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Michael Elkin, CTOFor decades, critical enterprise systems such as ERP platforms, CRM databases and operational data stores have resisted natural language interaction. Business intelligence systems filled the gap but only offered analysis of what had already happened.
GigaSpaces addressed this constraint by moving AI from after-the-fact analysis into live operational execution. Its flagship solution, eRAG allows intelligence to operate directly on structured operational data where business rules, obligations and execution logic already reside. By emphasizing semantic accuracy, the platform enables probabilistic language models to function reliably inside deterministic enterprise systems.
eRAG enables natural language interaction with live operational systems. Rather than focusing on answering more questions, it is designed to support earlier intervention in decisions, allowing users to adjust actions while execution is underway.
“Operators can now ask in real-time why a shipment is delayed or why a sensor reading has spiked and receive answers while assets are still in motion,” says Michael Elkin, CTO.
This ability to intervene during execution rather than after the fact earned GigaSpaces recognition as the Top AI-Powered Structured Operational Data Solution of 2026.
Much of its capabilities is rooted in GigaSpaces’ origins. For more than two decades, the company has specialized in low-latency, in-memory computing and Digital Integration Hubs that support high-throughput operational systems. When large language models became viable, GigaSpaces, with its extensive experience in structured data, is enabling AI to operate directly within live enterprise systems and understand not just data, but its organizational context.
A Digital Teammate that Learns the Business
In enterprise systems, meaning is rarely self-evident. A column labeled “T_15_STAT” may signal an urgent delivery condition, while a “Friday Report” may represent a specific internal revenue construct. These definitions are obvious to employees, but invisible to language models unless that context is learned.
Instead of forcing language models to infer meaning solely from structure, GigaSpaces built a semantic reasoning layer that acquires context the way people do. eRAG enters the organization as a junior teammate, beginning with formal orientation that includes schemas, workflows, procedures and documentation. Its deeper understanding develops through use. As teams interact with the system, assumptions are clarified, definitions are corrected and meaning is refined through the natural cadence of work.
Over time, the semantic layer extends beyond individual departments, allowing definitions and context to align across the organization. Users stop adapting their language to fit the tool and instead engage as they would with a colleague who already knows the terrain.
Operators can now ask in real-time why a shipment is delayed or why a sensor reading has spiked and receive answers while assets are still in motion.
Over time, the semantic layer extends beyond individual departments, allowing definitions and context to align across the organization. Users stop adapting their language to fit the tool and instead engage as they would with a colleague who already knows the terrain.
Most organizations have eRAG connected and operational within three to four weeks, without the need for modeling cycles or heavy engineering demands on internal teams. As a result, time to value is measured in days of use rather than quarters of configuration.
The Cost of Waiting in Renewable Energy Operations
For a renewable energy operator managing solar plants across multiple European markets, minor delays can carry outsized financial consequences. Penalties are triggered not by intent, but by timing.
A technician on site notices that the inverter is cycling more frequently than expected and logs the observation in their own words. In most systems, that note remains unstructured text, disconnected from live telemetry and contractual terms until it is reviewed later.
With eRAG, the moment is treated differently. The observation is correlated immediately with sensor data and service agreements. The legal team, based on this analysis, evaluates whether peak production hours are at risk and determines if an SLA breach is likely. A regional manager is alerted in real time with guidance on how to intervene before escalation.
That early clarity makes the difference. By distinguishing between unavoidable technical disruptions and preventable contractual breaches while events are still unfolding, the organization avoided tens of thousands of euros in penalties.
“This is where AI stops being theoretical,” says Elkin. “When it helps people act early enough to avoid real losses, that’s impact you can measure.”
From Reporting to Reasoning in Logistics
In the logistics sector, eRAG has reshaped how senior leaders interact with data. One global provider introduced the system to executives across import, export and corporate leadership functions.
Initial use mirrored existing BI behavior. Leaders asked familiar questions and reviewed known metrics. Over time, interaction deepened. Conversations extended across sessions. Executives returned to prior threads, shared insights with colleagues and explored scenarios on mobile devices as decisions unfolded.
Within a single conversational flow, leaders examined margins by route, tested pricing changes, compared internal performance with external benchmarks and modeled what-if scenarios in real-time. These discussions led to a fundamental restructuring of pricing strategy and profit models.
What changed was not the interface. It was the quality of engagement. When AI understands how a business defines its data and decisions, interaction shifts from retrieval to reasoning. Judgment takes shape while execution remains flexible.
Governance as an Operating Requirement
As enterprise AI adoption accelerates, trust has become a key factor in determining success. GigaSpaces treats governance as a foundational requirement rather than an afterthought. Through integrations with IBM WatsonX Governance and Amazon SageMaker, every interaction is evaluated for security, compliance and risk before execution.
