Digital Maker runs 54 AI agents in production internally — and the count grows daily. The same systems we build for clients are running in our own acquisition, project work and reporting.
We know it from daily operations — with everything that comes with it: the moments an agent works perfectly, and the moments it almost does. This knowledge flows directly into client projects. We've already made the mistakes ourselves. Not on your dime.
Incoming requests get contextualized automatically — industry, company size, identifiable need, fit with our core services. No manual CRM entry. No lost information between first contact and first conversation.
Project documentation emerges in parallel — not after the fact. Meeting transcripts get structured, decisions contextualized, open items land automatically where they belong. No "I'll write that down quickly." It's already written.
No manual collecting of numbers. The stack reports itself — project status, utilization, open items, next steps. Gee steers the company instead of managing it.
Whoever thinks of agents as "digital employees" builds themselves disappointment. Whoever thinks of them as surgical tools builds themselves an advantage.
We've experienced both ourselves. That's why we know how to avoid them.
We exclusively use tools we can explain publicly — and everything we explain, we can also build. No no-code tools, no vendor lock-in. If you want to stop working with us tomorrow, the entire code, infrastructure and knowledge belong to you.
What everything runs on. Models, infrastructure, data stores.
What our agents can read, write and control — via Model Context Protocol.
Tools for code, deployment and our own agents — Hermes and Paperclip.
When we build an AI agent for you, we don't just bring technical know-how. We bring operational experience — from a company that works with it daily. That's the difference between a system that can be demonstrated — and one that runs.