How We Use AI Internally: Eating Our Own Cooking

There's a credibility gap in the AI services industry. Companies selling AI agent development often haven't deployed agents in their own operations. We think that's a problem.

At TGH Tech, every AI capability we offer to clients has been tested internally first. Not as a proof of concept — as a production system that our team depends on daily.

Our internal agent stack includes: a code review agent that pre-reviews every PR before a human engineer looks at it, flagging security issues, performance concerns, and architectural inconsistencies. A proposal generation agent that drafts client proposals by pulling scope, pricing, and timeline data from our project management system. A knowledge base agent that answers team questions by searching across our internal documentation, Slack history, and past project artifacts.

We also use AI-assisted development tools across the team. Every engineer works with Claude Code for code generation, refactoring, and debugging. Our design team uses AI for rapid prototyping and visual exploration. Our ops team uses AI for scheduling, client communication drafting, and resource planning.

The benefit of eating our own cooking is twofold. First, we understand the real-world challenges of AI deployment — not from reading papers, but from experiencing them. Second, when a client asks 'does this actually work?', we can show them our own usage data instead of theoretical projections.

If your AI partner can't show you how they use AI in their own business, ask yourself why.