The honest objection to embedded engineering for smaller companies is cost: a senior engineer is expensive, and a big custom build rarely pencils against a mid-sized budget. We changed what one senior engineer can deliver, not just the price.
Experienced engineers wielding AI
A senior FDE augmented by agentic AI tooling delivers what used to take a small team. That is what puts a premium, US-based engineer within reach of a small or mid-sized business. The differentiator is experience plus AI leverage, not cheap labor and not offshore arbitrage. You get senior judgment moving at machine speed.
Your AI bill should not grow with your success
The industry has settled on metered AI: consumption pricing that turns every successful workflow into a cost that grows with adoption. The more your team uses it, the more you pay. We took a different path. We run open-weight models such as Llama, Qwen, and DeepSeek on owned, on-premise inference hardware, through open agentic platforms. That converts per-token AI cost, which normally scales with every request, into a fixed, owned capability. For you that means no usage meter on your AI, pricing that does not move with the frontier API market, and the option to keep your data on hardware we control.
Your data never has to leave your environment
Because we can run inference on owned, offline hardware, client data does not have to be shipped to a third-party model provider. For regulated and data-sensitive work this is decisive, and it is grounded in real experience: our founder built and ran systems inside US Army NIPRNet and SIPRNet environments and Qlik Alerting into the DoD's ADVANA platform. For everyone else, it is simply a cleaner answer to the question: where does my data go?
This is the position no one else occupies. Offshore shops compete on cheap labor with no AI leverage and your data offshore. Frontier-API competitors carry per-token cost, data egress, and lock-in to a closed model. We are senior US engineers, multiplied by AI, on inference we own.