About Gofar Labs
About Gofar Labs
Open-source, on-premise AI shaped for how hospitals actually work — not adapted from consumer tooling, not bolted onto an EHR, and not sending your patients’ data to someone else’s cloud.
What does Gofar Labs do?
Gofar Labs builds open-weight AI deployments for US healthcare organizations. The company focuses on local model deployment, workflow-specific tuning, and practical infrastructure that keeps sensitive healthcare data inside the hospital. The company is Gofar Labs, and its website is gofarai.com.
The Thesis
Hospitals need control over how AI handles PHI.
Frontier cloud models are powerful, but using them with PHI can require approved vendors, business associate agreements, security review, data-governance controls, and ongoing oversight. Hospitals also need systems that fit existing operational and compliance processes.
Open-weight small models — Llama, Qwen, Gemma, Phi, Mistral — can be a practical starting point for narrowly defined hospital workflows. We tune and evaluate the model for a specific job, then deploy the resulting system on hospital-controlled infrastructure.
The result is a stack hospitals can actually use: HIPAA-friendly by architecture, tuned for real jobs, priced for a real hospital budget.
How We Work
Three ways we’re paid. One thing we sell.
A working AI substrate inside your hospital. The commercial structure is straightforward.
01
Install
One-time hardware and platform setup. We spec, source, install, and configure the stack. Your infrastructure. Your control.
02
Subscription
Platform license, model updates as better open weights ship, new tool modules, and support. Recurring. Predictable.
03
Tuning Engagements
Consulting engagements where we tune models on your hardware and your data. Deep, high-margin, and how we get better at the whole business.
What We Believe
Principles.
- Our architecture is designed to keep patient data inside the hospital environment.
- Open-source is a moat, not a marketing checkbox. Recipes and evaluations should be public so hospitals can verify what they’re getting.
- Small tuned models can be effective on narrowly defined workflows when they are evaluated against real requirements.
- Every output has a human reviewer. The model drafts. Your team decides.
- Speed is not the selling point. Privacy, control, and auditability are.
- Hospitals don’t buy models — they buy working solutions. So we ship the full solution.
Founder
Jamal Hajisohn — Founder & CEO
Jamal is an AI engineer and systems architect with over a decade of experience designing, building, and deploying production software and data systems, specializing in local LLM deployment and fine-tuning. His focus is deploying and fine-tuning local, open-weight language models inside privacy-sensitive healthcare environments — the same architecture Gofar Labs brings to hospitals. He works hands-on across the full stack: local LLM deployment, retrieval-augmented generation (RAG), LoRA/QLoRA fine-tuning, and Linux infrastructure. He holds an M.S. in Information Systems from California State University, Los Angeles. Jamal leads Gofar Labs’ technical direction and every engagement; specialized engineering hires are planned as pilots convert.
