AI Automation
Turn manual, repetitive work into reliable AI-driven workflows.
Ship AI automation systems that combine LLMs, APIs, and n8n workflows to remove manual work — from lead intake and content to reporting and internal ops.
Problems I solve
- Team drowning in manual, repetitive tasks
- Data scattered across disconnected tools
- No AI adoption despite clear opportunities
- Reports built by hand every week
What you get
- End-to-end AI workflows orchestrated with n8n
- LLM-powered decision points (OpenAI, Claude)
- API integrations across CRM, email, databases
- Human-in-the-loop review for sensitive steps
Use cases
Lead qualification & routing
Inbound leads scored by an LLM against ICP criteria and routed to the right rep in Slack + CRM.
Content generation pipeline
Briefs → drafts → SEO checks → CMS publish, with editor approval before go-live.
Weekly reporting automation
Data pulled from GA4, Stripe, and HubSpot, summarized by an LLM and delivered as Slack + PDF.
Support ticket triage
Categorize, prioritize, and draft replies for CS agents to review and send.
Examples I've shipped
Inbox → CRM → Slack
Outcome — Auto-tagged inbound leads with next-best action within 30 seconds.
Meeting notes → Notion
Outcome — Structured summaries, action items, and CRM updates after every call.
SEO article factory
Outcome — 12 SEO-optimized articles/week from a single keyword sheet.
Case study
Challenge
Ops team spent 20+ hours/week qualifying, enriching, and routing 400+ weekly inbound leads across email, forms, and Intercom.
Approach
- Unified all inbound sources into a single n8n webhook
- LLM scoring against ICP + intent signals
- Clearbit enrichment + HubSpot dedup + Slack routing
- Human review UI for edge cases via a lightweight admin
Results
"We reclaimed a full FTE of work and our SDRs now only see leads worth calling. Islam shipped it in 4 weeks."
How I work
- 1
Discovery
Map current workflows and identify high-ROI automation candidates.
- 2
Architecture
Design the workflow, choose models, plan integrations and guardrails.
- 3
Development
Build in n8n with proper error handling, logging, and retries.
- 4
Testing
Validate against real data, edge cases, and failure modes.
- 5
Deployment
Ship to production with monitoring and version control.
- 6
Training
Hand off with documentation and team training.
Deliverables
- Production-ready n8n workflows
- Architecture diagram and documentation
- Runbook for on-call and maintenance
- Team training session
Benefits
- Hours of manual work removed each week
- Fewer human errors on repetitive tasks
- Faster response times to customers
- Team focused on high-value work
Frequently asked questions
How long does an AI automation project take?+
Most projects ship in 2–6 weeks. A simple one-workflow integration takes 1–2 weeks; multi-step, multi-system automations take 4–6.
Can you integrate with our existing tools?+
Yes — n8n has 400+ native integrations, and I add custom REST/webhook nodes for anything not covered.
Do you provide maintenance?+
Yes, optional monthly retainers cover monitoring, workflow updates, and adding new steps as your process evolves.
How do you handle sensitive data?+
Data stays in your environment where possible, secrets live in a managed vault, and every workflow has audit logging.