The Indie Founder Research-to-Product Spec Playbook
Go from scattered research to a shippable product spec in one afternoon
Designed for solo founders who drown in video recordings, podcast notes, and document dumps before they can write a single spec. This playbook converts multiformat research into queryable knowledge, then extracts it into structured specs your AI coding tools can act on immediately. It eliminates the translation gap between what you learn and what you build.
Goal
Convert raw research assets into actionable, structured product specs without a PM
Who this is for
Technical indie founders who do heavy research before building but struggle to formalise it into specs
When to use
When you have hours of interviews, videos, or notes you need to turn into buildable specifications quickly
When NOT to use
If your specs already come from a structured discovery process or you have a dedicated PM on the team
How to set it up
Capture all research inputs
Run user interviews through Fathom AI to get auto-transcribed notes. Collect all existing videos, podcasts, and documents into a single folder ready for ingestion.
Build queryable knowledge packs
Upload your transcripts, recordings, and files to the knowledge pack builder. Tag each source by theme (e.g. onboarding pain, pricing confusion) so you can query by topic later.
Extract structured spec components
Query your knowledge packs with prompts like 'what are the top 3 user pain points around onboarding?' then pipe the conversation into LoreSpec to extract structured requirements, user stories, and acceptance criteria automatically.
Assemble the spec in Notion
Paste your LoreSpec output into a Notion page and use Notion AI to fill gaps, rewrite for clarity, and add a prioritisation matrix. Structure it as: problem statement, user stories, acceptance criteria, out-of-scope.
Validate spec against code as you build
After your first sprint, point Specsight at your codebase to generate a living spec comparison. Review any gaps and update your Notion spec to keep research intent aligned with what is actually shipped.
Create queryable knowledge packs from videos, podcasts, and files
Turns your video interviews, podcast recordings, and uploaded docs into a queryable knowledge pack so no insight gets buried in a file you never reopen.
Extract structured knowledge from AI conversations automatically
Automatically pulls structured entities, requirements, and decisions from your AI conversations so you end up with clean spec components, not prose.
Provides the collaborative document layer where extracted knowledge gets assembled into a readable, shareable spec with AI-assisted formatting and gap-filling.
Auto-generate living product specs from your codebase for PMs and stakeholders
Once you start building, Specsight checks whether your codebase actually reflects your spec so the two never drift apart mid-sprint.
Expected outcome
A structured product spec document ready to hand to Cursor or Lovable, derived directly from your raw research materials
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