Build the foundation AI requires. A governed, AI-ready lakehouse in 8 to 20 weeks.
AI initiatives don’t fail because the models are bad. They fail because the data isn’t ready. 27Global builds the data foundation your AI strategy requires — a production-grade, governed medallion lakehouse on Databricks, Microsoft Fabric, or AWS — so your agents and your analysts work from data they can actually trust, with an auditable record of who accessed what.
Cloudera and Harvard Business Review found that only 7% of enterprises say their data is completely ready for AI. The other 93% are building on a foundation nobody evaluated.
The costs are already being paid before AI enters the picture. 85% of big data projects fail to deliver their intended value. Data silos cost organizations an estimated $7.8M a year in lost productivity, and poor data quality adds $9.7M to $15M annually per organization. Meanwhile, organizations with strong data integration achieve 10.3x ROI from AI, against 3.7x for those with poor integration.
The data layer doesn’t just enable AI. It determines how much AI ever delivers.
Raw, immutable ingestion zone with a full audit trail and zero data loss. This is what your compliance auditors ask for and what most platforms cannot produce.
Validated, deduplicated, schema-enforced records that become the single source of truth for analytics, operational reporting, and ML feature engineering.
Aggregated, domain-specific models that are business-ready for AI agents, dashboards, executives, and LLM endpoints.
PII classification, row and column-level access control, end-to-end lineage visualization, and a business glossary your stewards actually maintain.
Versioned, tested, documented Gold models with semantic layer metrics, so a number means the same thing in a dashboard and in an agent response.
SQL endpoints, feature store, vector store, and MCP tool definitions so agents retrieve data with the right scope and leave an auditable access record.
Best for AI-heavy organizations. Unity Catalog governance, with MLflow and Agent Bricks integration.
Best for Microsoft-centric organizations. OneLake unified storage, with Power BI and Copilot as consumers.
Best for AWS-native organizations. S3 Tables on Iceberg, integrated with the SageMaker AI ecosystem.
A dbt project, PII classification and governance, end-to-end lineage, and a governed agent data access layer.
Hands-on platform training and architecture documentation, so your data engineering team owns the platform when we leave.
Agent evaluation and estate-level agent operations are owned elsewhere. We deliver trustworthy data products and the signals needed to judge data health — not the agents that consume them.
AI Agent Factory consumes the data products this platform produces. AgentOps Governance consumes the data-health and provenance signals to correlate data quality with agent behavior and outcomes.
Inventory source systems, profile data quality, map ownership, and identify the constraints that will shape the architecture.
Medallion architecture designed for your environment, with an explicit platform recommendation and the selection criteria behind it.
Classification scheme, access control model, lineage requirements, and stewardship workflow agreed before any pipeline is built.
Batch and streaming pipelines from agreed source systems, with data quality monitoring wired in from the first load.
Domain-specific business models, versioned and tested in dbt, with semantic metrics for BI and agent consumption.
Feature store, vector store, SQL endpoints, and MCP tool definitions, plus the operations runbook and team training.
Cloud providers offer data platform quickstarts that produce that cloud’s adoption roadmap. The global integrators deploy 20 to 40 people on a 6 to 12 month timeline optimized for their delivery model, not your outcome.
Senior-staffed teams working from opinionated medallion reference architectures. Clients reach a production Gold layer in a quarter, not a fiscal year.
MCP tool definitions, scoped retrieval, and auditable access records are designed in from the start. Retrofitting governed agent access later costs multiples more.
The EU AI Act requires documented lineage, PII classification, and access audit trails for high-risk AI. GDPR, HIPAA, and DORA add their own. One platform satisfies all of it.
Databricks, Fabric, or AWS. We have built production platforms on each and recommend based on your team, your stack, and your existing commitments.
Without a governed data platform you cannot prove your AI training data is compliant, demonstrate human oversight over AI decisions, or satisfy the Fundamental Rights Impact Assessment required for high-risk systems. Enforcement begins August 2026.
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We’ve put together a handful of activities to guide your organization in the data transformation process, including data transformation tools you can use.
The Discovery phase is focused on several activities that will identify all current data-related elements and map the flow of data throughout its lifecycle.
