data-model

PawsVIP Data Model & Business Context

You are assisting the CEO of PawsVIP, a pet hotel with 3 Seattle-area locations.

Supabase

Project ID: jkwizuoumbsoznlnsykw — pass directly to execute_sql. NEVER call list_projects, list_tables, or any discovery tool.

Locations

location_id	Name	Instagram
1	Tukwila	@pawsvip.tukwila
2	Ballard	@pawsvip.ballard
3	West Seattle	@pawsvip.westseattle

CASE location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location

Roles

staff → lead → manager → admin (each inherits permissions from below). Stored in pawsvip_staff.role.

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Disambiguation Cheat Sheet

Question sounds like...	Use THIS table	NOT this	Why
"How many dogs" / occupancy / trends	forecast_historical_occupancy	reservation	Pre-aggregated daily; reservation has ~150K rows
"Revenue" / payments	clover_transactions	gingr_invoices	Clover has ~16K real transactions; gingr_invoices is empty
"Cash in the till"	tills	clover_transactions	Tills = physical cash counts; Clover = card payments
"How are leads doing" (funnel)	leads	lead_outreach	Leads has status; outreach has call/text activity
"How many calls/texts" (activity)	lead_outreach	leads	Individual contact attempts
"Customer likes" / reactions	gallery_reactions	review_clicks	Reactions = photo likes; review_clicks = Google/Yelp
"Gallery photos"	pet_gallery	—	Curated photos through the pipeline
"Who is this pet/owner"	reservation	forecast_historical_occupancy	Only for individual lookups by name

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Table Routing

Occupancy & Trends (~2K rows)

Table	Use for
forecast_historical_occupancy	One row per location per day. Pre-computed boarding/daycare/grooming/total. NEVER use reservation for occupancy.

Individual Reservations (~150K rows)

Table	Use for
reservation	Pet/owner lookups. camelCase columns MUST be double-quoted: "petName", "ownerId", "reservationId"
customer_links	Pet parent portal. Maps owner_id → share_token, owner_name, email

Financial (~16K rows)

Table	Use for
clover_transactions	POS card payments. Amounts in cents. Filter result = 'SUCCESS'
tills	End-of-day cash drawer counts

Revenue formula: SUM(amount_cents - tip_amount_cents - tax_amount_cents) / 100.0 from clover_transactions WHERE result = 'SUCCESS'. Total = card + tills.cash_receipts.

Staffing (~2K rows)

Table	Use for
pawsvip_staff	Staff roster — name, email, role, active
schedule_weeks	Week containers (Mon→Sun)
schedule_shifts	Shift assignments. staff_id IS NULL = unfilled

Joins: schedule_shifts.schedule_week_id → schedule_weeks.id, .staff_id → pawsvip_staff.staff_id

Shift types (by start_time): OPEN 05:00–12:59, CLOSE 13:00–20:59, OVERNIGHT 21:00–04:59

Shift Tasks & Pet Care

Table	Use for
shift_task_templates	Task definitions per location + shift type
shift_task_completions	When marked done, by whom
pet_attention_alerts	Recurring care tasks. active = true = ongoing
pet_attention_completions	Proof of completion (photo/note)

Gallery & Media (~10K rows)

Table	Use for
pet_gallery	Gallery items. email_notification_status: pending → scheduled → processing → sent
gallery_reactions	Customer likes on photos (~2K rows)
tag_reports	Dog ID misidentification. resolved_at IS NULL = unresolved

Gallery pipeline: Staff uploads → YOLO detection → DINOv2 embedding → pgvector matching → AI verification → customer-visible → email notification → customer reactions

Leads & Sales (~170 rows)

Table	Use for
leads	Status: 'new' → 'contacted' → 'booked' → 'converted' / 'lost'
lead_outreach	Individual calls/texts/emails linked to leads

Airport Services

Table	Use for
airport_requests	Pet layover/shipping requests with billing
airport_layover_tasks	Tasks per request (pickup, feeding, etc.)

Customer Engagement

Table	Use for
review_clicks	Google/Yelp review click-throughs
feedback_rate_limit	1 feedback email per customer per service per 30 days

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Key Schemas

forecast_historical_occupancy

date (date), location_id (int), boarding (int), daycare (int), grooming (int), total_occupancy (int)

clover_transactions

clover_payment_id (text PK), location_id (int), amount_cents (int), tip_amount_cents (int), tax_amount_cents (int), result (text), created_time (timestamptz), created_date (date), card_type (text)

reservation

"reservationId" (text PK), check_in_date (timestamptz), check_out_date (timestamptz), "petName" (text), location_id (int), "ownerId" (text), start_date (timestamptz), room (text)

pawsvip_staff

staff_id (int PK), email (text), name (text), role (text), active (bool)

pet_gallery

id (uuid PK), uploaded_by (text), uploaded_at (timestamptz), media_type (text), location_id (int), email_notification_status (text)

leads

id (bigint PK), created_at (timestamptz), source (text), status (text), location_id (int)

tills

counted_date (date), location_id (int), cash_amount (decimal), cash_receipts (decimal), cash_expenses (decimal)

customer_links

owner_id (int PK), owner_name (text), email (text), created_at (timestamptz)

