Devolar AI builds private AI infrastructure: self-hosted LLM inference, air-gapped deployments, and local fine-tuning on your own GPUs. Your prompts, your documents, your models — none of it ever leaves your network.
Production LLM serving on your own GPUs. Ollama, vLLM, or llama.cpp behind a hardened reverse proxy — with streaming, quota management, and an OpenAI-compatible API your existing tools already speak.
Air-gapped and network-isolated AI for environments where data cannot leave the building: legal, medical, defence, finance. Container-isolated model runtimes with no outbound connectivity — verified, not promised.
Fine-tune open models on your own data without it ever touching a third-party API. LoRA and QLoRA pipelines, dataset preparation, evaluation harnesses — all running on GPUs you control.
Local speech-to-text with Whisper-class models, multilingual OCR pipelines, and document intelligence — including strong support for Hebrew and RTL languages that cloud APIs often handle poorly.
Every prompt you send to a cloud AI API is a copy of your data on someone else's server, governed by someone else's terms, retained by someone else's policy. For regulated industries and privacy-serious teams, that's not a trade-off — it's a disqualifier. Devolar AI removes the trade-off entirely.
Inference, training, and storage all happen inside your network perimeter. Verifiable at the firewall.
No per-token billing that scales with your success. Own the hardware, amortise the cost, run unlimited inference.
Generic cloud models don't know your terminology, your documents, or your language. Locally trained ones do.
Tell us about your use case, hardware, and compliance requirements — we'll design the deployment.