Logic pursuits
Logic pursuits
MLOps and
AI Operations
AI systems drift after deployment. We keep performance on track.
Getting a model into production is only
the beginning
As customer behavior, fraud patterns and data distributions shift, model performance can decline quietly over time. Without continuous monitoring, retraining and version control, teams may continue relying on outdated predictions without knowing when accuracy has started to fall.
We build monitoring, automated retraining, CI/CD and governance alongside the model, helping your teams track performance, respond to drift and maintain a complete audit trail from day one.
How we
make it possible
- Define performance baselines, drift thresholds and alerts during validation, so monitoring begins as soon as the model goes live.
- Trigger data refresh, retraining and validation through drift alerts, data volumes or schedules, with promotion, review and rollback safeguards.
- Establish SLAs, approval workflows, model registries and runbooks, then transfer ownership or manage operations on your behalf.
Multiplying outcomes for your business
Model Monitoring & Drift Detection
Track model performance, feature drift and prediction quality against thresholds calibrated during training.
Automated Retraining & CI/CD for ML
Trigger retraining by drift, data volume or schedule, with automated validation, promotion and rollback controls.
Experiment Tracking & Model Registry
Track experiments, compare performance and maintain model approvals, version histories and audit trails through MLflow.
AI Operations as a Managed Service
Operate monitoring, retraining and governance across machine learning, GenAI and agent systems under defined SLAs.
Helping your leaders accelerate decisions
Impact tailored for your industry
Monitor fraud, credit and compliance models as behaviors change, with retraining records and approval workflows built for regulatory review.
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Maintain performance evidence, version histories and approvals for models used in investment and fiduciary decisions.
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Keep pricing, demand and propensity models aligned with changing booking patterns, customer behavior and market conditions.
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Recalibrate churn, recommendation and attribution models as content libraries, audiences and engagement patterns shift.
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Document every retraining cycle, parameter change and approval for clinical and commercial models in regulated environments.
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Maintain forecasting and predictive maintenance accuracy as supply chains, equipment and production conditions change.
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