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Most AI deployments rot in production — whether your team built them or a vendor did. Models drift, prompts go stale, costs creep, and the platform team that owns it gets pulled to the next thing. Maverin runs it forward, regardless of who shipped v1.
We audit what you already shipped — architecture, prompts, evals, observability, and the cost model — and document the gaps. We take the on-call within 30 days: one rotation, one runbook, one phone number across the whole AI surface. Then we operate and improve — drift monitoring, eval-set re-runs, prompt and model updates, token-cost optimisation. No part of your stack is off-limits; we coordinate with your platform team for upstream issues.
We slide straight from an Implementation Services engagement into the run phase — no handover scar tissue. The team that shipped v1 is the team that runs it: no re-discovery, no onboarding tax. The runbook is written as we build, so 24×5 coverage starts the day you go live — same on-call, same retainer, day one. The roadmap keeps moving with continuous improvement on what we shipped.
Ongoing ownership under a single monthly retainer. Uptime — SLA defined per engagement and reported monthly. Drift — drift monitoring, eval-set re-runs, and quarterly model-refresh windows. Cost — token-cost optimisation and provider routing tuned weekly, with cost and drift dashboards you can see. One new workflow per month against a shared, versioned roadmap.
Service levels are contractual, not best-effort: response, resolution, and new-workflow commitments in writing every month, plus a quarterly business review against the shared roadmap. A 90-day handoff window is built into every contract — we document, train, and walk away cleanly. We have never had to enforce it; clients renew.
Production ownership across agentic surfaces, MRM-aligned change control, and audit-ready monthly evidence packs. Quarterly regulator-friendly review.
Change windows are conservative. We pre-stage releases inside windows your change board already approves.
End-to-end rollout — integrations, data plumbing, observability, validation harness, and rollback.
Production-grade agents and workflows on your stack — harness, orchestration, tools, MCP, and evals.
Threat modelling, data-loss prevention, model risk, and audit trails for LLM and agent-based systems.