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New evidence report: AI and machine learning at Port Botany, for Sydney's freight and port operators.Read the report

Services

Model Monitoring & Observability

Models degrade quietly: data shifts, behaviour drifts, latency creeps up. We instrument your models and data end to end, alert the right owner with a runbook, trigger automated remediation where it is safe, and give you dashboards that show model health at a glance.

We put production models under watch — accuracy, drift, bias, latency and usage — with alerts that reach the right person and remediation that can run on its own, so problems surface before they cost you.

What's included

  • Monitoring of accuracy, drift, bias, latency and usage
  • Alerting with automated remediation
  • Observability dashboards and operational metrics
  • Root-cause analysis for model and platform incidents

How it works

A clear path from the first conversation to a system your team runs.

  1. Step 1:

    Define health metrics

    Accuracy, drift, bias, latency and usage thresholds agreed per model, with a named owner for each.

  2. Step 2:

    Instrument models and data

    Inputs, predictions and outcomes captured in production, without slowing the model down.

  3. Step 3:

    Alert and remediate

    Alerts reach the owner with a runbook; safe fixes such as retraining run automatically, with approval.

  4. Step 4:

    Review and tune

    Regular reviews retire noisy alerts and tighten thresholds as you learn how each model behaves.

Technologies we work with

Chosen for your environment: we work in your cloud and with the tools you already use.

MLOps platforms

  • PyTorch
  • TensorFlow
  • Vertex AI
  • MLflow
  • Kubeflow
  • Prometheus
  • Grafana

Languages

  • Python
  • SQL
  • FastAPI

Data engineering

  • BigQuery
  • Snowflake
  • PostgreSQL
  • dbt
  • Airflow
  • Spark
  • Databricks
  • Delta Lake
  • Power BI

In practice

Logistics

Model Monitoring and Drift Detection for an Australian Logistics Operator

AI models ran in production with no monitoring, so problems surfaced only when operations felt them.

Real timeVisibility into model performance

Client engagement · anonymised

Read the case study

Frequently asked questions

What exactly do you monitor?

Input data quality and drift, prediction distributions, accuracy against ground truth when it arrives, bias across key segments, latency, errors and usage.

What happens when drift is detected?

An alert goes to the named owner with a runbook. Where it is safe, remediation runs automatically — for example a retraining and evaluation job — and a person approves the promotion.

Do you monitor LLM applications too?

Yes, with LLM-specific signals such as response quality and hallucination rate. See our Generative AI & LLMOps service.

Which tools do you use?

We work with what you run — Vertex AI and MLflow for models, BigQuery or Snowflake for data, and your existing alerting — and add components only where there is a gap.

Can you help find the cause of an incident?

Yes. Root-cause analysis covers the model, the data and the platform, and ends with a fix and a check that stops the same issue coming back.

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