Project / Internal systems
Next-day financials became real-time, today included.
- Claude API
- OpenAI API
- LangChain
- LlamaIndex
- Pinecone
- n8n
- Make.com
- Zapier
- Supabase
- Airtable
- PostgreSQL
- Google Sheets
- Python
- FastAPI
- Node.js
- Cloudflare
- Google Cloud
- Hetzner
- Shopify
- Slack
- HubSpot
- GoHighLevel
- Klaviyo
- Chatwoot
- WhatsApp Business API
- Meta API
- Mailgun
- OpenAI Vision
- ClickFunnels
- Twilio
- ElevenLabs
- Whisper / TTS
- Docker
- Redis
- Stripe
What it is
Atlas ran on a spreadsheet that was always a day behind. Someone pulled Shopify, Meta Ads, and Google Ads by hand each morning, pasted them into one sheet, and estimated the Shopify transaction fees because the real per-country rates were too fiddly to look up. Half an hour a day, every day, and the number you were reading was yesterday’s.
Two things replaced it. Underneath, a scheduled pipeline pulls all three sources into one store every day, applies the actual per-country Shopify fee schedule instead of an estimate, and backfills COGS weekly. On top, a live-fetch scenario goes and gets today’s numbers on demand, so the dashboard includes the hours that have already happened. Multiple stores from day one — consolidated, or one at a time.
Manual entry went from thirty minutes a day to about ten minutes a week, and that ten minutes is checking, not typing.
- 3data sources unifiedShopify · Meta · Google
- 100%Shopify fee accuracyper-country, was estimated
- ~10 min/weekmanual entrywas ~30 min/day
From a real engagement. Names are changed; the numbers are not.
How it works
Two clocks — a nightly pull and a live fetch — against one store.
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A scheduled pull, every morning
A
Make.comscenario pullsShopify,Meta Ads, andGoogle Adsinto oneSupabasestore on a schedule. That replaces a person opening three dashboards and pasting the numbers into a spreadsheet by hand. -
The fees are exact, not estimated
The old sheet estimated
Shopifytransaction fees because the real per-country rates were fiddly to look up. The pipeline applies the actual per-country fee schedule instead, and reconciles COGS on a weekly backfill scenario. Fee accuracy went from "about right" to 100%. -
A live fetch fills the gap
Opening the dashboard triggers a second scenario that goes and gets today's numbers on demand. The view includes the hours that have already happened rather than ending at yesterday's close — that was the whole point of the project.
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One view, every store
Stores are consolidated by default and switchable one at a time, from day one. Manual entry dropped from about thirty minutes a day to ten minutes a week, and that ten minutes is checking, not typing.
“It cleared the queue we could never keep up with. We only look at the handful it flags now.”
Operations lead · multi-store retail operator
“Condition grading and pricing that used to be a weekend is now a photo upload.”
Founder · collectibles brand
“The drafts are good enough that answering is now approving.”
Operations lead · B2B services
“I stopped waiting for yesterday's numbers. Today's are just there, all stores in one view.”
Owner · multi-store retail operator
“It books and qualifies before we're even awake. The morning list is just the ones worth a callback.”
Founder · home-services company
“It shipped in pieces we could check as they landed, and the pieces we argued with got changed.”
Operations manager · logistics
“None of it is rented. I did not expect that to matter as much as it turned out to.”
Founder · consumer goods
Proof
"I stopped waiting for yesterday's numbers. Today's are just there, all stores in one view."
Owner · multi-store retail operator
References on request.