AI Reliability & MLOps Services for Real Estate.

AI reliability and MLOps services are engineering engagements that build the production layer beneath AI workloads - evaluation suites, observability, drift detection, versioning, deployment automation, incident response, and operational runbooks. They turn a model that "works in the notebook" into a system that operates safely and predictably at production scale. The work is engineering, not consulting.

MLOps is the broader operational discipline (deployment, monitoring, versioning, infrastructure). AI reliability is the specific reliability-engineering layer on top - eval design, drift detection, incident response patterns, and the safety guardrails that prevent production AI from misbehaving. In a mature team, AI Reliability Engineers (sometimes called AI SREs) own the reliability layer while MLOps engineers own the broader platform.

The reliability assessment is a fixed 3-week engagement that profiles your production AI workloads against an MLOps maturity model, identifies the reliability gaps, and produces a prioritized roadmap with engineering effort estimates. Most clients use the artifact to fund a subsequent platform build.

A typical reliability platform build runs 12–24 weeks depending on the number of AI workloads in scope. Eval suites and observability tend to ship in the first 6 weeks because they produce the fastest signal. Drift detection, canary/rollback, and fair housing safety guardrails ship next. Incident runbooks and handoff occur in the final phase.

Yes. Logiciel's reliability practice is stack-neutral. We integrate with Databricks, Snowflake, AWS SageMaker, GCP Vertex, Azure ML, MLflow, Weights & Biases, LangSmith, Langfuse, Arize, Fiddler, and self-hosted patterns. We design the reliability layer to fit the platform you already operate.

Every reliability engagement that touches generation workflows (listing copy, conversational search, agent assistants) includes a fair-housing-aware safety layer - protected-class output filters, language pattern detection, prompt injection defense, and reviewable generation logs. The safety layer is configurable and auditable. We design it with your legal team in the engagement, not retroactively.

The 3-week assessment is a fixed-price engagement in the mid-five figures. A full platform build typically runs in the low-to-mid six figures depending on workload count and stack complexity. MLOps retainers run on monthly pricing scaled to the AI portfolio. We scope and price after a 30-minute discovery call.