most people skip, and it's the part that determines whether anything you build actually sticks.

Quality before automation. Every tool was tested manually first. She refined prompts, reviewed outputs, lived with the results for a while. Only after she trusted the quality did we automate. This matters. If you automate bad output, you just get bad output faster.

Where she is now

She has three AI workflows she owns and can modify without technical help. Her clients are getting a level of follow-up and between-session support that didn't exist before. She has a feedback system that gives her the kind of self-evaluation most coaches only get from a supervisor.

And other coaches in her network have started asking how she did it.

If this sounds like you

Most people I talk to are in the same place this coach was. They know AI could help. They've tried a few things. But they haven't built anything real into their business because they're stuck on the "what" and the "how."

Three ways to get started: