Data Modernization & ML Engineering

Data is the lifeblood of AI, and most organizations discover their AI ambitions are blocked by their data foundations long before they are blocked by model selection. Data modernization is the process of moving from legacy, siloed data systems to scalable, cloud-native architectures that are governed, observable, and ready to feed AI workloads at production scale.

The Lakehouse architecture. The dominant pattern for modern data platforms is the Lakehouse — combining the flexibility and cost profile of a data lake with the transactional reliability of a warehouse. Open table formats like Apache Iceberg and Delta Lake make this practical, providing ACID transactions, schema evolution, and time travel on top of cheap object storage. We design Lakehouse implementations on the cloud platforms you already use, with bronze/silver/gold layered modeling, streaming ingestion where it matters, and clear governance boundaries.

Vector databases for AI applications. Traditional databases retrieve rows by exact match. Vector databases retrieve by semantic similarity — given an embedding of a query, they return the closest embeddings from your knowledge base. This is the foundation of RAG, recommendation systems, and similarity search. We help teams choose between pgvector, Pinecone, Weaviate, and other options based on scale, query patterns, and operational profile.

MLOps and LLMOps. Once models are trained, the work shifts to operating them in production — versioning, deployment, monitoring, retraining, drift detection, and rollback. MLOps brings DevOps discipline to machine learning. LLMOps adds layers specific to large language models: prompt versioning, evaluation harnesses, cost monitoring, and content safety. We build the pipelines and dashboards that turn one-off model experiments into reliable production systems.

Data governance and security. Modern data platforms include access control at column and row level, audit logging, lineage tracking, and PII detection. We implement governance from day one because retrofitting it later is expensive and risky. The result is a foundation you can scale AI initiatives on with confidence — clean data, accessible APIs, predictable costs, and the operational discipline to keep it running.