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main·2 cells modified
JK
Q3 Board Review
▾ notebooks
retention_cohorts
q3_revenue●
churn_drivers
▾ dashboards
board_q3
exec_weekly
▾ scripts
py seed_warehouse.py
py nightly_etl.py
▾ data
stripe_export.csv
cohorts_q3.parquet
▾ docs
md README.md
tex board_memo.tex
pdf q2_report.pdf
▾ queries
arr_by_segment
nrr_quarterly
top_accounts_q3
▾ connections
snowflake · prod
postgres · analytics
stripeapi
Schema · stripe
▾ stripe
charges8.4k
iduuid
amountint
created_atts
statustext
user_iduuid
subscriptions412
plan_changes1.2k
invoices9.1k
refunds204
q3_revenue.orchid×
churn_drivers.orchid×
q3_revenue·edited 2 min ago by Jamie·● postgres + stripe connected

Q3 revenue — where did the $200k week come from?

SQLweekly_revenue_by_planran 0.42s · 48 rows
1select date_trunc('week', created_at) as wk,
2 plan,
3 sum(amount) / 100.0 as revenue
4from stripe.charges
5where created_at >= '2025-07-01' and status = 'succeeded'
6group by 1, 2 order by 1;
✓ 12 weeks · 4 plans · total $4,317,204 · crossed $200k on 2025-09-22
CHARTrevenue_by_plan12 weeks · area
Weekly revenue+38% QoQJul → Sep
$200k
PYTHONattribute_spikeran 0.18s
1import pandas as pd
2spike = df_revenue[df_revenue.wk == '2025-09-22']
3upgrades = plan_changes.merge(spike, on='user_id')
4upgrades.groupby('from_plan').revenue.sum()
from_plan revenue Team 152,400.00 Pro 38,900.00 Free 9,200.00 → 76% of lift came from Team→Pro upgrades
OutputTerminalProblems (1)Debug
mainpostgres · stripe · bigqueryKernel: Python 3.11 · 1.2GB2 cells modifiedSaved
Analyst
JK
You14:02
Q3 revenue by plan. Mark the week we crossed $200k, and tell me why it spiked — was it new logos or expansion?
Analystagent14:02
Here's what I found. The 2025-09-22 spike is mostly expansion — three Team accounts upgraded to Pro that week. Drafting the cells now.
Inspected schema · stripe.* + subscriptions120ms
Wrote SQL weekly_revenue_by_plan0.42s
Cross-checked plan_changes · 14 upgrades0.18s
Drafting chart revenue_by_plan2 cells
q3_revenue.orchid+18−2
chart: line(revenue) by plan
chart: area(revenue) by plan
annotate: 2025-09-22 ($200k crossed)
annotate: 3 Team→Pro upgrades
Plan, Build, / for commands · @ for context
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Notebooks

Python and SQL, in the same file.

Variables flow between cells. SQL results land as DataFrames. The format is plain YAML on disk — diffable in git, reviewable in a PR, and the agent can read it as easily as you can.

Notebook · Python + SQL
Orchidq3_revenue_insightsNotebookApp

Where did the $200k week come from?

Jamie K.·edited 2m ago·● postgres + stripe live
SQLweekly_revenue_by_planran 0.42s · 48 rows
1select date_trunc('week', created_at) as wk,
2 plan,
3 sum(amount) / 100.0 as revenue
4from stripe.charges
5where created_at >= '2025-07-01' and status = 'succeeded'
6group by 1, 2;
✓ 12 weeks · total $4,317,204 · crossed $200k on 2025-09-22
CHARTrevenue_by_plan12 weeks · area
Weekly revenue+38% QoQ$200k crossed · 2025-09-22
$200k
PYattribute_spikeran 0.18s
1spike = df_revenue[df_revenue.wk == '2025-09-22']
2upgrades = plan_changes.merge(spike, on='user_id')
3upgrades.groupby('from_plan').revenue.sum()
from_planusersrevenue
Team3$152,400
Pro5$38,900
Free8$9,200
→ 76% from Team→Pro
Analyst
JK
You14:02
Q3 revenue by plan. Mark the week we crossed $200k, and tell me why it spiked — was it new logos or expansion?
Analystagent14:02
Here's what I found. The 2025-09-22 spike is mostly expansion — three Team accounts upgraded to Pro that week. Drafting the cells now.
Inspected schema · stripe.*120ms
Wrote SQL weekly_revenue_by_plan0.42s
Cross-checked plan_changes · 14 upgrades0.18s
Annotating chart revenue_by_plan2 cells
q3_revenue.orchid+18−2
chart: line(revenue) by plan
chart: area(revenue) by plan
annotate: 2025-09-22 ($200k crossed)
Plan, Build, / for commands · @ for context
PlanGemini 2.5 Flash▾
SQL · multi-warehouse

One editor. Every warehouse you actually use.

Snowflake, PostgreSQL, BigQuery, DuckDB — first-class. Schema autocomplete updates live as you push migrations. Results arrive as DataFrames you can pipe straight into Python or render as a chart.

