E-commerce · Case Study 01
Full order lifecycle automation for a DTC brand
A fast-growing direct-to-consumer skincare brand was manually copying orders from Shopify into HubSpot, updating spreadsheets, and sending fulfillment emails one by one. 18 hours a week of pure ops work — all of it eliminated.
Automation flow
The problem
The ops team was spending 3+ hours daily copying order data between Shopify and HubSpot, manually flagging high-value customers, updating inventory in Airtable, and triggering email sequences. Every step was a potential for human error — and errors were happening weekly.
Our solution
We built a Make.com scenario that fires on every new Shopify order: creates or updates the HubSpot contact and deal, syncs inventory to Airtable, tags high-LTV customers, triggers the appropriate Klaviyo sequence, and notifies the team via Slack for orders above a threshold. Full error handling with retry logic and Slack alerts on failures.
"I used to start every morning with an hour of copy-paste between Shopify and HubSpot. Now I open Slack and everything is already there. It just works. Autegra built this in three days — what were we even waiting for?"
— Sophie R., CEO, Bloom DTCSaaS · Case Study 02
AI-powered lead scoring and routing pipeline
A B2B SaaS company was drowning in inbound leads with no reliable way to prioritise them. Sales reps were spending 40% of their time on prospects who would never convert. We built an LLM-powered enrichment and scoring system that changed that overnight.
Automation flow
The problem
400+ inbound leads per month with no scoring system. Sales reps manually reviewed every lead, looked up company info, and wrote personalised emails from scratch. Response time averaged 4 hours. High-value prospects were going cold while reps were stuck on unqualified traffic.
Our solution
An n8n pipeline that triggers on every form submission: Clearbit enriches the contact data, GPT-4 scores the lead against their ICP criteria and outputs a 0–100 score with reasoning, the lead is routed to the right rep in Salesforce, and a personalised outreach email is drafted and surfaced in the rep's inbox — ready to send with one click.
"Our reps now spend their time closing, not researching. The AI drafts are genuinely good — we use them almost verbatim 70% of the time. The ROI on this was visible within the first week."
— James L., VP Sales, Vantage CRMFintech · Case Study 03
Real-time financial reporting and reconciliation
A fintech startup was spending 3 days every month closing the books — manually reconciling Stripe transactions against QuickBooks, chasing discrepancies, and building reports in spreadsheets. We brought that to 4 hours and made it largely automatic.
Automation flow
The problem
Every Stripe payment, refund, and dispute had to be manually entered into QuickBooks and reconciled. One person was dedicated to this for 3 days per month. Discrepancies were only caught at month-end, by which point tracing them back was a multi-hour job.
Our solution
A real-time Make pipeline that fires on every Stripe event: classifies the transaction type, creates the correct QuickBooks entry with the right account codes, flags anomalies (refund rate spikes, unusual amounts) via Slack, and feeds a live Google Sheet dashboard. Month-end close is now a review of the dashboard, not a reconstruction exercise.
"Our accountant used to dread month-end. Now she reviews a dashboard and it's done in an afternoon. Autegra also caught a recurring error in our refund accounting that had been wrong for months — we hadn't even noticed it."
— Marcus K., Head of Operations, Clearpath SaaSWant results like these?
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