Data & AI

Data & AI Foundations

Modern data and AI foundations: lakehouse architecture, modern data stack, MLOps, generative AI integration, governance, AI agents for B2B operations.

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150+

Data Programs

Avg Use Case Velocity

10+

AI Agents Shipped

4.7/5

Data Team NPS

The Data Foundation AI Actually Needs

Most AI pilots fail because the data foundation cannot support them. Modern data and AI foundations combine lakehouse architecture (Snowflake, Databricks, BigQuery), governance (Unity Catalog, Atlan, Collibra), MLOps platforms, and generative AI integration patterns. The combination turns AI from a series of pilots into an embedded capability across the operating model.

Capabilities

What this service delivers.

Modern Data Stack

Snowflake, Databricks, BigQuery, dbt, Fivetran, Airflow, reverse ETL.

Lakehouse Architecture

Unified data and AI architecture with Delta Lake, Iceberg, Hudi.

Data Governance

Unity Catalog, Atlan, Collibra deployment with data product ownership.

MLOps Platforms

MLflow, SageMaker, Vertex AI, Azure ML for production ML lifecycle.

Generative AI Integration

Foundation model access, RAG patterns, vector databases, AI guardrails.

AI Agents

Agent frameworks (LangGraph, AutoGen) for autonomous workflow execution.

Deep Dives

Specialized articles within this service.

Snowflake vs Databricks vs BigQuery Comparison

Pick the Lakehouse That Fits Your WorkloadSnowflake, Databricks, and BigQuery are all production-grade for modern data and AI. Snowflake leads on simplicity

Generative AI Implementation Patterns for Enterprise

From GenAI Pilot to ProductionMost enterprise GenAI pilots stall at production readiness. Mature implementations combine foundation models (OpenAI, Anthropic, Bedrock, Vertex), RAG

MLOps Platforms Compared: MLflow, SageMaker, Vertex

MLOps Platforms for Production MLMLflow is the open-source standard. Cloud-native platforms (SageMaker, Vertex, Azure ML) bundle managed services for full lifecycle. Databricks

AI Agent Frameworks: LangGraph, AutoGen, CrewAI

Build Production AI Agents Without HypeAI agent frameworks (LangGraph, AutoGen, CrewAI) enable autonomous multi-step workflows: research, document processing, support triage, sales follow-up.

Process

How we deliver this engagement.

Assessment

Data maturity assessment, AI use case prioritization, capability gap analysis.

Architecture

Reference architecture across data, ML, GenAI, governance.

Foundation Build

Build core platforms with first 2-3 use cases.

Scale

Onboard additional use cases, harden governance, scale agents.

Outcomes

Outcomes you can measure.

AI-Ready Foundation

Foundations make AI deployment a feature, not a project.

Faster Use Case Velocity

Standardized patterns cut time to first prediction from quarters to weeks.

Governance at Scale

Catalog and lineage prevent data sprawl and shadow stacks.

Cost Discipline

FinOps for data plus AI prevents cost explosions.

FAQ

Common questions, answered.

Lakehouse vs warehouse?

Lakehouse for unified data and AI workloads. Warehouse adequate for BI-only environments. Most modern enterprises move to lakehouse.

Build vs buy AI?

Buy foundation models, build context. Use OpenAI, Anthropic, Bedrock, Vertex for foundation. Build RAG, agents, fine-tunes for differentiation.

Generative AI in production?

Yes with discipline: guardrails, evaluation frameworks, human-in-loop, observability. Otherwise hallucination and compliance risk.

AI agents real?

Production-ready for narrow workflows: support triage, sales follow-up, document processing. Multi-agent autonomy still maturing.

Discuss this service with our team.

Scope the program, the team, and the outcomes in a single working session.

Book a Strategy Session