AI PLATFORM SUPPORT
AI platform operations and support
Your AI platform is now business infrastructure. Give it the service discipline to match.
- Tiered service levels matched to each system's criticality
- SLAs on availability, response, restore, and quality
- A phased transition that keeps the hand-off boring by design
Buy the level the system warrants, not premium across the board.
Not every model deserves the same response clock, and paying premium rates everywhere is its own kind of waste. We map each system to a tier by business criticality, then support it to that standard. This is one arm of AI Managed Services, operationalizing the foundations from MLOps.
Tiers are a starting frame, not a straitjacket. Actual availability, response, and quality targets are agreed per system and written into the SLA. (Targets are illustrative; Softobiz to confirm against your requirements.)
An AI platform can be up and still be wrong.
Support is only credible when the commitments are explicit.
Every engagement is governed by SLAs across four dimensions, not just uptime.
Quality is a first-class SLA because an AI platform can be up and still be wrong, so we hold output to baseline via pipeline monitoring, not just infrastructure health.
Four phases, so the hand-off is boring by design.
Assess
Inventory platforms, dependencies, runbooks, cost profile, and gaps. Exit state: a shared system map.
Instrument
Add monitoring, alerting, and cost observability where blind spots exist. Exit state: full visibility.
Stabilize
Clear the backlog of drift, quality, and reliability issues; agree SLAs. Exit state: signed service levels.
Operate and improve
Steady-state run with proactive tuning and reporting. Exit state: compounding reliability.
We can take on an AI platform built by another team. The previous arrangement stays in place until instrumentation, ownership and runbooks show that the platform is ready to transition.
Availability, updates, and knowledge that does not live in one head.
- Platform availability and incident management to agreed SLAs and severity definitions.
- Updates and dependency maintenance: model, framework, and library upgrades applied safely behind a promotion gate.
- Proactive health monitoring across availability, quality, and cost, so issues are caught early.
- Runbook and knowledge capture so support does not live in one person's head.
- Regular service reviews: SLA attainment, incident trends, and improvement actions reported to owners.
From support scattered across teams to a single accountable owner.
Challenge: Hungry Jack's depended on an AI platform with no defined SLAs and support scattered across teams.
Result: Tiered service levels, a phased transition, and a persistent embedded pod held availability to SLA, cut response times sharply, and gave the platform a single accountable owner. (Softobiz to verify.)
One managed practice, three operating arms.
Pipeline Optimisation and Monitoring
Layered monitoring that holds quality to baseline, the signal behind the quality SLA.
AI FinOps
Inference cost cut to cost-per-task without degrading output.
AI Managed Services
The managed practice this support arm belongs to.
MLOps
The industrialized ML lifecycle this support operationalizes.
Scaled GenAI and AI Platforms
The platform layer these systems are built on.
Dedicated Teams
The persistent embedded pod that runs your platform with you.
What platform owners ask us first.
Yes, it is the common case. We assess the platform, close visibility gaps, then take on support against agreed SLAs through the phased transition above.
By system criticality. We tier each platform, then commit to availability, response, restore, and quality targets appropriate to that tier.
No. AI can be up and still degrade, so quality is an SLA dimension in its own right, monitored continuously alongside availability.
Review your platforms, their criticality, and the service levels they need.
Tiered SLAs, a phased transition, and a persistent pod, so the systems your teams build on stay available and dependable.