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Private AI and inference systems

Task quality, execution boundaries and the operating workload are considered together in deployment decisions.

Discuss the programme family
Specialist engineering programme
Organisation boundaryRecordsOnsite modelReviewMission and domain requirements

Select the right programme within private ai and inference systems.

Capability dossier

The programme family

Task quality, execution boundaries and the operating workload are considered together in deployment decisions. Softinator connects specialist contribution to the engineering, information and operating requirements around the task.

Choose a focused engagement

The catalogue includes private model deployment, sovereign ai architecture, on premises inference, air gapped ai deployment, small model inference optimisation. Each programme has its own contribution, artefacts and acceptance scope. Use the programme links below to identify the relevant engagement.

Commission the work

Bring the operating task, selected environment and result that matters. The engagement establishes scope, access, delivery responsibilities and the evidence used to assess the outcome. Connected platform and project pages provide the relevant context.

Choose the programme.

AI infrastructure cost optimisationAssess compute and serving tradeoffs against workload and quality requirements.Air gapped AI deploymentPrepare offline model execution and controlled update paths for isolated environments.Edge AI deploymentPackage and operate model tasks on selected edge compute devices.GPU cluster integrationConnect model workloads to distributed GPU scheduling, storage and network resources.GPU inference optimisationProfile and tune inference workloads against defined serving objectives.Inference gateway engineeringProvide governed model access, routing and usage controls for enterprise applications.Model deployment migrationMove existing serving workloads to a reviewed deployment architecture.Model quantisation engineeringAssess reduced precision serving against task quality and hardware constraints.Model serving reliabilityEngineer rollout, monitoring and recovery for dependable model endpoints.Multi model servingHost and route multiple models according to task, capacity and operational needs.On premises inferenceIntegrate model serving with enterprise compute, network and operating requirements.Private model deploymentDeploy models inside a defined enterprise boundary with operational integration.Small model inference optimisationTune specialised model serving to device capacity and response requirements.Sovereign AI architectureDesign control over model execution, data location and deployment dependencies.

Follow the connection.

Explore the method, its context
and the engineering underneath.

Our work

Regulated private model adaptation

Private adaptation programmes for regulated records, with domain evaluation and release decisions grounded in the client’s boundary.

Our work

Sovereign mission model deployment

Compact onsite-model programmes that connect domain behaviour, isolated deployment and accountable operator control.

Trust centre

Enterprise procurement

Commercial scope, technical requirements and the evidence needed to commission the work.

Bring the next
requirement.

Discuss your programme