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Blueprints, case studies, and practical guides. Everything we publish is based on work we have actually done, with real systems, real constraints, and real results.
Beyond the Template: Automating Payment Advice in Global SSCs
Traditional OCR breaks when layouts change. Our AI-vision approach doesn't. We deployed an n8n orchestration layer that processes vendor remittance in any format - PDF, email body, scanned image - and outputs structured data ready for cash application in the ERP. 90% straight-through processing on day one, no pre-training required.
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Observability for n8n at Scale
Once you have 20+ automations running in n8n, failures go unnoticed until something downstream breaks. We built a watchdog layer: centralized error alerts, execution logging, AI token cost tracking, and automatic Git versioning. The infrastructure that turns experimental workflows into production-grade assets.
AI Triage Rescues the Operations Inbox
A shared mailbox receiving 200+ daily emails - supplier queries, client requests, internal escalations, all in one pile. We built a four-workflow n8n system that categorizes, labels, and drafts responses. Keeping the human in the loop: below 85% confidence, it flags for human review instead of guessing. Result: daily triage dropped from 30 minutes to 10 - a 2/3 reduction in time spent sorting email.
Agentic APEX: Embedding AI Into the Full Development and Support Lifecycle
We built a multi-agent system that resolves Oracle APEX support tickets end-to-end. The AI reads the ticket through a secure API, connects to the client database over VPN, analyses the application export, writes PL/SQL packages, generates step-by-step Page Designer instructions, and verifies the build against the spec. Four specialised agents coordinate the work: architect, database developer, spec writer, and QA verifier. All connections are encrypted; no client data leaves the infrastructure.
AI-Powered IT Support: From Email to Pre-Filled Ticket in Seconds
Support email arrives. The system identifies the sender, checks their service contract, analyses the problem against 10,000+ historical tickets, and opens a pre-filled ticket with a diagnosis and recommended resolution - ready for an agent to review. The entire intake runs in seconds, not the 15 minutes it takes to manually search, categorize, and assign. All data stays on-premise.
Context Engineering: The New Frontier of Variance Analysis
A finance team was spending two days per quarter writing variance commentary. We loaded their budget assumptions, prior period explanations, and regional patterns into an AI context library. Now the AI drafts CFO-ready commentary that references specific drivers - not generic summaries - and the controller's job shifted from writing to reviewing and approving.
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