Model accuracy degrades as real-world data diverges from training data. Fraud detection that was 94% accurate at launch might be 81% accurate today. A recommendation engine that drove conversions six months ago is now surfacing irrelevant results. You find out when a business metric drops, not when the model starts failing. We build MLOps systems that close the gap between AI deployment and AI maintenance: model monitoring, drift detection, automated retraining pipelines, and experiment tracking infrastructure. Every AI system we build comes with the operational layer it needs to stay accurate.
Recent outcomes
Voice AI · Research
Text-based interviews converted to automated phone calls
6× deeper insightsAI Automation · Ops
Manual invoice OCR across 40+ gas stations
20k+ txns day oneLoyalty · Retail
SuperValu & Centra loyalty platform with receipt validation
1,062 users in 4 weeksSaaS · Logistics
Multi-carrier shipping hub for Indonesian eCommerce
2,000+ shipments yr 1RaftLabs builds MLOps infrastructure for production AI systems including model performance monitoring with business-aligned metrics, data and concept drift detection, automated retraining pipelines triggered by drift thresholds, experiment tracking with MLflow or similar, model versioning and registry, feature store development, and A/B testing infrastructure for model comparison. MLOps engagements are scoped at a fixed price after a discovery phase that assesses your current model infrastructure, data pipelines, and deployment environment.
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A model that was accurate when you deployed it is rarely accurate two years later at the same level. The data changes. Customer behaviour evolves. New product types appear that the model has never seen. Fraud patterns shift. Seasonal patterns create distribution shifts the training data did not represent.
Without monitoring, you find out from the business metric, not the model metric. Conversions drop. Fraud losses climb. Customer complaints increase. By the time the downstream signal reaches you, the model may have been underperforming for months.
MLOps infrastructure catches the degradation at the source.
Capabilities
Continuous tracking of model output quality using metrics tied to your business outcomes, not generic ML metrics that don't map to what the model is doing for your business. For classification models: precision, recall, F1 by class, and confusion matrix tracked over time -- but also the business-specific metric (fraud dollar value caught vs. false positive rate for fraud models; clinical sensitivity vs. specificity for diagnostic models). For regression models: MAE, RMSE, and MAPE, but also the business impact of a given error magnitude (a $50 forecast error on a $100 item is different from a $50 error on a $10,000 item). For ranking and recommendation models: NDCG, click-through rate, conversion lift from recommended items vs. baseline.
Monitoring implementation: Evidently AI or Arize for production model monitoring with custom metric dashboards; alternatively a custom Prometheus metrics layer exporting model output statistics to Grafana. Per-prediction logging to a data warehouse (BigQuery, Redshift, or Snowflake) captures every model input, output, and associated ground truth label (when available) for retrospective analysis and drift computation. Alert thresholds calibrated during deployment using the first 2-4 weeks of production data as the performance baseline: threshold = observed metric × 0.90 for warning, × 0.80 for critical -- not arbitrary values set before production data exists. PagerDuty or Slack alerting routes to the model owner, not the general engineering on-call, because model degradation requires ML-specific investigation.
Statistical monitoring of incoming feature distributions against training baselines. Feature drift detection uses Population Stability Index (PSI threshold of 0.2 triggers warning, 0.25 triggers critical -- the industry standard that distinguishes noise from genuine distribution shift), Kolmogorov-Smirnov two-sample tests for continuous numeric features, Jensen-Shannon divergence for probability distributions, and Chi-squared tests for categorical features. Each feature in your model gets its own monitoring configuration: the right statistical test for the feature type, the right threshold calibrated to how much that feature influences the output, and its own alert routing based on criticality. A PSI of 0.22 on a low-signal feature is noise; the same PSI on a high-importance feature according to your SHAP values is an alert.
Concept drift -- the relationship between inputs and correct outputs changing even when input distributions are stable -- requires different detection techniques because it depends on ground truth labels from production. For use cases where labels arrive promptly (e.g., click-through rates, immediate transaction fraud confirmations), we implement sliding-window performance monitoring that fires when accuracy drops below threshold. For delayed-label use cases (e.g., loan default prediction where the outcome is known months later), we implement proxy metrics -- leading indicators correlated with model accuracy -- and ADWIN (Adaptive Windowing) statistical change detection on those proxies. Drift dashboards surface which features are drifting, by how much, and since when, presented in priority order by estimated business impact. Combined feature importance from SHAP plus drift magnitude scores each feature so your team knows which drift is worth investigating and which is background noise.
Trigger-based retraining pipelines that rebuild models when drift thresholds are crossed, not on a fixed calendar schedule. Pipeline orchestration implemented in Apache Airflow (DAG-based, schedule and event-triggered, retryable per-task), AWS SageMaker Pipelines (managed, no infrastructure to maintain, native integration with SageMaker training jobs and Model Registry), or Prefect (Python-native, excellent for ML teams preferring code-first workflow definition). The choice is made during scoping based on your existing infrastructure and team familiarity -- we are not opinionated about orchestrators, only about the outcome.
