Enterprise data engineering firm Hoonartek has introduced ClearView, an agentic decisioning platform designed to help enterprises translate large-scale data investments into automated business execution. The new platform sits above existing data infrastructure—such as lakehouses and cloud data warehouses—and deploys AI agents capable of making governed, traceable decisions across business operations.

Enterprises have spent the past decade building large-scale data platforms—modernizing infrastructure with lakehouses, cloud warehouses, and advanced analytics pipelines. Yet many organizations still struggle to convert these data investments into real-time operational decisions.

That gap is precisely what Hoonartek is targeting with its newly launched ClearView platform, an AI-driven decision layer designed to connect enterprise data estates directly to business execution.

Rather than adding another analytics tool or SaaS application, ClearView introduces what Hoonartek describes as an agentic decisioning layer. The system deploys autonomous AI agents that interact directly with an organization’s existing data infrastructure to execute operational decisions in real time.

The idea is simple but increasingly relevant in modern enterprise IT: if data platforms contain valuable insights, those insights should directly trigger business actions.

Why Enterprises Are Struggling to Activate Data

Over the last decade, companies have invested heavily in enterprise data architecture. Technologies such as cloud data warehouses, distributed lakehouses, and real-time analytics pipelines have become central components of digital transformation initiatives.

Yet despite these investments, many organizations still rely on fragmented SaaS tools for operational decision-making. Marketing teams use separate platforms for campaign optimization, finance departments rely on forecasting software, and supply chain teams deploy independent analytics tools.

The result is what many technology leaders describe as SaaS sprawl—a rapidly expanding stack of niche applications that operate independently from the underlying enterprise data platform.

According to research from Gartner, organizations now manage hundreds of SaaS applications on average, creating governance challenges, rising licensing costs, and fragmented decision workflows.

ClearView attempts to address this issue by shifting the architecture away from tool-centric automation toward decision-centric automation.

Instead of deploying individual SaaS tools for specific functions, the platform embeds AI agents that act directly on enterprise data to perform business decisions such as pricing adjustments, fraud detection, operational alerts, or customer engagement triggers.

An Architecture Built Around Autonomous Decisioning

The ClearView platform operates across three core layers that together create an enterprise AI decision engine.

The first layer focuses on decision governance, defining how AI agents operate, what authority they hold, and how their actions align with enterprise policy.

The second layer is RealizeAI, Hoonartek’s AI development framework designed to scale machine learning models and analytics use cases across the organization.

The final component, BlueFoundry, functions as the operational execution engine. It converts business intent—such as a rule or optimization objective—into automated agent workflows capable of executing real-time decisions.

Every action generated by the system remains traceable from intent to outcome, creating an audit trail designed for enterprise governance and regulatory compliance.

For enterprise leaders, traceability has become a critical requirement as AI moves from experimental analytics into operational systems.

AI Moves Closer to Business Execution

The broader shift toward agentic systems reflects a growing trend in enterprise technology: AI is moving beyond analysis and into autonomous operational execution.

Large technology ecosystems—including Microsoft, Google, and Amazon—have increasingly invested in AI agent frameworks that automate tasks previously handled by human operators.

However, most enterprise deployments still rely on human-in-the-loop workflows where AI generates insights but stops short of making decisions.

ClearView attempts to close that gap by enabling AI agents to execute actions directly within business systems while maintaining governance oversight.

Industry experts say this shift may become increasingly important as organizations look to scale AI beyond isolated use cases.

“Enterprises don’t fail at AI because of poor models,” said Dejan Deklich, former CTO of Aisera, in reference to the platform announcement. “They fail because no one connected the data platform to decisions.”

Growing Pressure to Rationalize SaaS and AI Spending

The launch of ClearView also reflects broader economic pressures shaping enterprise technology strategies.

Chief financial officers and chief data officers are increasingly tasked with reducing SaaS complexity while accelerating AI adoption.

According to IDC, worldwide spending on AI technologies is expected to exceed $500 billion by 2027, while organizations simultaneously attempt to consolidate software vendors and simplify digital infrastructure.

Platforms that enable AI-driven automation directly on existing data environments could help organizations achieve both goals: activating AI capabilities while reducing reliance on specialized SaaS tools.

Hoonartek says ClearView is already being deployed in sectors such as financial services, telecommunications, and manufacturing—industries where operational decisions often depend on real-time data signals.

The company recently received recognition for AI Service Excellence at the NASSCOM Inspire Awards 2026, highlighting growing interest in its enterprise AI services.

What This Means for Enterprise Technology Strategy

For enterprise IT leaders, the concept of a decision layer above the data platform represents a new architectural approach.

Instead of building separate applications for every operational function, organizations may increasingly adopt AI-driven orchestration layers capable of executing decisions across systems.

If successful, this model could reshape how companies design enterprise technology stacks—placing autonomous agents at the center of operational workflows rather than traditional SaaS applications.

As AI infrastructure matures, the next competitive frontier may not be data collection or analytics alone, but how quickly organizations can translate data into automated decisions that drive business outcomes.

Market Landscape

The emergence of agentic AI platforms reflects a broader evolution in enterprise software architecture. Vendors across the technology ecosystem—including Microsoft, Google, and Amazon—are investing heavily in AI systems capable of automating complex workflows.

Meanwhile, analysts at McKinsey & Company estimate that generative AI could generate $2.6 trillion to $4.4 trillion in annual economic value, much of it tied to automation of operational decision-making.

As enterprises mature their data platforms, technologies that connect data infrastructure directly to autonomous decision systems are expected to become a new layer of enterprise AI architecture.

Top Insights

• Hoonartek launched ClearView, an AI-powered decisioning layer designed to activate enterprise data platforms by deploying autonomous agents capable of executing real-time business decisions.

• The platform addresses growing enterprise concerns around SaaS sprawl by enabling decision-centric automation directly on top of existing lakehouse and cloud data infrastructure.

• ClearView’s architecture combines governance, machine learning development, and workflow orchestration to create traceable AI-driven decision systems for enterprise operations.

• Industry experts say the future of enterprise AI will depend less on model accuracy and more on how effectively organizations connect data infrastructure to operational decision workflows.

• As AI spending grows globally, enterprises are increasingly seeking platforms that activate existing data investments while reducing dependence on fragmented SaaS applications.

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