Professional services for production ML inference

Inference architecture review

A structured assessment of your current or planned inference pipeline — model registry design, endpoint topology, GPU allocation strategy, latency bottlenecks and responsible AI governance gaps. Our platform architects deliver a prioritised roadmap with effort estimates in CAD, aligned to your team's ML operations maturity. Ideal before major generative AI investments or cloud migration initiatives affecting production inference workloads.

Endpoint sandbox provisioning

Spin up isolated inference environments with pre-configured GPU pools, observability hooks and sample model serving endpoints. Sandboxes let your engineering team validate integration patterns, test LLM serving configurations and benchmark latency before committing to production infrastructure. Includes two weeks of platform engineering support and handover documentation for internal AI infrastructure teams.

Latency optimisation sprint

Focused two-to-four week engagement targeting p50 and p99 latency reduction for existing inference endpoints. We profile request paths, evaluate quantisation opportunities, tune batching parameters and reconfigure GPU orchestration policies. Outcomes depend on model architecture and infrastructure baseline — we document measured improvements with before-and-after benchmarks rather than promising universal zero-latency results. Computer vision and NLP model workloads both supported.

Typical sprint deliverables include latency histogram analysis, optimised serving configuration, updated runbooks and knowledge transfer sessions for your DevOps and ML engineering leads. Pricing from C$12,500 based on endpoint complexity.

Model serving cutover

Managed transition from legacy inference infrastructure to InferenceAI-aligned production endpoints. Blue-green deployment planning, canary traffic routing, rollback procedures and post-cutover monitoring for the first 72 hours. Minimises downtime risk during critical model updates — whether deploying new neural network versions or migrating large language models to updated serving runtimes.

Cutover services include stakeholder communication templates, incident escalation paths and compliance documentation for teams operating under Canadian corporate governance requirements.

Observability setup

Configure production inference monitoring dashboards, alerting rules and log aggregation for your model serving stack. We integrate with common observability platforms and establish SLI/SLO frameworks tied to business outcomes — not just technical metrics. Drift detection, error rate tracking and token throughput visualisation for generative AI endpoints included. Foundation for long-term ML operations excellence.

Delivered as a fixed-scope engagement from C$8,200 with optional ongoing platform success review retainer.

Platform success review

Quarterly or bi-annual review of your inference platform health — SLA adherence, cost efficiency, governance compliance and roadmap alignment. Our team benchmarks your operations against industry patterns for real-time AI and edge AI deployments, identifying optimisation opportunities and responsible AI policy updates. Keeps production ML infrastructure aligned with evolving business requirements and regulatory expectations in the Canadian market.