Case Studies
Real systems.
Measured results.
01
Consultant
02
Workflow Architect
03
AI Automation
Real service businesses. Real operations rebuilt around automation and AI. Every number below reflects a real system build — not a pitch.
Case Study 01
SYSTEMS CONSULTANT & AUTOMATION ARCHITECT
AI Real Estate Lead & Property Matching System
A real estate business was managing property enquiries manually, leading to delayed responses,
missed opportunities, and inefficient tracking of buyer and seller interactions.
- Manual handling of property enquiries resulted in no structured way to match buyers with available properties.
- Lack of centralised lead tracking
- Inefficient communication between team and prospects
- Captured enquiries automatically via structured intake, then stored and organised property and lead data in a central system
- Matched buyers to relevant properties using AI analysis and notified agents instantly with qualified opportunities.
- Logs and tracks interactions for ongoing visibility
Technology Stack
Webhook
Gmail
AI Agent
Airtable
Gemini
Architecture Summary
Real n8n build: webhook intake → AI agent (Google Gemini) → Airtable property search → structured response →
logged interaction summary.
Case Study 02
SYSTEMS & WORKFLOW ARCHITECT
Automated Client Onboarding & CRM System
A service business was onboarding clients manually, causing delays, inconsistent experiences,
and unnecessary workload for the team.
- Slow onboarding process caused by repetitive manual admin tasks.
- Inconsistent client experience
- Lack of visibility in CRM stages
- Captured and validated client information on submission, automatically creating and structuring CRM records.
- Sent internal team notifications with key details and delivered welcome emails and onboarding resources.
- Triggered timed follow-ups and engagement sequences while updating onboarding status in real time.
Technology Stack
HubSpot
Jotform
Telegram
Gmail
Architecture Summary
n8n build: Jotform submission → HubSpot contact created → team notified → timed welcome/follow-up sequence
over 2 hours–2 days → CRM status updated throughout.
Case Study 03
SYSTEMS CONSULTANT & AI WORKFLOW DESIGNER
AI Customer Feedback Classification & Routing System
Customer feedback was being collected but not processed efficiently, leading to slow responses
and missed insights.
- Unstructured feedback data
- Delayed response to customer issues stemmed from having no clear routing to relevant teams.
- Limited ability to identify trends
- Automatically classified feedback by type and category and structured data for easy tracking and analysis.
- Routed feedback to the appropriate team instantly
- Updated records with categorised insights
Technology Stack
Gmail
Gemini
Google Sheets
AI Agent
Architecture Summary
Real n8n build: Google Sheets trigger → AI classification (type + area) → standardised labels → routed to kitchen/
delivery/service team by email.
Case Study 04
SYSTEMS CONSULTANT & AUTOMATION ARCHITECT
AI Voice Intake & CRM Automation System
A professional services consultancy relied on an AI voice agent to handle inbound discovery calls, but had no reliable
way to capture outcomes, sync booking status, or keep a single source of truth for leads across calls and follow-ups.
- Post-call outcomes (booking status, lead details, summary) were not captured in any structured, reliable format
- No duplicate-checking meant repeat callers created cluttered, disconnected CRM records
- Field mapping gaps between the call platform and CRM caused booking status to silently fail to sync
- Automated post-call webhook captures transcript summary, contact details, and booking outcome the moment a call ends
- AI-assisted parsing extracts structured lead data and reliably distinguishes new leads from returning ones
- Existing-record lookup ensures updates land on the correct CRM entry instead of creating duplicates
Technology Stack
Webhook
ElevenLabs
Conversational AI
Notion
OpenAI
Architecture Summary
Real n8n build: post-call webhook → field normalization → AI lead extraction → existing-record lookup → CRM create/update
with synced booking status.
Case Study 05
SYSTEMS CONSULTANT & AUTOMATION ARCHITECT
WhatsApp Conversational Lead Qualification System
A B2B services business wanted to offer WhatsApp as a contact channel without sending visitors off-site to a third-party app, and needed replies handled by an AI agent grounded in the business’s own knowledge base rather than
generic scripted responses.
- Linking out to WhatsApp caused visitors to abandon the site before making contact
- Business-initiated WhatsApp messages require Meta-approved templates, an easy-to-miss compliance step
- Inbound replies had no connection to a knowledge base, session memory, or CRM
- Website form submissions trigger an approved WhatsApp template message, keeping outreach compliant without leaving the site
- An AI consultant agent, grounded in a live knowledge base and persistent conversation memory, handles all replies
- Every conversation and lead outcome logs automatically to a shared CRM alongside voice and SMS leads
Technology Stack
Meta
WhatsApp Business
Cloud API
Notion
OpenAI
Architecture Summary
Real n8n build: form webhook → approved template send → inbound reply webhook → AI agent with knowledge retrieval and
memory → CRM logging.
Case Study 06
SYSTEMS CONSULTANT & AUTOMATION ARCHITECT
SMS Conversational Lead Qualification System
The same business wanted SMS as a lightweight, high-open-rate first-touch channel for inbound leads, with replies
handled conversationally rather than routed to a shared inbox.
- No automated first-touch SMS outreach existed from website form submissions
- Inbound replies had no session tracking, so the AI agent couldn't hold a coherent conversation across messages
- Rich-text formatting reused from other channels broke on SMS, producing unreadable messages with raw markdown syntax
- Website form submissions trigger an instant, personalized SMS from a dedicated business number
- Inbound replies are handled by an AI consultant agent with conversation memory keyed to the sender's phone number
- Message formatting is normalized to plain text for SMS, with clean, tappable links instead of broken markdown
Technology Stack
Twilio
Notion
OpenAI
Architecture Summary
Real n8n build: form webhook → Twilio SMS send → inbound reply webhook → AI agent with session memory and knowledge
retrieval → plain-text formatted reply → CRM logging
Aggregate Impact
Across every engagement, one pattern.
Fewer handoffs. Shorter cycle times. Same headcount doing dramatically more work.
80%
Reduction in manual work
3x
Faster Onboarding
↑
Improved lead handling & follow-up consistency
●
Real-time operational visibility across systems
Want results like these on your operation?
Every case study started with a system audit. Book yours and we’ll map the bottlenecks costing you the most this quarter.