Access controls are enforced. Sensitive data is protected from exposure to public models. Every response remains explainable and traceable to its source data. This architecture positions eRAG for compliance with emerging regulatory frameworks, including the EU AI Act, while enabling the safe deployment of AI inside mission-critical environments.
Designing Autonomy that Can Be Trusted
The next phase of enterprise AI is not about generating better answers. It is about leveraging GenAI for planning, analysis and spontaneous intervention based on core enterprise data. GigaSpaces is laying the groundwork for this shift through a progression from RAG to TAG and ultimately to autonomous systems that operate with assurance.
Many organizations attempt to achieve autonomy by reverse-engineering processes and building dozens of specialized agents. Without semantic grounding, those systems amplify inconsistency, fragment accountability and fail under real operational pressure. The result is complexity without trust and automation without control.
GigaSpaces has taken a different path. By first establishing semantic accuracy, governance and real-time reasoning, eRAG creates the consistency and accuracy required for autonomy to emerge without compromising accountability. Corrective actions can transition from recommendation to execution only when the underlying data and logic are proven to be reliable.
The journey from the ‘Context Gap’ to ‘Assured Autonomy’ is the defining narrative of the year and GigaSpaces is the pragmatic guide for that journey.
Structured Data Intelligence Where AI Meets Enterprise Reality
Enterprise adoption of artificial intelligence has moved beyond experimentation, yet many executive teams remain dissatisfied with results. The gap rarely comes from ambition or funding. It stems from where AI is applied. Core revenue systems such as ERP, CRM and transaction platforms hold the structured data that governs how organizations operate, comply and decide. Applying AI around the edges, or only on unstructured content, leaves this foundation untouched and limits impact. Decision makers evaluating structured data intelligence platforms increasingly focus on whether these systems can engage directly with business data without destabilizing governance, accountability or speed.
A central challenge is interaction. Traditional dashboards and predefined reports answer known questions but struggle with ambiguity, exceptions or emerging issues. Executives want systems that can interpret natural language, understand business context and explore unfamiliar lines of inquiry without weeks of modeling or data preparation. This expectation introduces risk unless the intelligence layer truly understands enterprise semantics, including how roles, departments and policies shape meaning. Tools that simply connect language models to databases often fail here, producing responses that appear confident yet lack contextual grounding.
Another pressure point is the time to value. Building custom agents or workflows across multiple data domains demands specialized skills and long implementation cycles. Many organizations discover that experimentation scales faster than production use, leading to stalled initiatives. Buyers now look for platforms that respect existing systems, integrate tightly with structured sources and begin delivering insight within weeks rather than quarters, while remaining extensible as usage grows.
Consistency across the organization also matters. Intelligence that serves only analysts or technologists limits reach. Executives, managers and frontline teams increasingly expect shared access through familiar interfaces, whether desktop or mobile, while maintaining appropriate controls. The ability to learn continuously from user interaction, documentation and real operational behavior distinguishes systems that mature over time from those that remain static. Multilingual environments, regulatory complexity and distributed teams further amplify the need for context-aware intelligence that reduces friction without diluting accountability.
Within this context, GigaSpaces stands out as a disciplined approach to structured enterprise data intelligence. The company brings more than two decades of experience building complex data platforms for mission-critical environments, and then applies that foundation to AI engagement where businesses actually operate. Its solution focuses on enabling natural interaction with structured systems while preserving semantic accuracy and organizational context.
At the core is a semantic layer that acts as a translation brain between people and enterprise data. Rather than treating language models as autonomous decision engines, the platform grounds them in how the organization defines entities, relationships and responsibilities. Knowledge can be introduced through formal documentation or learned progressively through interaction, allowing the system to adapt in the same way new employees do. This approach supports cross-departmental understanding without forcing rigid upfront modeling.
The result is an assistant that supports exploratory questioning, follow-up analysis and scenario discussion directly against live business data. Executives gain faster clarity on exceptions, exposure and opportunity, while teams reduce dependence on intermediaries for everyday insight. Deployment is designed to be rapid, minimizing internal burden and accelerating practical use.
For organizations seeking meaningful returns from AI applied to structured enterprise data, GigaSpaces represents a credible benchmark. It aligns interaction, context and speed in a way that respects how real businesses run, making it a strong choice for leaders ready to move beyond experimentation. Its emphasis on disciplined integration, controlled learning and practical deployment reflects the priorities of executives who value insight over novelty, and who require intelligence that earns trust through consistent, explainable behavior across critical enterprise functions....Read more
Company
GigaSpaces
Management
Michael Elkin, CTO
Description
For over two decades, GigaSpaces has been a pioneer in real-time data platforms, powering some of the world’s most demanding systems. Building on this foundation, the company delivers advanced GenAI solutions that enable organizations to unlock the full value of their structured operational data and transform how they interact with information.
