AI Deployment & MLOps
Production Inference|Measured Capacity|Controlled Model Releases
Sphere 10 takes AI workloads from a working model to an operated service. We package inference, application integration, evaluation and release controls together, with clear ownership of data, compute and production state. The deployment can use managed model services, dedicated GPU infrastructure or a private environment according to your requirements.
An operated AI service includes the business work around the model: knowledge refreshes, unanswered enquiries, pending approvals, booking reminders, incomplete documents, content jobs and order handovers. We make those queues, integration failures and responsible owners visible, so staff can tell whether work completed and what requires attention.
Size the Service Against Real Traffic
Capacity planning starts with model size, context length, output length, concurrency and the response time users need. We measure cold starts, time to first output, sustained throughput and memory pressure using representative requests. GPU selection and quantisation follow those measurements; a model fitting into memory does not establish that it can serve the required load.
For supported open-weight models, vLLM provides an inference serving option with compatible HTTP interfaces. ONNX Runtime can suit supported predictive models embedded in .NET services. We compare those approaches with managed inference, including network placement, model availability, cost and the operational work your team is prepared to own.
The operating baseline includes completed workflows, answer quality, response times, staff handling time and model/provider cost. A fast model is not sufficient if an appointment never reaches the calendar or a customer reply is never sent. We identify the record and confirmation that establish completion for each agreed channel.
Make a Model Release Reversible
Keep Business Changes Controlled
Policies, source documents, product data, prices, booking rules and approved message templates can change independently from a model release. We record who maintains each source and how updates, deletions and permissions reach the served result. Content approvals, document versions and workflow configuration are traceable alongside prompts and model settings.
For communications and campaign production, staff can inspect pending approvals, failed or incomplete jobs, usage and cost, then pause or resume the defined process. Booking cancellations and declined signatures must change their reminders; an already completed order or CRM action must not repeat when a worker recovers.
Connect Experiments to Operations
MLflow can record experiment configuration, metrics and model artefacts. We connect that history to the package actually deployed, rather than treating an experiment dashboard as proof of a production release. Alerts distinguish provider errors, retrieval failures, capacity limits and changes in model quality.
Our .NET application and deployment experience includes Local Notion , where source content, rendered output and served files have distinct verification boundaries. We apply that same discipline to AI: a model saved, a container healthy and a useful response served are separate checks.
The handover names source-content owners, integration accounts, exception queues, failed-import procedures and the route back to the affected record. Knowledge-update monitoring, customer escalations, order recovery and publication state are checked against the useful business result. That operating view is separate from infrastructure health and model-quality telemetry.
Hand Over a Service Your Team Can Run
Acceptance includes controlled overload, interrupted requests, a failed dependency and a rollback to the previous accepted version. We document recovery objectives, backup scope, alert thresholds and who responds to each class of incident. The handover contains build definitions, infrastructure configuration, deployment and recovery runbooks, and measured operating limits. Continuing model updates, performance review and support can be covered by an agreed execution SLA with defined responsibilities.
Project Execution Under an Agreed SLA
After the fixed-price build is accepted, deployment, monitoring, support and ongoing improvement can continue under a negotiated SLA. We agree service coverage, response expectations, ownership of credentials and integrations, source-content responsibilities and who may approve operating changes. Model usage, hosting and third-party subscriptions are identified separately from build and support fees.
A dedicated QA round may be time and materials or fixed price, depending on the engagement. New channels, schedule changes, additional creative formats, model updates and deeper integrations are agreed work, with acceptance and handover appropriate to the change. The SLA states responsibilities and recovery expectations; it does not rely on an unsupported blanket promise about a model or external provider.
Platforms & Tooling
.NET vLLM ONNX Runtime MLflow PyTorch Hugging Face OpenTelemetry NVIDIA CUDA
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