Infrastructure for Deliverable Quality Review Assistant

✅ REQUIREMENT JUSTIFIED - AUTONOMOUS QUALITY ASSESSMENT - Requires: Brand/quality standards as machine-readable rules, not prose documents - Must be explicit: Brand guidelines as structured if/then rules, quality rubrics with computable scoring criteria, completeness checklists as validation logic - Why L3 fails: Guidelines exist and are findable but not structured for machine consumption—AI can read "use Helvetica 12pt" but can't evaluate "logical flow requires introduction→analysis→recommendation sequence" - Why L2 fails: Guidelines scattered in multiple documents—AI can't synthesize coherent quality standards from fragmented sources - Why L1 fails: Quality standards are tribal ("partners know what good deliverables look like")—no explicit rules for AI to learn - **Gap from baseline F:2 → BLOCKED** (Gap 2) -

- Requires: Systematic capture of quality feedback (what revisions required, which issues recur, client feedback) - Template-driven post-delivery reviews - Why L2 fails: Reviews happen but inconsistently—missing systematic templates means feedback isn't categorized for pattern learning - Why L1 fails: Feedback capture ad-hoc—sparse training data - **Gap from baseline C:2 → STRETCH** (Gap 1)

✅ REQUIREMENT JUSTIFIED - QUALITY ONTOLOGY REQUIRED - Requires: Formal ontology mapping deliverable types → required sections → quality criteria - Entities: Document types (proposal, report, analysis), brand elements (fonts, colors, terminology), quality dimensions (completeness, accuracy, coherence), evaluation rules - Relationships: Deliverable type → required sections → evaluation criteria → compliant/non-compliant examples - Why L3 fails: Schema exists but evaluation rules incomplete—can check "font is Helvetica" but can't evaluate "analysis supports recommendations" - Why L2 fails: Basic categorization but no formal quality ontology—can tag documents as "proposal" or "report" but no structured quality standards - **Gap from baseline S:2 → BLOCKED** (Gap 2) -

- Requires: API access to document repositories (SharePoint, Google Drive), brand guidelines, previous deliverables (for consistency), source documents (for fact-checking) - Why L2 fails: Partial access—can access guidelines but not source documents for fact-checking - Why L1 fails: All access manual—quality review requires downloading files manually - **Gap from baseline A:1 → BLOCKED** (Gap 2)

- Requires: Event-triggered updates when brand guidelines change, quality issues emerge - Why L2 fails: Quarterly updates but not event-triggered—brand refresh mid-quarter means AI enforces old standards - Why L1 fails: Updates when someone notices staleness—quality standards lag reality by months - **Gap from baseline M:2 → STRETCH** (Gap 1)

- Requires: Document repository → Quality review system, Brand guidelines → Quality checker - Point-to-point sufficient - **Gap from baseline I:2 → READY** (Gap 0)