AI Support & Ops Systems

Every growing business hits the same wall. Support volume climbs faster than hiring can catch up, quality slips, and founders end up back in the queue. AI is finally good enough to change that curve without pretending humans are out of the loop.

I build systems that sit between the helpdesk platform, an LLM reasoning layer, and a Supabase store of product knowledge. Tickets get classified, drafted, and enriched before an agent ever sees them. The human still ships the reply, but they start from a much better first draft.

Average first response time is the single metric that most correlates with support CSAT. Industry benchmarks put a healthy first response at under two hours. Most growing teams sit north of six. An AI triage layer collapses that number without adding headcount.

The cost side is straightforward. A support agent in the UK is around £30,000 all in. An AI triage and drafting layer sits comfortably under 10 percent of that per year for the ticket volume most teams are actually handling.

The pattern I keep landing on is not a fully autonomous agent. It is an assisted operator. Every AI action is logged, every escalation goes to a human, every automated reply is traceable to the prompt version that produced it. That is what makes these systems safe to run in production for months.

For operations that go beyond support (finance workflows, deal sourcing, internal reporting) the same design principles apply. Autonomous where it is boring, human in the loop wherever a mistake is expensive.

An AI powered client support system for an agency that classifies incoming tickets, drafts a suggested response, logs context centrally, and routes anything complex directly to a human. First response times dropped materially without a support team hire.

A deal sourcing operation for an acquisitions team where three specialised agents handle sourcing, broker reply qualification, follow up scheduling, and LOI drafting, all gated by a Slack approval workflow before anything is sent externally.

An email marketing and conversation automation system with a five table data model that separates leads, contacts, conversations, messages, and events, so the system can track full history per contact instead of just whether an email was sent.

This is a common entry point for teams that are curious about AI but not ready to rebuild their product around it. Start where the cost is obvious, prove the pattern, then expand. Most engagements start as a fixed price triage build and become a longer retained ops engagement once the numbers land.