By Brand Solutions

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AI is advancing at breakneck speed — yet most enterprises are stuck in first gear. A recent MIT study, The GenAI Divide: State of AI in Business 2025, revealed that 95% of generative AI pilots fail to deliver measurable financial impact. Enterprises have oceans of data, but their AI agents stumble because the data lacks context, connectivity, and speed.

This is the paradox of modern enterprise AI: cutting-edge models running on outdated data foundations.

Intugle believes this must change. That’s why it is building what it calls the world’s smartest and fastest data platform for AI and analytics workloads — a platform designed from first principles to make data understandable, contextual, and instantly usable for AI agents at scale.
Smart: Context is the next compute

Being “smart” is more than storing petabytes or running faster SQL. It’s about building systems that understand meaning, relationships, and business context across every data silo. Embedding human-like cognitive intelligence into enterprise data is no longer aspirational — it’s essential.

The Intugle platform transforms existing warehouses, lakes, and lakehouses into a layer of ontology —an enterprise brain, a unified intelligence layer connecting context, relationships, and semantics across silos without replication.

A layer that is context and policy aware

Context Aware – It understands your business semantics. For example, an ACC_ID means Account in your ERP, Customer in your CRM, and Subscriber in your support system. It knows that a “Platform Configuration” can have many “Replaceable Units” but not vice versa. This context creates the human–machine fusion AI needs to reason accurately.
Policy Aware – As data becomes democratized, governance becomes non-negotiable. The platform embeds policies defining how data is shared, accessed, and consumed — the rules of the game for both human and AI workforce.
This context-driven architecture allows generative artificial intelligence (GenAI) models, reasoning agents, and LLM-powered applications to operate with human-level precision and machine-level scale.
From raw data to AI-ready in minutes, not months
Speed isn’t about shaving seconds off queries. It’s about removing the months-long bottleneck of manual data prep. Traditionally, launching an AI use case requires building pipelines, cleaning data, and wrangling schemas.

Intugle’s approach is disruptive:
No data movement: Data stays where it is, surfaced through a “shoppable” product catalog for both humans and AI agents. Distributed computation handles processing on demand, making latency irrelevant.
Enterprise knowledge generated by AI (for AI): It harvests, enriches, and synthesizes metadata through systemic, AI-guided processes with human oversight.

Intugle deploys specialized AI agents to automate the entire lifecycle:
AI data profiler: Performs autonomous exploration to infer statistical heuristics for every attribute and classify them into business domains.
AI data steward: Predicts semantic and logical relationships across attributes, then validates them autonomously.
AI data engineer: Traverses the knowledge graph to find optimal join paths and generate machine-executable federated queries to produce Data Products on-demand.
AI data analyst: A multi-agentic NL2SQL system that translates business questions into accurate queries, achieving > 90% contextual accuracy, outperforming industry norms.
Fast becomes a force multiplier: the faster you can go from data to decision, the faster your AI can learn, adapt, and deliver value.
Purpose-built for AI workloads
This isn’t a legacy data warehouse with AI bolted on. It’s built for AI from the ground up:

Semantic-first architecture for structured data comprehension
Real-time vector search for reasoning agents
Scalable, NVIDIA GPU-accelerated compute to power semantic modeling, LLM fine-tuning, and low-latency inference at scale
As GenAI and agentic architectures reshape enterprises, data infrastructure can no longer be an afterthought—it must be the foundation.
The future: Autonomous intelligence at scale
AI will only be as smart as the data it’s built on. The future belongs to organizations that treat data not as static records but as a living intelligence layer—a system where AI agents can reason, act, and learn across the enterprise in real time.