We design source-to-target flows and build ELT/ETL pipelines across batch and streaming environments. We implement lakehouse or warehouse architectures with pragmatic data modeling. Quality and observability are built in—lineage, drift, freshness, and SLAs—followed by cost, performance, and reliability tuning so your data platform scales without surprises.

Our work spans classical ML—forecasting, churn, and propensity modeling—and GenAI use cases where they make sense. We handle feature engineering, experiment tracking, and offline/online evaluation, then operationalize with real-time or batch serving, monitoring, and drift/guardrails. Responsible AI principles—fairness, explainability, and human-in-the-loop—are standard, not optional.

We implement retrieval-augmented generation with secure enterprise connectors and role-based controls, enabling chat over your documents, apps, and data. Agent workflows handle summarization, enrichment, and actions, while evaluation harnesses, hallucination tests, and red-teaming ensure quality and mitigate risk.

Starting with process assessment, we define a human-plus-AI automation roadmap and orchestrate LLMs, RPA, APIs, and internal tools. Governance is embedded in every step—logging, approvals, rollback paths—along with cost and performance guardrails. We track outcomes like time saved, error reduction, and throughput to ensure measurable value.

We establish a metrics layer and semantic models as a single source of truth, then deliver self-serve dashboards, scorecards, and alerts tailored to teams. Leaders get planning scenarios and what-if models, while adoption playbooks—enablement, templates, and governance—ensure the tools are actually used and drive business impact.