Automation Scenarios

How we'd approach it.

These are illustrative scenarios based on common SMB operations — not specific past clients. Each shows the bottleneck, the architecture we'd reach for, and the tradeoffs behind the call.

Illustrative scenario
01 · Logistics SMB

Unifying four logistics portals

PORTALSGATEWAYQUEUEPOSTGRESDASH
Architecture snapshot
Bottleneck

Operators copied shipment data between four carrier portals every morning. Errors and 24–48h reporting lag.

Architecture

Built a FastAPI gateway that pulls each portal on a schedule, normalizes shipments into a single schema, and pushes status updates back. Background workers handle retries; a small dashboard shows daily totals.

Stack
FastAPICeleryRedisPostgres
Outcome
  • · No more morning copy-paste
  • · Shipment data consistent across portals
  • · Reporting refreshes automatically
Why this architecture

A custom gateway was simpler than chaining no-code tools and gave operators clear visibility when a portal hiccuped.

Illustrative scenario
02 · SaaS SMB

Stripe-to-accounting reconciliation

STRIPEWEBHOOKPOSTGRESRECONCILESLACK
Architecture snapshot
Bottleneck

Finance reconciled Stripe payouts to invoices manually each month. Mismatches were caught late and slowed close.

Architecture

Webhook listener captures Stripe events into Postgres, a nightly job reconciles them with invoices and surfaces mismatches in a review queue. Slack notifications for anomalies.

Stack
FastAPIPostgresCelerySlack API
Outcome
  • · Month-end close moved earlier
  • · Mismatches surface within a day
  • · Audit trail for every payout
Why this architecture

Direct webhook ingestion plus a small review UI was cheaper than buying another finance tool.

Illustrative scenario
03 · Services SMB

Bi-directional CRM ↔ internal DB sync

HUBSPOTAPIMAPPERPOSTGRES
Architecture snapshot
Bottleneck

Sales lived in HubSpot. Delivery lived in an internal Postgres app. Status updates drifted apart.

Architecture

FastAPI service syncs both directions on a schedule with conflict resolution rules. Field-level mapping is stored in config so non-engineers can adjust.

Stack
FastAPIHubSpot APIPostgresAPScheduler
Outcome
  • · Sales and delivery see the same status
  • · Mapping changes don't need a deploy
  • · Errors are logged with the row that failed
Why this architecture

A real service with conflict logic was more durable than two-way Zaps that fought each other.

Got a workflow that looks like one of these?

Tell us about it on a 30-minute discovery call.

Book a Free Discovery Call
Start QuizBook a Call