Executive workshops to map business goals to high-ROI AI use cases. Build-vs-buy analysis, value sizing, and phased delivery plan. Risk, compliance, and change-management considerations.
Identify and prepare source data; batch and streaming ingestion. Data quality, lineage, and privacy controls for AI training and inference. Feature engineering and semantic layers for consistent metrics.
Model selection and training (classical ML and LLMs). CI/CD for data, features, models, and prompts. Automated testing, evaluation, and drift detection.
Policy frameworks, access controls, audit logging, and human-in-the-loop. Bias testing, safety checks, and model cards. Compliance support aligned to your industry requirements.
Inference pipelines, vector search, and caching for performance. Model, data, and cost observability with alerts and SLOs. Continuous improvement loops using feedback and A/B testing.
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