Kjell Tore Guttormsen e57dee5a03 chore(ms-ai-architect): scrub identifying references from fixtures + remove screenshots

Removes:
- All 6 PNG screenshots (playground/screenshots/) and the capture script
  (scripts/screenshots/capture-playground.py).
- "Screenshots" section from plugin README.
- "Screenshot-suite" section from plugin CLAUDE.md.
- Screenshots bullet from marketplace root README's ms-ai-architect listing.

Scrubs the 17 synthetic fixtures + CHANGELOG/CLAUDE/README of identifying
references: organization names, government-agency names, agency-specific
terminology, sector-specific use cases. Replaced with generic placeholder
data ("Acme AS" / "Demosystem") that exercises the same parser archetypes.

Plugin's domain-target wording (Datatilsynet, offentlig sektor, offentlig
myndighet, rettshåndhevelse, NS 5814, Utredningsinstruksen, EU AI Act
Annex III categories) is intact — those describe the plugin's intended
audience, not any specific entity.

This is a cleanup commit. Earlier git history still contains the prior
references; force-push or rebase is required if scrubbing the history is
desired. That decision is out of scope here — please run it separately
if needed.

Verified post-scrub:
- bash tests/validate-plugin.sh -> 215/215 PASS
- bash tests/run-e2e.sh --playground -> 240/240 PASS (170 + 70)

2026-05-03 20:53:49 +02:00

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Kostnadsestimat — Demosystem

System: Demosystem (Acme AS) Periode: 12 måneder fra produksjonssetting Valuta: NOK

Distribusjon (P10/P50/P90)

Persentil	Månedlig (NOK)	Årlig (NOK)
P10	78 000	936 000
P50	142 000	1 704 000
P90	285 000	3 420 000

Månedlig fordeling (P50)

Komponent	Kostnad (NOK/mnd)
Azure AI Services (OCR + classification)	64 000
Azure OpenAI (forklaringsmodell)	28 000
Azure AI Search (indeks for objektregister)	12 000
Storage (blob + cosmos for audit)	8 500
Compute (Container Apps for orchestration)	11 000
Networking (Private Endpoints + egress)	5 200
Monitoring (Sentinel + Log Analytics)	9 800
Backup og DR	3 500

TCO-tabell (3 år)

År	Capex	Opex	Total	Akkumulert
År 1	850 000	1 704 000	2 554 000	2 554 000
År 2	120 000	1 875 000	1 995 000	4 549 000
År 3	80 000	2 060 000	2 140 000	6 689 000

Kostnadsdrivere

Datavolum: ~12 millioner Demosystem-deteksjoner/mnd
Forklaring-prompt-tokens: ~250 tokens per flagged hendelse
Reservert kapasitet for 99.9% SLA

Konfidensgradering

P50 er beregnet med 95% konfidens basert på 6 måneder pilot-data. P90 inkluderer 2× volum-skalering ved fullnasjonal utrulling. P10 forutsetter optimaliserte prompt-cache (>40% hit-rate).

Anbefaling

Bruk P50 som budsjettlinje. Sett alarm på 1.4× P50 (≈ 200 000/mnd) for tidlig varsling.

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