Doc-to-LoRAv1.2.0

Law firms can internalize case files, contracts, or legal briefs into the model to enable quick, on-device Q&A without exposing sensitive documents in prompts. This supports paralegals in researching precedents or extracting key clauses securely on a Mac.

Researchers can feed academic papers or reports into the model to create a LoRA adapter, allowing them to ask questions about methodologies or findings without needing the full document during inference. This accelerates literature reviews on personal Apple Silicon devices.

IT departments can internalize product manuals or troubleshooting guides, enabling support agents to query the model for solutions without accessing external databases. This reduces latency and works offline on Mac hardware.

Analysts can process quarterly reports or market analyses to generate adapters, facilitating rapid Q&A on financial metrics or trends without manual retrieval. This aids in decision-making with local, secure inference on macOS.

Medical staff can input clinical guidelines or patient records (anonymized) to create adapters, allowing quick reference for treatment protocols without compromising data privacy. It runs on-device, ensuring compliance on Mac systems.

Offer a cloud-based platform where users upload documents to generate and manage LoRA adapters, with tiered pricing based on usage or document size. Revenue comes from monthly subscriptions for businesses needing scalable document internalization.

Sell enterprise licenses for deploying the skill on internal Mac servers, providing customization and support for large organizations. Revenue is generated through one-time license fees and annual maintenance contracts.

Provide services to integrate Doc-to-LoRA into existing workflows, such as legal or research tools, with training and ongoing support. Revenue comes from project-based fees and retainer agreements for technical assistance.