Segmentation, inference, fine-tuning, embeddings, molecular modeling, docking, and batch analysis.
Compute
On-demand infrastructure for clinical AI and research.
Sarovi Compute is the infrastructure layer behind the workspace: GPU bursts, CPU and memory-heavy analysis, private model serving, databases, storage, simulation, bioinformatics pipelines, and governed research environments.
Operating model
Healthcare should not wait weeks for a machine.
Many hospitals can buy isolated workstations, but the hard part is making compute available exactly when the clinical or research question appears. Sarovi Compute follows an on-demand model: launch the workload, run the job, keep the evidence, shut down what is not needed.
The model is broader than GPU rental. A department may need fast GPUs for a segmentation model, large-memory CPU nodes for bioinformatics, databases for longitudinal records, encrypted object storage for imaging, or private model endpoints for SaroviX agents.
Bioinformatics, VCF processing, transcriptomics, cohort joins, simulation prep, and ETL.
DICOM, NIfTI, WES/WTS outputs, reports, artifacts, embeddings, and immutable audit evidence.
Structured outputs, vector search, cohorts, timelines, metadata, and data quality checks.
Workloads
Compute sits under the whole care layer.
Imaging AIDICOM/NIfTI preprocessing, segmentation, registration, 3D reconstruction, detection models, and longitudinal comparison.
OmicsWES/WTS alignment, variant calling, annotation, expression analysis, fusion detection, pathway scoring, and re-analysis.
AgentsPrivate LLM inference, retrieval, summarization, clinical drafting, and tool execution close to protected data.
Molecular researchProtein folding, docking, molecular simulation, compound screening, and mechanism exploration.
Data productsCohort search, patient embeddings, longitudinal dashboards, quality metrics, and department-level analytics.
Clinicians and researchers should request analysis from SaroviX, not negotiate hardware access manually every time.
Pricing logic
Usage-based when possible, reserved when necessary.
Departmental pricing should map to actual workload behavior. Some teams need short GPU bursts. Others need predictable monthly blocks, private databases, managed storage, or an isolated research environment. The structure can feel closer to modern serverless GPU platforms than traditional procurement.
GPU-hours, CPU/RAM-hours, storage read/write, and model runtime for jobs such as overnight segmentation or WES re-analysis.
Committed capacity for radiology, oncology, bioinformatics, or research teams that need predictable access and support.
Dedicated storage, databases, vector indexes, model endpoints, audit logs, backup policies, and governance documentation.
Run sensitive workloads where policy requires, while still exposing them through SaroviX with the same workflow layer.
Example units are GPU-hour, vCPU-hour, GB-RAM-hour, TB-month storage, database instance-month, model endpoint uptime, and managed pipeline run. Final pricing depends on clinical data scope, isolation, compliance, and support.
Access path
Run the work from the patient workspace.
For a clinician or researcher, compute should feel like asking SaroviX to do the next piece of work: segment this scan, re-analyze this exome, run this cohort query, prepare this tumor-board packet, simulate this target. The infrastructure can be complex underneath; the access pattern should stay simple.
Selected environment H100/H200 路 private model 路 clinical data store
Evidence captured inputs 路 parameters 路 model version 路 artifacts 路 audit trail
- 01Ask from SaroviX
The request starts where the case already lives: scan viewer, molecular review, patient timeline, or research workspace.
- 02Match the workload
Sarovi routes to the right mix of accelerator, CPU/RAM, database, storage, model endpoint, or approved tool.
- 03Run with provenance
Inputs, model version, parameters, logs, and outputs are recorded so the result can be reviewed and reproduced.
- 04Return as clinical context
Outputs appear as images, plots, notes, reports, structured fields, or Continuum updates inside the same patient workspace.
Governance
Medical compute needs evidence, not just speed.
Every clinical compute environment needs controls: encryption, network isolation, least privilege, access review, audit logs, vulnerability scans, backup and disaster recovery, data-flow diagrams, vendor review, and incident response evidence.
Identity, least privilege, key management, workload isolation, and production access review.
Versioned containers, logs, artifacts, backup/DR evidence, and reproducible execution.
DPIA summaries, data-flow diagrams, architecture diagrams, subprocessor lists, and training evidence.
Make computation available at the moment of clinical need.
Compute is how Sarovi turns the workspace from a record viewer into a living analysis environment.
Discuss compute architecture ->