How a CEO Coach Saved 30 Min Per Client Each Week
An executive coach for PE and VC CEOs offloaded session notes, summaries, and follow-ups to a no-code AI stack — saving 30+ minutes per client every week.
30+ min/client/week
Saved per client each week
The Challenge
Rachel runs an executive coaching practice serving CEOs of PE and VC firms, with four subcontracted coaches. Her processes were entirely manual: session notes had to be written up by hand, coordination with coaches happened only quarterly, and the summaries she sent clients were rarely read. She wanted to differentiate her practice in a crowded market — but had no idea how to manage technical work or even determine what to build before spending money building it.
What They Built
Mollie started with prompts, not pipelines. Before touching any automation, she ran prompts manually against Rachel's real session transcripts to test whether the outputs were actually useful. Only after Rachel confirmed the quality did they wire up the automation. The result was three interconnected tools: a Coaching Feedback Analyzer that scores sessions against ICF competencies like talk-listen ratio and open-ended question rate; automated post-session email summaries with action items and a one-week follow-up reminder; and 'Rachel Bot,' an AI assistant trained on Rachel's methodology for between-session client support. The entire system runs on Granola, Zapier, Relay.app, and custom prompts — no code written, no engineers hired.
Mollie Amkraut Mueller began with prompts rather than pipelines — a deliberate sequencing choice. Before wiring any automation, she ran every prompt manually against Rachel's real session transcripts to confirm the outputs were actually useful. Only after Rachel validated the quality did they build the automation layer. This prevented the most common failure mode: automating a mediocre process at scale. The resulting system connects three tools. The Coaching Feedback Analyzer ingests session transcripts from Granola and scores each conversation against ICF competencies — tracking talk-listen ratio, open-ended question rate, and other coaching quality markers. A Zapier-and-Relay.app pipeline generates post-session email summaries with action items and schedules a one-week follow-up reminder automatically. Rachel Bot, trained on Rachel's proprietary methodology, provides between-session client support without requiring coach involvement. No code was written. No engineers were hired. The full build took under three weeks.
AI Role
The AI performs three distinct roles: it analyzes session transcripts to score coaching performance against ICF competencies (talk-listen ratio, open-ended question rate); it generates post-session email summaries with action items and one-week follow-up reminders automatically; and 'Rachel Bot' provides between-session client support by responding to client queries using Rachel's coaching methodology as its knowledge base.
Infrastructure
Granola (session transcription) • Zapier (workflow automation) • Relay.app (pipeline orchestration) • Google Drive (document storage)
Integration Points
Granola → Zapier (transcript handoff) • Zapier + Relay.app → email automation and follow-up scheduling • Google Drive → Rachel Bot knowledge base
Impact
Time saved per client weekly once the full automation pipeline scales across Rachel's practice
Rachel adjusted her coaching approach in her very next session after seeing AI-generated feedback that she was 'giving too much advice'
Entire system built with off-the-shelf tools and custom prompts — no custom code, no dev shop, no ongoing development dependency
Implementation Complexity
Best Fit For
Founders and operators of small professional services practices — coaches, consultants, advisors — who know their workflows feel outdated but assume fixing them requires technical expertise or a developer. Especially relevant if you're already delivering good work but spending too much time on follow-up, documentation, and coordination that AI could handle.