SQL workbench · multi-warehouse
Orchidarr_by_segment.sqlsnowflake · prod
CONNECTIONS
snowflake · prod
postgres · analytics
bigquery · events
duckdb · local
SCHEMA · stripe
charges8.4k
iduuid
amountint
created_atts
statustext
user_iduuid
subscriptions412
plan_changes1.2k
invoices9.1k
refunds204
SAVED
arr_by_segment
nrr_quarterly
top_accounts_q3
arr_by_segment.sql×nrr_quarterly.sql×+
1with active as (
2 select user_id, segment, plan, mrr
3 from stripe.subscriptions
4 where status = 'active'
5)
6select segment,
7 count(distinct user_id) as accts,
8 sum(mrr) * 12 as arr,
9 sum(mrr) * 12 / sum(sum(mrr)) over() as share
10from active
11group by 1 order by arr desc;
✓ ran 0.31s3 rows · 4 colssnowflake · prod · WAREHOUSE_S
#segmentacctsarrshare
1Enterprise84$2,412,80056.0%
2Mid-Market196$1,184,20027.4%
3Startup312$724,00016.6%
Total592$4,321,000100%
Docs & reports

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Doc · Markdown + LaTeX live preview
Orchidq3_board_memo.mdSourceSplitPreviewauto-saved
1# Q3 board memo
2_Prepared by Jamie · 2025-10-04_
3
4## Headline
5Q3 revenue closed at {{revenue_q3}}, up {{qoq_pct}} QoQ.
6The week of 2025-09-22 crossed $200k for the first time.
7
8## Why it spiked
9Three Team accounts upgraded to Pro. Expansion revenue:
10
11$$\Delta R = \sum_{i} (p_{new,i} - p_{old,i}) \cdot u_i$$
12
13## Chart
14![](chart:revenue_by_plan)
15
16## Recommendation
17Lean into Team→Pro upgrade nurture. Forecast Q4 in
18**Appendix A**.

Q3 board memo

Prepared by Jamie · 2025-10-04

Headline

Q3 revenue closed at $4,317,204, up +38% QoQ. The week of 2025-09-22 crossed $200k for the first time.

Why it spiked

Three Team accounts upgraded to Pro. Expansion revenue:

ΔR = ∑i(pnew,i − pold,i) · ui

Chart

revenue_by_planlive

Recommendation

Lean into Team→Pro upgrade nurture. Forecast Q4 in Appendix A.

Agents

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The agent reads your live schema, drafts the queries and chart, and hands you a diff. No black box, no surprise runs against production.

Jamie · 14:02
“Q3 revenue by plan. Mark the week we crossed $200k, and tell me why it spiked.”
14:02:08Inspected schema · stripe.* + subscriptions
14:02:11Queried stripe.charges · 8,422 rows · 0.42s
14:02:14Cross-checked plan_changes · 14 upgrades that week
14:02:18Wrote SQL weekly_revenue_by_plan
14:02:22Drafted chart · awaiting review
1select date_trunc('week', c.created_at) as wk, 2 c.plan, 3 sum(c.amount) / 100.0 as revenue, 4 count(distinct p.id) as upgrades 5from stripe.charges c 6left join plan_changes p 7 on p.user_id = c.user_id 8 and p.changed_at between c.created_at - '7d' and c.created_at 9where c.created_at >= '2025-07-01' 10 and c.status = 'succeeded' 11group by 1, 2 12order by 1; -- spike on wk of 2025-09-22: -- Pro $214k (+38% wow) -- 3 Team→Pro upgrades drove 71% of lift
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Q3 board review · weekly_revenue×
Linear — INF-241
Stripe Dashboard
↻
🔒acme.orchidide.com/q3-board-review/weekly_revenue☆
acme/q3-board-review/weekly_revenue● saved · 2s ago
MP
DR
SF
JK
+2
weekly_revenue.orchid·Editing·v0.4.2

Q3 revenue — board review

Weekly revenue by plan, with the upgrade events that drove the late-September spike. Numbers tie to Stripe; methodology is documented in cell methodology below.

MDintroedited 1m ago

TL;DR — Q3 closed at $4.31M, up 38% QoQ. The week of 2025-09-22 crossed $200k for the first time, driven by three Team→Pro upgradesDiego.

Source: stripe.charges + plan_changes, joined on user_id, succeeded status only.

SQLweekly_revenue_by_planran 0.42s · 48 rows
1select date_trunc('week', created_at) as wk,
2 plan,
3 sum(amount)/100.0 as revenue
4from stripe.chargesMaya
5where status = 'succeeded' and amount > 0
6group by 1, 2 order by 1;
CHARTrevenue_by_plan12 weeks · area
$200k
PYattribute_spikeran 0.18s
1spike = df_revenue[df_revenue.wk == '2025-09-22']
2upgrades = plan_changes.merge(spike, on='user_id')
3upgrades.groupby('from_plan').revenue.sum()
from_plan revenue Team 152,400.00 Pro 38,900.00 Free 9,200.00 → 76% of lift came from Team→Pro upgrades
Sara

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