Private coding assistants and internal AI tools running on hardware you own. Your source code, prompts and outputs stay inside your walls, and your team learns agentic coding workflows that hold up on real work.
Hosted coding assistants send source code and context to a third party. Security and legal reviews stall, or the answer is simply no.
Engineers paste code into public chatbots anyway. Nobody knows what left the building, and there is no policy that people actually follow.
Licences get bought and adoption stays shallow. Nobody has shown the team how to use agents on a real codebase, with real reviews.
Code completion, chat and agentic coding against models hosted on your hardware, behind your firewall.
Search and Q&A over your codebase, docs, runbooks and tickets, without sending any of it to an outside AI API.
Hands-on sessions, playbooks and review practices so agents produce code your team will merge. See Agentic Engineering Enablement.
Two defined offers. Each starts with a free 30-minute call.
For: Engineering leaders who want AI coding help but can't send code outside.
For: Teams ready to put a private assistant in front of real engineers.
We review your codebase, toolchain, security requirements and hardware, and pick the first use case.
4 to 6 weeks: a private assistant or internal tool running on your hardware, used by one team on real work.
We work with your engineers on agentic coding workflows, prompts and review practices until the habits stick.
You keep the system and the playbooks. Roll out to more teams, or bring us back for the next use case.
No. Models, indexes and logs run on hardware you control, and the system makes no calls to outside AI APIs. If you later choose to download new models or updates, that is a deliberate step you control.
The largest hosted models are still stronger on some tasks. Open models running locally handle a lot of day-to-day coding work well: on our own RTX 3090, 33B and 34B coding models such as deepseek-coder and codellama generate about 41 tokens per second, entirely on the GPU. The Readiness Assessment tests this against your codebase, so you know where the line is before you buy hardware.
Many editor plugins and agent tools can be pointed at a self-hosted model. We check your specific toolchain during the assessment and tell you what works and what doesn't.
It depends on how many people will use it and which models fit the work. Sizing is part of the Readiness Assessment, along with a cost comparison against cloud AI APIs. We use ai-on-a-box, our own tool, to turn a bare machine into a private inference API.
A senior SunFox architect leads every engagement. Our leadership brings experience from Microsoft, Volkswagen, Elektrobit, and Change Healthcare, building platforms used by 100M+ customers.
Book a free 30-minute call. Tell us about your team and your security constraints, and we'll tell you whether private AI coding makes sense for you and where to start.