Methodology

Scaffolding

APQC PCF v8.0 Cross-Industry — ~1000+ processes across 13 categories, 5 levels of detail. Category 3.0 (“Market and Sell Products and Services”) is the source for the Sales department.

Composite Demand Score (top-25 selection)

Each process gets a composite score:

• 40% Upwork demand DB row count where archetype/keyword matches process
• 30% Marketplace template count (n8n + Make + Zapier) for process keyword
• 30% G2/Capterra critical review count (1-3 star) mentioning process pain in vertical SaaS

Top 25 by composite score were curated in Phase A. Remaining processes are flagged phase_a_deferred = true with reason.

v0 limitation: Phase A v0 uses Upwork-only ranking — marketplace template counts (n8n / Make / Zapier) and G2/Capterra critical review counts are seeded to 0 and not yet integrated. The composite scoring formula above represents the v1+ target. Top-25 selection for v0 is therefore equivalent to “top 25 Sales L3-L4 processes by Upwork demand DB row count keyword match.” This is acceptable for v0 because (a) Upwork demand DB is the highest-trust paid-to-fix signal we have, and (b) operator review during Phase A scoring can flag any top-25 process whose Upwork match is clearly spurious.

Evidence tiers

• Tier A — operator voice: Reddit (r/sales, r/B2BSales, r/saas, etc.), industry forums, podcast transcripts, critical G2/Capterra reviews where operators describe pain in their own words
• Tier B — paid-to-fix: Upwork demand DB (curated from existing pursuit pipeline), consulting case studies, RFPs
• Tier C — SaaS pricing reveals pain: Enterprise-tier feature lists from vertical SaaS — features priced at $$$$ reveal mid-market pain

Each evidence row carries: quote (≤600 chars), source URL, vertical (if any), dimension (time/money/scalability/handoff/shadow), and capture date.

LLM-extraction (v0 pivot)

Phase A v0 uses LLM-first extraction with operator review (modern industry-standard human-in-the-loop pattern, matches Gatewerk’s approve/edit conventions). Evidence quotes from Tier A/B/C pools are selected by an LLM (OpenAI gpt-4o-mini), pathology scoring is LLM-generated, and the operator reviews + overrides via Supabase Studio.

Every row carries an audit flag:

• processes.scored_by: unscored | llm-v0 | operator-edited | operator-original
• evidence.extracted_by: llm-v0 | operator-edited | operator-original
• handoffs.detected_by / shadow_deltas.detected_by: same enum

This transparency lets readers see exactly what’s been operator-validated vs. LLM-generated. The Phase B atom suite will rebuild this pipeline as proper atoms (process-pathology-score, process-sipoc-extract, process-evidence-curate) with full test coverage.

Curation discipline

This atlas uses LLM-extraction + operator review, not pure auto-generation. Phase A explicitly defers atomization — scripts produce candidate pools, but evidence rows and pathology scores are operator-curated. See docs/strategy/portfolio_v2/10_diagnostic_engine_plan.md for the full Phase A → B → C sequence.