Data Lakes to LLMs: Building the Foundation for AI-Driven Enterprises

Large Language Models (LLMs) are redefining how enterprises interact with data.
Yet, many pilots fail because organizations underestimate the importance of data foundations.
To make LLMs enterprise-ready, businesses need well-architected data lakes, pipelines, and managed platforms.

The Data Gap

• Data silos: Fragmented systems prevent unified intelligence.
• Poor governance: Without metadata and lineage, AI outputs are unreliable.
• Scalability issues: Enterprises lack infrastructure for large-scale AI workloads.

Inteliment’s Approach

Inteliment helps enterprises move from raw data to LLM-ready data pipelines through:

• Data Lake Engineering: Unified storage with metadata-driven management.
• Rubiscape Managed Data Platform: End-to-end governance, model lifecycle, and visualization.
• ODC Execution: Global delivery with local compliance (India + Australia for GCC enterprises).

Industry Use Cases

• Insurance: LLMs for automated claims summarization.
• Retail: AI copilots for personalized recommendations.
• Travel: Conversational AI copilots for customer journeys.
• Energy: LLMs analyzing unstructured operational reports.

GCC Angle

For GCC enterprises driving Vision 2030 initiatives, data strategy is foundational.
Inteliment’s outsourcing partnerships ensure enterprises don’t just experiment with LLMs but scale them responsibly and profitably.

Conclusion

LLMs are powerful, but without data lakes and engineered pipelines, they’re destined to fail.
Inteliment ensures enterprises have the right foundation to move from data chaos to AI clarity.