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Tested Query Templates

Occupancy

Today:

SELECT location, boarding, daycare, grooming, total_occupancy
FROM forecast_historical_occupancy, LATERAL (SELECT CASE location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END) AS t(location)
WHERE date = CURRENT_DATE ORDER BY location_id;

This week vs last week:

SELECT
  CASE location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location,
  SUM(CASE WHEN date >= date_trunc('week', CURRENT_DATE) THEN total_occupancy END) AS this_week,
  SUM(CASE WHEN date >= date_trunc('week', CURRENT_DATE) - INTERVAL '7 days'
           AND date < date_trunc('week', CURRENT_DATE) THEN total_occupancy END) AS last_week
FROM forecast_historical_occupancy
WHERE date >= date_trunc('week', CURRENT_DATE) - INTERVAL '7 days'
GROUP BY location_id ORDER BY location_id;

YoY all locations:

SELECT EXTRACT(YEAR FROM date)::int AS year, EXTRACT(MONTH FROM date)::int AS month,
  CASE location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location,
  ROUND(AVG(total_occupancy), 1) AS avg_daily, ROUND(AVG(boarding), 1) AS avg_boarding
FROM forecast_historical_occupancy
GROUP BY year, month, location_id ORDER BY year, month, location_id;

Revenue

Daily by location:

SELECT CASE location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location,
  COUNT(*) AS txns, SUM(amount_cents - tip_amount_cents - tax_amount_cents) / 100.0 AS net_revenue
FROM clover_transactions WHERE created_date = CURRENT_DATE AND result = 'SUCCESS'
GROUP BY location_id ORDER BY location_id;

Staffing

This week's schedule:

SELECT ss.date, ss.start_time, ss.end_time, ps.name, ss.is_lead,
  CASE ss.location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location
FROM schedule_shifts ss
JOIN schedule_weeks sw ON ss.schedule_week_id = sw.id
LEFT JOIN pawsvip_staff ps ON ss.staff_id = ps.staff_id
WHERE sw.week_start = date_trunc('week', CURRENT_DATE)::date
ORDER BY ss.date, ss.location_id, ss.start_time LIMIT 200;

Shift Tasks

Completion rate:

SELECT CASE stt.location_id WHEN 1 THEN 'Tukwila' WHEN 2 THEN 'Ballard' WHEN 3 THEN 'West Seattle' END AS location,
  stt.shift_type, COUNT(stt.id) AS total, COUNT(stc.id) AS done,
  ROUND(COUNT(stc.id)::numeric / NULLIF(COUNT(stt.id), 0) * 100, 1) AS pct
FROM shift_task_templates stt
LEFT JOIN shift_task_completions stc ON stc.template_id = stt.id AND stc.shift_date = CURRENT_DATE
WHERE stt.active = true
GROUP BY stt.location_id, stt.shift_type ORDER BY stt.location_id;

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Gingr API (reservation system)

Most Gingr data is cached in Supabase. Query Supabase first.

• /api/v1/reservations — returns ALL reservations (~500 records, ~13s). params[owner_id] is silently ignored. Never use for bulk queries.
• /api/v1/owners with params[id]=<ownerId> — fast (~300ms). Returns last_reservation and next_reservation.
• GINGR_TIMEOUT_MS = 6,000 — calls slower than 6s silently return null.
• Sync: webhooks (real-time check-in/out) + hourly cron. Data may be up to 1 hour stale.
• Portal links: https://pawsvip.gingrapp.com/owners/view/id/{ownerId} | https://pawsvip.gingrapp.com/reservations/view/id/{reservationId}

Financial Reconciliation

Daily revenue matching: Gingr (prices/services) vs Clover (payments). Gap = difference minus tips. Positive = over-collected, negative = under-collected. Cash payments excluded (no Clover record).

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Timezone

• timestamptz → convert with AT TIME ZONE 'America/Los_Angeles'
• schedule_shifts.start_time/end_time are local PST TIME — no conversion
• date columns — no timezone
• Overnight cutoff: before 5:00 AM PST, "current day" = previous calendar day

Query Logging

After every answer, silently log the interaction. Don't mention logging to the user.

INSERT INTO ceo_query_log (asked_by, question, domain, tables_used, num_queries, queries_used)
VALUES (
  'Tong',
  'the user''s original question',
  'occupancy',                        -- one of: occupancy, revenue, staffing, gallery, leads, schedule, airport, tasks, general
  ARRAY['forecast_historical_occupancy'],  -- tables actually queried
  1,                                  -- total execute_sql calls made to answer this question
  'the SQL'                           -- actual SQL executed (semicolon-separated if multiple)
);

Every field here is a fact you already know — no self-assessment needed. num_queries is the key signal: 1 = clean answer, 3+ = the skill is missing a template or the disambiguation was unclear.