Eureka AI wins InfoWorld 2025 Platform of the Year

“The Eureka AI Platform brings AI to many companies, because it is tailored to different vertical industries. This is a highly practical approach to AI in the enterprise that will deliver significant value by saving implementation time.” 

— InfoWorld Judges, 2025 Technology of the Year Awards 

The tolerance for systems of intelligence that cannot survive the rigor of production ownership is gone. For the last two years, enterprises have flooded their sandboxes with foundation models, only to hit the same wall: Foundation models are now interchangeable inputs; your AI architecture determines whether they create value or failure. 

Access to intelligence is no longer a differentiator; every competitor has access to the same foundational model APIs. InfoWorld’s recognition reflects a broader shift we’re seeing in enterprise AI: value now comes from the governed vertical architecture around models, not the models themselves. 

We won this award because we solved the “Custom Build Trap”—the architectural bottleneck where promising pilots devolve into brittle glue code, untestable workflows, and systems no one wants to own after a production push. 

The Problem: The “Leaky Pipe” 

Many organizations are layering AI onto workflows that lack shared state or a single source of truth —what we call the “Leaky Pipe.” 

Take a standard industrial maintenance scenario. A critical pump goes offline. This triggers a scramble: engineers hunt through SCADA screens, technicians check paper logs, and managers coordinate via email. It is a workflow built on human heroics, not system intelligence. 

When you drop a foundational model into this workflow, it fails. The model might be able to write an email or summarize a shift record, but it has no access to real-time state, failure history, or asset context because it’s stored in multiple data silos and formats—so it generates output without situational awareness. 

Without a shared context layer, models and agents just add noise—generating more alerts and text without identifying the root cause of why the pump failed. 

To fix the leak, you need a vertical architecture. 

Inside the Award-Winning Stack 

InfoWorld’s recognition validates our architectural thesis: Intelligence is a commodity, but Context is not. Here is the stack that separates a plug-and-play AI platform from a horizontal toolkit.

1. Context Layer: The Domain Knowledge Graph

Standard AI retrieval finds text. It doesn’t understand state. 

Our architecture is founded on a Domain Knowledge Graph (DKG)—essentially, a digital map of your business showing how customers, products, suppliers, and processes relate to one another. This ensures every model, agent, and API reads and writes against the same semantics, replacing thousands of lines of maintenance-heavy glue code with a reliable, shared source of truth. 

Why is this hard to build? Most teams underestimate the hidden cost of connecting the data. It sounds simple to match “J. Smith” in a transaction log to “John Smith” in a KYC document. But doing this accurately across millions of fragmented records in real-time is a massive engineering undertaking. 

If you try to build this semantic layer yourself, you aren’t building an AI app—you are accidentally building a Master Data Management (MDM) platform. This is undifferentiated heavy lifting that distracts fromyour core product roadmap.  

SymphonyAI delivers this map pre-built, accelerating time-to-value so you can ship features instead of debugging plumbing.

2. Orchestration: Stopping “Agent Sprawl”

In the rush to adopt agentic AI, many teams are creating “Agent Sprawl”—deploying autonomous agents that operate in silos without a shared map. Autonomy isn’t the problem—uncoordinated autonomy without shared context is. 

Our Orchestration Layer acts as a central nervous system. It deconstructs complex workflows (like investigating a financial crime alert) and assigns the specific AI type required for each step—using Predictive AI for anomaly detection, Generative AI for summarization, Agentic AI for automation, or a human for final sign-off. 

Crucially, every step draws directly from the Context Layer. Because the orchestration engine is grounded in the DKG, it makes decisions specific to your unique business context—like recognizing a “VIP status” or a “Maintenance Window”—rather than offering generic, unanchored guidance.

3. Governance: Policy-as-Code

In DIY horizontal platforms, governance is often an afterthought. In Eureka AI, governance is an active architectural gate. We have moved beyond “logging” to a Control Plane that embeds Policy-as-Code and deterministic guardrails directly into the workflow. 

• Authentication and Authorization: We implement enterprise-grade RBAC that extends down to the agent level. Every user and system component is verified; an agent investigating a fraud alert only “sees” the PII it is authorized to access based on role-based policies. 

• Control Plane Signal Sets: At every decision point, the system records a standard signal set: inputs, policy checks, model/tool choices, and confidence/uncertainty scores. If an agent’s confidence falls below a set threshold or its narrative cites a transaction not found in the DKG evidence log, the system rejects the output before it reaches a user. 

• Monitoring and Traceability: We provide granular visibility through real-time observability. This includes tracing AI agents and their decision flows throughout the workflow, ensuring that every automated action is traceable and compliant. 

• Continuous Learning & Compounding Value: With every workflow cycle, the system captures feedback from outcomes and human overrides. This creates a closed feedback loop where the architecture becomes smarter and more effective over time—turning every maintenance loop or fraud check into an asset that improves future reliability. 

Stop Patching, Start Scaling 

The InfoWorld award confirms a shift in the market. The industry is realizing that the gap between a demo and a deployed solution is closed by vertical architecture. 

While others are still maintaining bespoke ingestion scripts and debugging agent sprawl, SymphonyAI customers are deploying governed, specialized AI that understands your business context out of the box. 

The Eureka AI Platform is the foundation of our specialized product platforms—CINDE (Retail), Sensa Risk Intelligence (Finance), IRIS Foundry (Industrial), and Apex (IT). 

The award validates the results delivered by these platforms—whether it is Sensa reducing false positives by 77% for financial institutions or IRIS Foundry slashing root cause analysis from 48 hours to less than 10 minutes in industrial manufacturing. To reveal the vertical architecture behind these outcomes, we have released “Scaling Production AI“, a playbook for enterprise leaders.