We build AI applications designed for enterprise environments — document intelligence, demand forecasting, anomaly detection, and process automation. Applications are built on your existing data infrastructure with APIs, access controls, and integration into your SAP or cloud environment from day one.
We develop, train, and fine-tune models suited to your data and your business problem — selecting the right architecture, preparing training data from enterprise sources, and validating performance against metrics that reflect real operational requirements, not benchmark scores.
We build the pipelines that take a model from training into production and keep it there. Automated retraining, data validation, model versioning, CI/CD for ML, and deployment automation — engineered on Vertex AI, SageMaker, Azure ML, and Databricks depending on your existing stack.
A model accurate at launch degrades without visibility. We instrument production models with monitoring for data drift, concept drift, and prediction quality — with alerting thresholds, root-cause tooling, and retraining triggers so issues are caught before they reach downstream decisions.
Enterprise AI carries audit, compliance, and explainability requirements that sandbox models don’t. We build governance frameworks covering model documentation, bias assessment, explainability tooling, access controls, and audit trails — aligned to your regulatory environment and internal risk standards.
AI that operates in isolation from your ERP produces insights nobody acts on. We integrate AI applications with SAP S/4HANA, SAP BTP, and your data platform — so model outputs feed planning, procurement, finance, and operations through the systems your teams already use.
An AI roadmap exists. Use cases have been prioritized. The engineering capability to build production-grade applications and the MLOps infrastructure to sustain them hasn’t been assembled yet.
AI is running in production but without retraining pipelines, monitoring, or governance. Drift is managed reactively when business users notice problems, not proactively when the data signals them.
The organization is moving from one AI application to several and needs a shared MLOps platform, governance standards, and delivery capacity that doesn’t rebuild the same infrastructure for every new model.
No AI engineering engagement starts with model selection. The right architecture depends on your data maturity, existing cloud and SAP infrastructure, the regulatory environment, and the operational teams who will act on model outputs.
existing models, pipelines, data infrastructure, monitoring, and governance in place
where your AI program falls short of production-grade engineering requirements
platform selection, pipeline design, integration points, and governance framework
whether Candent builds the full MLOps platform, augments an existing engineering team, or takes over a deployment that isn’t performing
