Below are examples of automation systems we’ve designed and implemented, focusing on real operational problems and long-term reliability.
AI SMS Lead Qualification & Follow-Up Automation
Built a production AI SMS agent that handles inbound leads automatically from the first message to booking.
The agent reads full conversation history, identifies intent (interested, more info, disqualified, human follow-up), and avoids repeating questions already answered. Qualified leads are guided to booking, while disqualified or low-intent leads are handled gracefully without escalation.
The system updates CRM records in real time, logs conversation context, and notifies sales only when human action is required.
Focus: AI agents, SMS automation, intent detection, CRM updates, and booking workflows.
Internal Operations & Workflow Automation
Built internal automation workflows to replace manual operational processes across forms, notifications, task routing, and reporting.
CRMs were structured with clean stages, status logic, and automation triggers to ensure consistency across teams. Supporting SOPs and system documentation were created to allow non-technical staff to operate and maintain the system without dependency.
The result was reduced manual handling, clearer ownership, and systems that scale without breaking.
Focus: internal workflows, CRM systemization, SOPs, maintainable automation
AI Workflow Audit & Production-Grade Rebuild
Conducted AI and automation audits for existing workflows that were unreliable, over-engineered, or difficult to maintain.
Identified failure points, unnecessary complexity, and logic gaps, then redesigned systems with clear triggers, decision trees, and error handling. Rebuilt workflows focused on reliability, maintainability, and long-term scalability rather than experimentation.
Focus: AI audits, workflow redesign, production reliability, system architecture.
Multi-Stage Lead Follow-Up & Regeneration Automation
Designed a multi-stage follow-up automation to re-engage cold, inactive, or previously unresponsive leads across SMS and email.
The system checks past messages, conversation state, and CRM notes before sending any follow-up to avoid redundancy. Follow-ups are context-aware, time-based, and stop automatically when a lead responds or books.
Leads are scored based on intent and routed accordingly, ensuring sales teams only engage when there is a clear buying signal.
Implementation leveraged a combination of automation platforms, AI agents, and custom logic depending on system requirements.
Focus: lead regeneration, CRM memory, follow-up logic, n8n workflows.
Want to explore something similar?
If you’d like to discuss how automation could improve your operations, we can start with a review and take it step by step.