95d6fdf499
Add luxury/vertical signal to auto-tiering
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pick_baseline now applies a premium floor: if prospect.vertical matches
rate_card.tiering.premium_verticals (luxury/premium/deluxe/5성/특1급…), the
auto-selected tier is floored to premium_min_tier (default basic) so a premium
single property won't drop to the smb entry tier.
Validated: L'Escape (hotel_luxury, 1 property) smb -> basic (₩10.5M);
non-luxury single -> smb (₩3.0M); SHR chain -> treatment (₩29.5M) unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com >
2026-05-28 01:15:55 +09:00
60734dbde7
Recalibrate estimate for SMB acceptability
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Real-world feedback: list-rate calc overshot SMB-acceptable levels.
- scaling: driver properties_total -> subbrands_total (chains share templates),
cap x6.5 -> x2.0, applied to On-page only (Technical now fixed site-wide)
- add 'smb' entry tier (lean hours @ 0.55 billing); 3-tier auto-select
(smb/basic/treatment) by portfolio size; per-tier billing; --baseline smb enabled
- docs (findings_to_service.md, SKILL.md) synced to 3-tier model
Effect: SHR 25-property chain 71.5M -> 29.5M; SMB single hotel ~3.0M;
basic/treatment still reproduce real 10.5M/25.0M at 1 property.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com >
2026-05-28 01:13:38 +09:00
5f66f57a8e
Rebuild estimate engine to effort-based SOW model
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Replace flat per-service ranges with OurDigital's real quoting model
(role_rate × billing_rate 0.70 × standard hours), sourced from the
06_Working Template quotes:
- rate_card.yaml: role rate card, billing/basis/terms, tools, scaling bands
- sow_templates.yaml: basic + treatment task-hour templates
- estimate.py: assemble SOW from findings, scale Technical/On-page hours by
properties_total, 제안가 = 합계 floored to 500k
- build_deck.py: estimate slide shows module 소계 + 제안가 (point)
- findings_to_service.md / SKILL.md / DESIGN.md: synced to new model
Validated: reproduces real Basic ₩10.5M and Treatment ₩25.0M exactly;
SHR (25 properties) scales to ₩71.5M, L'Escape (1) = ₩25.0M.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com >
2026-05-28 00:51:52 +09:00
ba88247496
Implement ourdigital-presales-seo skill
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SKILL.md orchestration (8 gated stages), references (rate_card.yaml,
findings_to_service rubric, competitor sets), findings.schema.json contract,
and scripts: kg_query.py (generalized KG examination), estimate.py
(findings→rate-card 견적 md/xlsx/json), build_deck.py (9-slide branded PPTX),
render_pdf.sh (Korean PDF via headless Chrome), plus client_brief.html template.
Validated on Sono Hotels & Resorts findings: estimate OD-2026-001
(23-47M KRW) and a 9-slide deck generated cleanly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com >
2026-05-27 23:10:53 +09:00
