Ertas for Healthcare

The Challenge

Healthcare organizations sit on vast amounts of unstructured clinical data — physician notes, discharge summaries, radiology reports, and patient communications — yet extracting actionable intelligence from this data remains painfully difficult. Generic large language models hallucinate medical terminology, misinterpret clinical abbreviations, and lack the nuanced understanding of disease pathways that clinicians rely on every day.

The regulatory landscape makes the problem even harder. HIPAA, HITECH, and emerging state-level privacy laws impose strict requirements on how protected health information (PHI) is stored, processed, and transmitted. Sending patient data to third-party API endpoints is a non-starter for most compliance teams, and the lack of on-premise or private-cloud inference options from major AI providers forces hospitals and health-tech companies into an impossible tradeoff between innovation and compliance.

The Solution

Ertas gives healthcare teams a complete pipeline for building domain-specific AI models without exposing PHI to general-purpose third-party APIs. With Ertas Studio, clinical NLP engineers can fine-tune foundation models on de-identified datasets using LoRA adapters on Ertas's optimised managed cloud, producing lightweight models that understand ICD codes, SNOMED CT concepts, and clinical shorthand with high fidelity. Ertas Vault ensures that all training data is encrypted at rest and in transit, with configurable retention policies aligned to your compliance posture.

Once a model is ready, it can be deployed as a private inference endpoint on your own infrastructure — inside your VPC, on-premise cluster, or via Ertas Cloud — with audit logging and role-based access controls. Inference data stays within your network perimeter, and Ertas Vault guarantees that model weights and logs are handled according to your organization's privacy policies. The result is production-grade clinical AI that your CISO and compliance officer can actually approve.

Key Features

Example Workflow

A hospital's clinical informatics team exports 50,000 de-identified radiology reports as a JSONL dataset and uploads them to Ertas Vault, which automatically scans for residual PHI and flags any records that need further scrubbing. Once the dataset is clean, the team opens Ertas Studio, selects a BioMistral-7B base model from Hub, and launches a LoRA fine-tuning run targeting radiology impression generation. After two hours of training on Ertas's managed cloud, the adapter is merged and published to an internal model registry. The team then deploys the model to an on-premise server inside the hospital network as a private REST endpoint accessible only from the EHR integration layer. Within a week, radiologists are using the model to draft preliminary impressions, cutting report turnaround time by 40% — with all inference running locally so patient data never leaves the hospital network.