Jobs 2B Done -- Run JTBD analysis on sales, coaching, and industry call transcripts with UXinator Expectation Mapping. Pulls transcripts from Notion or pasted text, enriches with HubSpot and Clay connections, tracks coaching series with session numbering, saves structured analysis to organized folders, and populates the JTBD Analyses Notion database.
Three-layer AI context framework. Bootstraps docs/architect/ with Product, Project, and Task layers. Auto-commits on plan approval and nudges feature-dev for implementation.
Design, audit, and rehearse training sessions. Timing checks, engagement checks, survey generation, slide review, dry-run coaching, and story harvesting.
Discover brand materials across enterprise platforms, generate LLM-ready guidelines, and enforce brand voice on AI content.
Expectation Architecture for all delivery modes. Routes context to strategy, keynote, or training mode. Maps expectations, detects mode drift, designs deliverables, and evaluates sessions.
Ingest Claude Code JSONL session transcripts into Supabase for analytics and learning loop closure. Log, batch-ingest, and query session data across all your repos.