Automation that pays
for itself — with proof.

The Problem

A Shopify store with $80K/month in traffic was losing 71% of carts at checkout. Their Klaviyo setup sent a single generic email 24 hours later — barely converting 3%. Each abandoned cart was on average $180, so $57K/month was silently walking away.

The Solution

Built a 5-touch intelligent recovery system. First message fires 6 minutes after abandonment, AI-personalized based on cart items and customer history. Subsequent messages use urgency escalation (low stock, expiring discount), dynamic product recommendations, and SMS + email coordination. High-value carts ($300+) trigger a personal outreach from the sales team.

Business Impact

• 💰 $64K in previously-lost revenue recovered in month one
• 📈 Cart recovery rate from 3% to 23%
• 🤖 AI personalization lifted email CTR by 67%
• 📱 SMS integration added 11% additional recovery

The Problem

A consulting firm with 40+ active clients was manually creating invoices in Google Docs, tracking payments in a spreadsheet, and chasing overdue payments via personal emails. Average payment collection time: 47 days. Finance staff spent 12 hours/week on billing admin alone.

The Solution

End-to-end billing automation: project completion triggers invoice generation from template, Stripe link embedded, automatic payment reminders on day 3, 7, 14 with escalating urgency. Overdue past 21 days triggers a formal notice and flags the account in CRM. All payment events sync to accounting in real time.

Business Impact

• 💰 $23K in overdue invoices collected in first 30 days
• ⏱️ Average collection time dropped from 47 to 18 days
• 🧑‍💼 12 hours/week of finance admin completely eliminated
• 📊 Real-time AR visibility for the first time

The Problem

A SaaS product with 3,000 users was drowning in support tickets. Emails from Gmail, messages from Intercom, and DMs on Twitter/X were going to three separate inboxes with a 24+ hour response time. Support team of 2 people was spending 90% of their time on repeat questions.

The Solution

Unified all inboxes into a single n8n processing pipeline. GPT-4 reads every incoming message, classifies intent, checks the knowledge base, and either sends an instant AI-drafted resolution or routes to the correct human agent with context pre-filled. Sentiment analysis flags angry customers for immediate escalation.

Business Impact

• 🤖 73% of tickets auto-resolved without human involvement
• ⚡ Response time dropped from 24 hours to 3 minutes
• 💰 $8,400/month saved in support labor costs
• 😊 NPS increased by 22 points in 60 days

The Problem

A marketing agency produced one long-form blog post per week and manually adapted it for LinkedIn, Twitter, and Instagram. This took 4 people 18+ hours weekly. Distribution was inconsistent, engagement was low, and the content strategy was bottlenecked by bandwidth.

The Solution

Built an AI content factory: one CMS publish triggers automatic generation of 12 derivative content pieces — LinkedIn articles, Twitter threads, Instagram carousels (copy), email newsletter excerpts, and YouTube script intros — each adapted to platform tone via GPT-4. Human approval via Slack with one-click publish to all channels.

Business Impact

• 📝 1 blog post now becomes 12 platform-ready pieces
• ⏱️ 18 hours of weekly content work reduced to 2
• 📈 214% increase in LinkedIn organic reach in 90 days
• 💰 Saved $4,200/month in content production costs

The Problem

A real estate firm received 80–120 property inquiry forms daily. Agents spent 4+ hours/day sorting genuine buyers from tire-kickers, manually following up, and updating the CRM — work that kept them from high-value activities like showings and negotiations.

The Solution

Built a lead intelligence platform: every inquiry triggers AI qualification (budget, timeline, seriousness signals), automatic property matching from the MLS database, and personalized follow-up sequence. High-intent leads get a phone call booked instantly. Mid-funnel leads enter a tailored property alert system. Agents receive a scored, prioritized dashboard every morning.

Business Impact

• 🏠 35% higher lead-to-showing conversion rate
• 💰 $420K in new pipeline within 60 days of launch
• ⏱️ Agent prospecting time reduced by 4 hours/day
• 📊 100% CRM data accuracy (was 60% before)

The Problem

A SaaS company was only discovering churned customers after they had already cancelled — too late to intervene. No usage monitoring, no health scores, no early warning system. Customer success team was reactive, not proactive.

The Solution

Built a behavioral health scoring system: product usage events stream into n8n in real-time, an ML model calculates a daily health score per account, and accounts crossing risk thresholds trigger automated intervention sequences — in-app messages, personalized outreach from the CSM, and executive escalation for enterprise accounts. 14-day average early warning lead time.

Business Impact

• 📉 Churn rate dropped from 8.2% to 5.7% monthly
• 💰 $95K ARR retained that would have churned
• ⚡ CS team proactivity went from 10% to 85%
• 💡 Feature adoption increased 40% from targeted nudges

The Problem

A 200-person company took 3–5 days to fully onboard a new hire: IT provisioning, HR paperwork, tool access, training assignments, and manager introductions were all manual, uncoordinated, and inconsistent. Compliance documentation often missed, causing audit risks.

The Solution

Single-trigger onboarding: when an offer is signed in DocuSign, a coordinated workflow fires simultaneously — IT receives provisioning checklist, HR gets compliance docs, a personalized welcome email + Slack message goes to the new hire, training modules are auto-assigned, a 30/60/90 day check-in calendar series is created, and the manager gets a daily reminder sequence until all tasks are complete.

Business Impact

• ⏱️ Onboarding time reduced from 5 days to same day
• ✅ 100% compliance documentation completion (was 71%)
• 😊 New hire satisfaction scores increased 38%
• 🧑‍💼 HR team saves 12 hours per new hire