Allma: Product Strategy
As Fractional Head of Product at Allma (AI content generation startup), I implemented a comprehensive strategic framework that moved the Engineering team from reactive feature development to structured, data-driven prioritization.
Context & Challenge
Allma is an AI platform that helps Product Marketing teams generate and manage go-to-market content at scale. Their technology uses LLMs to transform product knowledge (docs, slides, websites) into various marketing assets while maintaining brand voice and accuracy.
  • Early-stage AI startup with a barebone MVP and 50 beta customers
  • Engineering team responding to random user requests
  • No clear prioritization framework or strategic direction
  • Limited resources requiring careful prioritization
Approach
  1. Created comprehensive jobs-to-be-done framework by analyzing sales discovery calls and interviewing beta users (PMMs at tech companies).
  1. Mapped the product marketing pain points to identify highest-value automation opportunities.
  1. Developed prioritization matrix using Impact/Confidence/Effort scoring to evaluate features
Strategic Recommendations
Based on the analysis, I recommended three strategic priorities:
  1. Prioritize Content Quality
  1. Implement configurable templates and persona support
  1. Highest confidence in driving adoption
  1. Clear differentiation from generic AI tools
  1. Expand Knowledge Integration
  1. Critical for reducing time-to-value
  1. Foundation for advanced features
  1. Selective Feature Expansion
  1. Start with high-impact personalization capabilities
  1. Postpone resource-heavy features like slide deck building
  1. Maintain engineering focus on improving core AI generation
What We Decided Not to Do
  • Launch management workflow (despite customer requests)
  • Slide deck builder (too resource-intensive)
  • Asset repository features (low strategic value)
  • Complex approval workflows (outside core value proposition)
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