GofarAI Labs · private, on-premise AI for US healthcare
On-Premise AI for Hospital Workflows
Deploy open-weight language models inside your hospital network for document processing and other privacy-sensitive workflows. GofarAI combines local infrastructure, model fine-tuning, auditable workflow tools, and human review — without relying on an external model API.
Our core platform, PDF processing workflow, fine-tuning pipeline, and MCP gateway are working prototypes available for hospital pilot evaluation.
Additional workflow modules are available as pilot concepts for co-design.
Llama · Qwen · Gemma · Phi · Mistral · vLLM · LoRA · MCP
What is GofarAI Labs?
GofarAI Labs designs and deploys open-weight AI systems for US hospital workflows. Models run on hospital hardware, with patient data, inference, and audit trails kept inside the hospital’s environment.
The Thesis
Hospitals need control over how AI handles PHI.
Using cloud LLMs with PHI can require approved vendors, business associate agreements, security review, data-governance controls, and ongoing oversight. For hospitals that need tighter operational control, GofarAI offers an on-premise alternative.
We deploy open-weight models inside the hospital environment and tune them for defined workflows. This keeps inference under hospital control, removes the need for an external model API during normal operation, and makes model activity auditable.
The Stack
The full stack, installed on your premises.
Five layers, one deployment. Every layer designed to keep patient data inside your building.
Layer 01
Hardware
We spec, source, and install the server and GPU on premise — or certify hardware you already own.
Layer 02
Platform
Inference server, model manager, audit logging, role-based access, encryption at rest. No external calls.
Layer 03
Models
Tuned open-weight models in the 7B–14B range. Swappable as better open models ship. Department isolation available.
Layer 04
Tools
Modular workflow plugins. Start with one, add more over time. Same server, same models, more value each quarter.
Layer 05
MCP Gateway
Every agent-to-agent interaction inside your hospital is inspectable. Policy enforced. Fully logged.
Tool Modules
Workflows your teams use every day.
Read → extract → classify → draft. Same engine, different prompts. A human approves every output.
PDF Batch Processing
Page-by-page OCR, structure extraction, and summarization across large document stacks.
Fax & Referral Triage
Classify inbound documents, extract key fields, route to the right department queue.
Prior Authorization Drafting
Assemble clinical justification letters from chart notes. Human clinician reviews and signs.
Denial Letter Analysis
Extract denial reason codes, suggest appeal grounds, draft appeals for the revenue cycle team.
Coding Assistance
Suggest ICD-10 and CPT codes from encounter notes for human coders to verify.
Discharge Summary Drafting
Assemble discharge summaries from chart data. Physician reviews and signs off before release.
Chart Summarization
Condense transferred records before appointments so clinicians walk in prepared.
Audit Preparation
Search and compile documentation for Joint Commission and payer audits in hours, not weeks.
Open Source Position
Open where it should be. Closed where it must be.
A clean legal and philosophical line, drawn in the same place every time.
What we plan to open-source
- Training recipes and tuning pipelines
- Evaluation benchmarks for healthcare tasks
- Synthetic and de-identified datasets
- Deployment tooling and platform code
- Base model weights tuned on public data
We never open-source
- Anything trained on real patient data
- Hospital-specific model adapters
- Hospital-specific evaluation results
- Anything that could reidentify individuals
- Weights that ever touched PHI
Security & Auditability
Every inference. Every interaction. Inspectable.
No outbound service dependency during normal operation. Prompts, completions, and tool invocations are designed to be logged inside your building. When other vendors’ AI agents show up in your hospital, the MCP gateway provides a control point for which agent can call which tool, with what data scope, all reviewable.
On narrowly defined workflows, a smaller model tuned and evaluated against local requirements can outperform a larger general-purpose model. We train LoRA and QLoRA adapters on your existing GPUs, overnight or on weekends. Training is designed to occur inside the hospital environment without transferring training data to an external model provider.
Fine-Tuning as a Service
We tune. On your hardware. On your data.
Build the AI substrate for your hospital.
A short call. We map your workflows to a deployment plan and walk through the security model and the pilot plan.