No-code QLoRA and LoRA training on your own data, plus an on-prem gateway that filters sensitive data before any query leaves the building. Real training, fully on-premise.
Patient (MRN ) reports chest pain since .
Patient reports chest pain. Summarize likely differentials and next diagnostic steps.
A web app for non-ML users: upload, map columns, pick a model and run real QLoRA or LoRA training on efficient small language models. Compare base versus tuned in an evaluation playground, with tuned presets for regulated domains.
Sits between your users and external cloud AI. Locally-run PII and classifier models detect, redact or block sensitive information before any query leaves the building, so teams get cloud-AI capability without leaking confidential data.
A non-expert never touches a config. The specialized models it ships become the domain brains that power task-specific agents in regulated environments.
Reads the dataset and decides the model type and task on its own.
Generates an appropriate QLoRA configuration, no config files by hand.
Launches and supervises training, monitoring live logs and handling failures.
Auto-evaluates the result and surfaces a base-vs-tuned verdict.
Purpose-built for regulated domains, so teams safely use cloud AI while owning private small models, with no ML engineers and no raw data sent to the cloud.
Adapt models on clinical notes without PHI ever reaching the cloud.
Redact contract and case data, then tune private models for extraction.
Keep student-record PII inside the institution while still using AI.
Tell us about your capture program or private-model project. We will walk you through a self-hosted deployment end to end.