SLIDE 1 TITLE Acmecorp Sales pipeline coaching for B2B SaaS teams SLIDE 2 PROBLEM B2B SaaS AEs spend hours each week building QBR slides by hand - [SPECIFICS NEEDED: name the segment, the task, and the cost - e.g. "30 AEs surveyed at 6 Series B SaaS firms; median 9.5 hrs/week on QBR prep; ~$2.4M/yr of AE time for a 50-rep team." Replace with your real customer-interview data.] SLIDE 3 SOLUTION We watch sales calls, extract pipeline signals, and auto-generate the QBR slides AEs use - Recording integration (Gong / Chorus / Zoom), extraction of deal stage + competitor mentions + win/loss reasons, output as an editable per-account brief refreshed weekly - [SPECIFICS NEEDED: the quantified outcome - hours saved per AE per week] SLIDE 4 MARKET - [SPECIFICS NEEDED: this slide showed only a "50 Billion" TAM with no segmentation or source. Give TAM / SAM / SOM with the math and a cited source, e.g. TAM $4.2B sales-enablement (Gartner 2026), SAM $1.1B (B2B SaaS with 20+ AEs), SOM $180M (20-200 AEs, US/UK/CA).] --- **Changes:** - Slide 1: replaced the "Disrupting the Future of Sales" tagline with one sentence saying what the company does and for whom - Slide 2: replaced 4 generic bullets with one specific claim and flagged it for the sample size, method, and cost only the founder has - Slide 3: replaced "AI-powered insights / seamless integration / world-class UX / end-to-end automation" with the actual mechanism (record -> extract -> generate); flagged the outcome metric - Slide 4: flagged the unsourced "50 Billion" TAM for TAM/SAM/SOM segmentation with math and a source - Cut "disrupting," "world-class," "underserved," "ripe for disruption" throughout **Defensibility verdict:** - Would a skeptical investor find a specific claim to question on each slide? Partial - the structure now invites specific questions, but the numbers are flagged placeholders until the founder supplies real customer and market data - Which slides still read as filler: none in shape; slides 2-4 need the founder's actual figures in the flags **Word count before -> after:** 92 -> 150 (pitch text grows because vague claims compress poorly; specific-and-longer beats vague-and-short here) **AI/template signal estimate before -> after:** high -> low