Four phases of custom AI development
Here is exactly what happens after you book that first call and what stays in place long after launch.
Audit
We come to you. We walk your floor, sit with the people who do the work, and ask the questions generic software never asks: the outcomes you need, the hardware you depend on, the rules you answer to, the data stuck in spreadsheets and in someone's head, the edge cases that break every off-the-shelf tool. We call them the million-dollar questions. Missing one is how most projects quietly fail.
Co-build
We build software around how your business actually works with your words, your steps, not a template bent to fit. Often that's an internal app, a client portal or a custom ERP, plus the AI agents that do the routine work inside it: sorting mail, updating records, chasing overdue invoices. It runs on production-grade AWS from the first day: Aurora PostgreSQL across multiple zones, automatic failover, point-in-time recovery, data kept in Europe. The class of infrastructure a bank runs on, sized for you.
Train
While we build, your team learns from doing it. Non-technical staff learn to capture requirements, change the software, and build new things in Studio. Most agencies skip this part; we think it's the most important one. Your people get genuinely capable, and we stay reachable when they want a second opinion.
Stay partners, on your terms
When the work is done, your team can run it. You move from a consulting engagement to a subscription on the OpenKBS AI app builder, the same platform we built it on, and keep building at your own pace. But independence doesn't mean alone: we don't vanish, we don't sit on your data, and we're a message away when you want us. Run it yourself, lean on us monthly, or call us only for the big projects. All three are fine.
