Your Clinicians Already Use AI. Patient Data Shouldn’t Be the Price. With Akasiio, It Never Is.

Hybrid AI uses different models for different tasks. In Akasiio, anything involving patient data stays on private, open-source models in your cloud, with small models answering first and larger models when needed. Tasks with no patient data, such as tracking new guidelines or summarising published research, can use managed models such as Claude, GPT or Grok.

Private-only means clinicians miss out on the most capable models for public research. Managed-only sends patient data to a vendor and bills every page by the token. Hybrid gives each task the right model: confidentiality where it matters, frontier capability where it helps, and the lower cost for each workload.

No. Every request passes a policy check before any model sees it. Anything involving patient data, or where the check is unsure, stays private. You decide which task types may use managed AI at all, and you can switch it off entirely.

In Akasiio, private AI is the part that handles patient data. The models run inside your own cloud account or data centre, under your control, instead of on a vendor’s servers. Akasiio delivers it as an operated platform, so hospitals and clinics get the benefits without building an AI team.

Running clinical AI is far more than provisioning a server with a GPU. It needs infrastructure provisioning as code, model serving, retrieval over your own documents, staging environments, release engineering with evaluation gates and rollback, observability of both the AI applications and the infrastructure, autoscaling, backup and security policy, all kept running every day. Akasiio delivers that complete platform engineering stack, built on XePlatform, inside your own cloud account and operated for you, so a supervised pilot can start within weeks without new hires.

XePlatform, the engine under Akasiio, runs the full stack in your account: managed Kubernetes and node pools, infrastructure provisioning, model serving and scaling, the policy check that routes each request, staging environments, the release pipeline that tests every model against your benchmark, observability for AI applications and infrastructure, backup and recovery, and security policy enforcement. Your team keeps ownership; we keep it running.

Akasiio is designed to support both GDPR in Europe and HIPAA in the United States. Patient data is processed only inside your own account, your organisation remains the data controller or covered entity, and we provide documentation to support your impact assessments and security reviews. Compliance itself depends on how your organisation uses the platform.

Yes. Akasiio runs on the major public clouds in the region you choose, in Europe or North America, or in a private cloud, built on open standards so you can move without rebuilding.

English, plus leading European languages such as Dutch, German, French, Spanish and Italian. Akasiio uses medical speech recognition for English and a multilingual model adapted to medical vocabulary for other languages, validated on your own clinicians’ recordings and running inside your account. Uncertain drug names, doses and dates are flagged for review.

No. Akasiio is clinical decision support with human oversight: a clinician reviews every output. Your organisation defines the intended use of each workflow.

Open-source medical and general models, such as the MedGemma family, run inside your account for all clinical work. New models are adopted only after they pass evaluation on your own cases. Managed models such as Claude, GPT or Grok are used only for tasks with no patient data.

Yes, the open medical models Akasiio uses can process CT, MRI, histopathology and chest X-ray images, including comparisons with prior images. Imaging scenarios need local validation on your own cases before clinical use, and may fall under medical-device rules depending on intended use, so we start them as supervised evaluations.

Any specialty can start with protocol questions, lab report extraction, pre-visit summaries and dictation. Specialty scenarios cover radiology, pathology, dermatology, ophthalmology, internal medicine and intake or triage, each grounded in your own protocols and criteria.

A fixed platform subscription plus your own cloud costs, with no per-token fees for clinical work. Managed AI is billed per token only for the tasks you route to it, under a monthly cap. Small models first, capacity that follows clinic hours and shared hardware keep the cost of each answer low. Our break-even calculator shows when owning capacity becomes cheaper than paying per use.

The environment is set up in hours and your knowledge loaded within days. A supervised pilot with your clinicians typically runs about four weeks, while your privacy team completes its assessment in parallel.