Pipeline stages: (1) Trigger evaluation -- drift threshold crossed, or business metric SLA breach confirmed. (2) Data pull -- fresh labelled data extracted from your data warehouse (BigQuery, Redshift, Snowflake) combined with historical training data, versioned using DVC (Data Version Control) so the exact dataset that produced any given model can be reconstructed. (3) Training -- executed in a reproducible Docker container with pinned dependency versions; MLflow Projects or SageMaker Training Jobs log every run with parameters, metrics, and environment hash. (4) Validation -- retrained model evaluated against a held-out test set for accuracy metrics, against a suite of business-logic assertions (e.g., "high-risk customer segments must be flagged at recall >= 0.90"), and against an adversarial edge-case set for the failure modes your team knows matter. (5) Promotion -- model that passes all validation gates is registered in the Model Registry at candidate status, promoted to shadow mode (receives production traffic but predictions are not served), then promoted to production after shadow-mode accuracy confirms real-world performance matches offline validation. Failed validation triggers a Slack or PagerDuty alert to the model owner with the specific assertion that failed and the metric delta. The previous champion version remains in production and is retained for immediate rollback.
Experiment tracking infrastructure using MLflow, Weights and Biases, or similar, configured for your team's workflow. Every training run logged with parameters, metrics, data version, and code version. Model registry with staged promotion: development, staging, production. Champion-challenger tracking for A/B tests between model versions. Reproducible environments using Docker and dependency pinning so any experiment can be recreated six months later. The audit trail that makes AI development a managed engineering process rather than a series of undocumented experiments.
Centralised feature storage that makes model features consistent between training and serving. Online feature store for low-latency feature retrieval at inference time. Offline feature store for training data preparation and backtesting. Feature versioning and lineage tracking. Elimination of training-serving skew -- the gap between the feature values seen during training and the feature values computed at inference. For teams with multiple models consuming the same features, the feature store avoids redundant computation and inconsistent feature definitions across models.
End-to-end MLOps platform setup on your cloud infrastructure (AWS SageMaker, Azure ML, Google Vertex AI, or self-hosted). Pipeline orchestration using Airflow, Prefect, or Kubeflow. Container-based training environments. Model serving infrastructure with auto-scaling and canary deployments. Infrastructure as code using Terraform so your entire MLOps stack is version-controlled and reproducible. Integration with your existing CI/CD pipelines and data infrastructure. Built for your team to operate and extend independently after delivery.
Bring us your deployed AI systems and current monitoring setup. We'll identify the gaps and design the MLOps layer you need to keep accuracy from silently degrading.
AI Development -- custom AI systems built and delivered
Data Engineering -- the data pipelines that feed your models
Predictive Analytics -- forecasting and prediction model development
Custom AI Development -- bespoke AI model development
RAG Pipeline Development -- retrieval-augmented generation systems
Frequently asked questions
MLOps -- machine learning operations -- is the set of practices and infrastructure that keeps AI models performing reliably in production over time. Most AI projects focus heavily on model development and treat deployment as the finish line. In practice, deployment is where the ongoing work begins. Real-world data changes constantly: customer behaviour shifts, product catalogues expand, fraud patterns evolve, sensor environments change. A model trained on historical data gradually becomes a model trained on the wrong data as the world it was built to understand diverges from the world it is asked to predict. MLOps puts monitoring and maintenance infrastructure in place before this becomes a problem. Model monitoring tracks key metrics continuously. Drift detection identifies when incoming data no longer matches the training distribution. Automated retraining pipelines rebuild and validate the model when drift thresholds are crossed. Experiment tracking ensures every model version is reproducible. These systems turn AI from a one-time build into a maintained capability.
Data drift occurs when the statistical properties of the input data your model receives in production diverge from the data it was trained on. There are two types that matter. Feature drift means the inputs themselves are changing -- your customer demographics are shifting, transaction volumes are moving, or the distribution of product categories in your catalogue has changed. Concept drift means the relationship between inputs and correct outputs has changed -- fraud tactics have evolved, customer preferences have shifted, or the macro environment has changed the meaning of the signals your model uses. Feature drift is detectable statistically by comparing incoming data distributions to training data. Concept drift is harder to detect because it requires ground truth labels from production, which often arrive with a delay. Our monitoring design accounts for both. For each use case, we define the appropriate drift metrics, detection thresholds, and alert logic based on how quickly drift translates to business impact in your specific context.
Automated retraining pipelines work in three stages: trigger, retrain, and validate. The trigger is a drift threshold -- when model performance metrics or data distribution metrics cross a defined boundary, the pipeline fires. Retraining pulls fresh labelled data from your data pipeline, combined with historical training data, and runs the model training job in a reproducible environment. Validation runs the retrained model against a held-out evaluation set and a set of business-logic tests before it is promoted to production. If the retrained model fails validation, it does not deploy and the team is alerted. If it passes, it deploys through your standard deployment pipeline and the previous model version is retained for rollback. The trigger thresholds and validation criteria are defined during scoping based on how sensitive your use case is to model degradation. Some contexts warrant retraining when drift crosses a statistical threshold. Others require business metric confirmation. We design the pipeline around the tolerance for false positives and false negatives in your specific application.
Application monitoring watches whether the system is up and responding: response times, error rates, infrastructure health. MLOps monitoring watches whether the outputs are correct: whether the model's predictions are still accurate, whether the data flowing through the system still looks like it should, and whether business metrics tied to AI output are tracking as expected. Both matter, but they catch different failure modes. Application monitoring tells you the API is returning 200. MLOps monitoring tells you the answers it is returning are wrong. For AI systems where accuracy directly affects revenue, fraud exposure, or customer experience, monitoring only the application layer is a significant gap. We integrate with your existing application monitoring infrastructure and add the model-specific monitoring layer on top.
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