Model Ops Retainer. @model-ops-retainer

The failure mode for GenAI in production is rarely a bad build — it is abandonment. Models update underneath you, prompts drift, retrieval corpora go stale, and token costs creep. The Model Ops Retainer is an SLA-backed subscription that treats ongoing model monitoring, prompt optimization, RAG corpus refresh, and version migration as first-class operations, not favors from the original build team.

The retainer covers ML and LLM systems alike, model-agnostic across OpenAI, Anthropic Claude, Google Gemini, Meta Llama, and Mistral — with hallucination tracking, latency monitoring, and cost visibility running through the SEOP. The result is a production AI system that improves with use, not one that decays after handoff.

Model & Prompt Monitoring

Continuous monitoring of output quality, latency, and error rates, with prompt performance tracked and optimized against real traffic.

Evaluation & Drift Detection

A standing evaluation harness: accuracy and hallucination rates measured on schedule, with alerts when behavior drifts from baseline.

RAG Corpus Refresh

Retrieval corpora kept current on an agreed cadence — new content indexed, stale content retired, retrieval quality measured, not assumed.

Version & Cost Management

Managed migrations when providers update or retire models, plus token cost optimization — right-sizing models against quality requirements.

1. Onboard — System audit: models, prompts, pipelines, and eval criteria baselined; monitoring and evaluation harness wired in.
2. Monitor and respond — Continuous monitoring with SLA-backed response to degradation, scheduled evals, and corpus and prompt upkeep.
3. Report — Monthly reports: quality and drift metrics, cost trends, migrations performed, and recommendations for the next cycle.

The Model Ops Retainer is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

Can you operate a system another team built?

Yes. Onboarding starts with a full audit of the models, prompts, and pipelines in production — the retainer exists precisely for systems whose builders have moved on.

What metrics does the retainer track?

Accuracy and hallucination rates from the evaluation harness, latency, error rates, retrieval quality for RAG systems, and token spend — baselined at onboarding and reported monthly against that baseline.

What happens when a provider retires our model?

That's a planned event under the retainer, not an emergency: candidate models are evaluated against your harness, the migration is tested and staged, and the switch ships with before/after eval results.