Case Studies

Work we've built.
Results that are real.

Every project below started with a business running on manual effort. We mapped the friction, built the automation, and handed back the time. Here's how.

Recruitment & Staffing
n8nAI (Claude)Google SheetsSMTPCalendly API

Automated Candidate Pipeline for a Growth-Stage Recruiter

A fast-growing recruitment firm was drowning in CV reviews, interview scheduling, and candidate follow-ups — all handled manually by a team already stretched thin.

The Problem

Consultants were spending 3–4 hours daily on repetitive candidate comms: acknowledging applications, chasing interview confirmations, sending prep materials, and following up with no-shows. High-value candidates were falling through the gaps because nobody had time to nurture them.

What We Built

  • AI screening layer that reads incoming CVs and scores candidates against role criteria before any human reviews them
  • Automated acknowledgement and status update sequences — every candidate kept informed at every stage
  • Interview scheduling workflow: self-book links, confirmation, prep guide, and day-of reminder — all triggered automatically
  • Post-placement follow-up sequence for both client and candidate, feeding a referral and repeat-placement engine

Results

4 hrs

daily admin eliminated per consultant

60%

faster time-to-interview

placement capacity, same headcount

+31%

candidate satisfaction score

E-Commerce & Retail
n8nShopify APIAI (Claude)Klaviyo APISMTP

Post-Purchase Journey & Customer Win-Back Automation

A DTC brand with strong acquisition but poor retention. Most customers bought once and never returned — not because they were unhappy, but because no one followed up.

The Problem

The team was running paid ads effectively but had no post-purchase communication beyond a generic order confirmation. Repeat purchase rate was low, the review pipeline was non-existent, and lapsed customers were never re-engaged. The brand was leaving significant revenue on the table.

What We Built

  • Post-purchase journey: order confirmation → delivery update → usage tips → review request → loyalty offer — all timed to the product cycle
  • Segmented win-back sequence for customers inactive at 60 / 90 / 120 days, with escalating incentive logic
  • Review and UGC collection flow triggered 7 days post-delivery, with social sharing prompt and referral reward
  • AI-personalised product recommendation emails based on previous purchase history and browse behaviour

Results

+44%

repeat purchase rate in 90 days

3.8×

ROI on win-back campaign

220+

new verified reviews in 60 days

Zero

additional headcount required

Professional Services
n8nApollo APIAI (Claude)SMTPGoogle Sheets

AI Outbound Agent for a B2B Advisory Firm

A specialist consultancy with a strong track record but no repeatable process for finding new clients. Business development relied entirely on referrals and the founder's network.

The Problem

The firm had a clear ideal client profile but no system for reaching them. Lead research was done manually when time allowed — which was rarely. Outreach was sporadic, untargeted, and not tracked. There was no way to know what was working or improve over time.

What We Built

  • AI agent that continuously sources and scores leads against ICP criteria using live company data
  • Personalised first-line generation — each outreach email references something specific and relevant to the prospect
  • Multi-touch sequence with adaptive follow-up logic — timing and tone adjusted based on open and click behaviour
  • Self-learning loop: weekly agent review of campaign performance, updating scoring and messaging parameters autonomously

Results

qualified pipeline in 90 days

42%

average email open rate

10 hrs

per week recovered

Zero

manual lead research required

SaaS & Technology
n8nAI (Claude)Stripe APISMTPSlack API

Customer Onboarding Automation to Cut 30-Day Churn

A SaaS platform with solid acquisition numbers but a leaky funnel. Users were signing up and going quiet — churning before they ever saw the product's value.

The Problem

The onboarding experience was a single welcome email and a static knowledge base. No guidance, no check-ins, no alerts when users stalled. The success team only found out a customer was at risk when they requested a refund or simply disappeared.

What We Built

  • Behaviour-triggered onboarding sequences — different paths for users who complete vs. skip key setup steps
  • In-app milestone detection feeding automated nudge emails when users stall on critical actions
  • Success team alert system — flagging at-risk accounts based on login frequency and feature adoption scores
  • 30-day check-in call automation: self-book link, agenda pre-fill, post-call follow-up with resource pack

Results

35%

reduction in 30-day churn

core feature adoption rate

48%

fewer support tickets in month one

+22 pts

NPS improvement at 60 days

Property & Real Estate
n8nAI (Claude)Rightmove APISMTPGoogle Sheets

Lead Nurture & Viewing Pipeline for a Property Agency

A busy estate agency was losing buyers and vendors to competitors — not because of price or service, but because enquiries from portals were going unanswered for hours.

The Problem

Agents were manually responding to portal leads between valuations and viewings. Response time averaged 4+ hours. Many leads went cold before anyone spoke to them. There was no structured follow-up sequence and no visibility on which leads were warm vs. ready to act.

What We Built

  • Instant portal lead capture with automated first-response within 90 seconds — personalised to property and enquiry type
  • Qualification sequence that segments buyers by timeline, budget, and intent before any agent time is spent
  • Viewing booking workflow: self-schedule link, confirmation, property info pack, and day-of reminder
  • Post-viewing follow-up and offer stage nurture sequence — keeping interested parties warm through the sales process

Results

90 sec

average first-response time

+40%

viewings booked from same enquiry volume

20%

increase in enquiry-to-offer rate

3 hrs

per day returned to each agent

Hospitality & Venues
n8nAI (Claude)OpenTable APISMTPGoogle Sheets

Reservation Management & Guest Loyalty Automation

A multi-site hospitality group was losing revenue to no-shows and repeat visits that never happened — because nothing was in place to confirm bookings or bring guests back.

The Problem

Booking confirmations were sent manually when staff had time. No-show rates were high and unpredictable. Post-visit, guests received nothing — no review request, no return offer, no recognition for regulars. Each site operated in isolation with no shared guest intelligence.

What We Built

  • Automated booking confirmation sequence with 48-hour and 2-hour reminders, reducing no-shows without staff involvement
  • Post-visit review request triggered 3 hours after reservation end time — platform-specific links based on guest profile
  • VIP guest recognition system — flagging regulars for personalised treatment on next visit and triggering exclusive return offers
  • Cross-site guest data layer — unified view of visit history, spend, and preferences across all locations

Results

65%

reduction in no-show rate

150+

new reviews across sites in 90 days

+28%

repeat visit rate in 6 months

Unified

guest data across all locations

Media & Content Production
n8nAI (Claude)Google Drive APISMTPAirtable

End-to-End Production Workflow for a Content Studio

A growing content production studio managing multiple client projects simultaneously — all tracked through email threads, shared spreadsheets, and memory. Nothing was scalable.

The Problem

Project briefs arrived in different formats. Approval loops ran over email with no clear status. Asset delivery was ad hoc. Clients chased for updates. Internally, the team had no reliable way to know what was in progress, what was blocked, or what was overdue without a manual check-in.

What We Built

  • Structured project intake: standardised brief form that populates a live project tracker automatically on submission
  • Milestone-based notification system — client and internal team both kept informed at each production stage without manual updates
  • Approval workflow with automated reminders if sign-off isn't received within the agreed window
  • Asset delivery and feedback collection sequence — version tracking, revision requests, and final sign-off all handled in one flow

Results

50%

reduction in project admin time

3 days

faster average approval cycle

Zero

projects missed without a status update

+35%

client satisfaction on project clarity

AI Chatbot
AI (Claude)n8nWebhookSMTPGoogle Sheets

24/7 AI Sales & Support Chatbot for a Service Business

A service business losing leads every evening and weekend — enquiries landing outside office hours with no response until the next working day. By then, most had moved on.

The Problem

The sales team was first point of contact for every inbound enquiry — qualifying, answering questions, and booking consultations manually. Out-of-hours leads received an automated 'we'll be in touch' reply and no follow-through until the next morning. Support tickets for common questions were eating into time that should have been spent on growth.

What We Built

  • AI chatbot embedded on the website — trained on the business's services, pricing, FAQs, and objection responses
  • Lead qualification flow: the bot asks the right discovery questions, scores intent, and routes hot leads to an immediate booking link
  • Out-of-hours enquiry capture — bot collects full context and notifies the team with a briefed lead summary ready to action
  • Support knowledge base integration — handles 80% of common questions without human involvement, escalating only complex cases

Results

24/7

lead capture — including out of hours

63%

of support queries resolved without human

more consultations booked per week

Zero

missed enquiries from outside office hours

AI Voice Agent
Vapi.aiAI (Claude)n8nGoogle Calendar APITwilio

AI Voice Agent for Inbound Calls & Appointment Booking

An appointment-based business missing calls during busy periods and after hours. Every missed call was a missed booking — and the team had no capacity to call back consistently.

The Problem

The front-of-house team was handling calls, managing walk-ins, and running operations simultaneously. Calls during peak hours rang out. After-hours calls went to voicemail and were rarely followed up. Booking required a human to check availability, confirm the slot, and send a reminder — a process that took 8–12 minutes per appointment.

What We Built

  • AI voice agent that answers every call in under 2 rings — introduces itself, understands the caller's need, and handles the full booking conversation naturally
  • Live calendar integration — agent checks real-time availability and confirms slots without human involvement
  • Post-call automation: booking confirmation SMS and email sent instantly, with 24-hour and 2-hour reminders
  • Escalation logic — agent recognises complex or sensitive calls and transfers to a human or flags for urgent callback

Results

Zero

missed calls during business hours

8 min

booking time reduced to under 60 seconds

+34%

appointments booked in first 30 days

After-hours

bookings now captured automatically

Cold Outreach Infrastructure
n8nAI (Claude)Apollo APISMTPGoogle Sheets

AI-Personalised Cold Email System Across Multiple Domains

A B2B company needed a scalable, deliverable outbound engine — not a spray-and-pray blast, but a system that sent the right message to the right person with context that felt genuinely researched.

The Problem

Previous cold outreach had poor deliverability and generic messaging. Open rates were low, reply rates were near zero, and the sales team had no confidence in the channel. The business needed an infrastructure that could operate at scale without sacrificing personalisation — and without landing in spam.

What We Built

  • Multi-domain sender infrastructure across several warmed domains with rotating inboxes — protecting deliverability at volume
  • AI research layer that pre-processes each lead: company context, role signals, trigger events, and a personalised first line written before sending
  • Two-track campaign system — deep personalised sequences for high-value targets, lighter templated sequences for broader prospecting
  • Automated follow-up cadence (Day 3, 7, 14) with reply detection to pause sequences the moment a lead responds
  • Performance tracking layer comparing open rates, reply rates, and positive response rates by domain, sender, and sequence type

Results

3,175

targeted leads across 4 domains

42%

average open rate on personalised sequences

12

sender inboxes with maintained deliverability

A/B

tested: deep vs standard personalisation

Multi-Agent AI Systems
n8nAI (Claude)TavilySMTPGoogle Sheets

Multi-Agent Business Intelligence System for Market Research

A business development team spending 2–3 days manually compiling research before every major pitch, partnership discussion, or market entry decision. The process was slow, inconsistent, and entirely dependent on one analyst.

The Problem

Competitive intelligence, customer research, market sizing, and financial signals all lived in different places and required different expertise to interpret. The team had no repeatable system — each research project started from scratch. Speed was a competitive disadvantage.

What We Built

  • Orchestrator agent that receives a single research request and deploys four specialist AI agents simultaneously — each expert in its domain
  • Market Research Agent: market size, growth trends, emerging opportunities, and regulatory signals
  • Competitor Analysis Agent: competitor positioning, recent moves, pricing, and weaknesses
  • Customer Intelligence Agent: buyer personas, pain points, purchase triggers, and community sentiment
  • Synthesiser layer: orchestrator collects all findings and produces a single structured intelligence report, emailed within minutes of request

Results

2 days

of research compressed to under 8 minutes

4

specialist agents running in parallel

Repeatable

research process — no analyst dependency

100%

of briefs prepared before the first meeting

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