Raj Shukla, CTO of SymphonyAI – Interview Series

Raj Shukla drives SymphonyAI’s technology roadmap and execution, leading the engineering team that builds the Eureka Gen AI platform. With almost 20 years of AI/ML engineering and research experience, Shukla also has extensive enterprise AI SaaS experience from his engineering leadership roles at Microsoft, where his successful 14-year career included leading global AI science and engineering organizations across Azure, Dynamics 365, MSR and the search and advertising divisions. Raj has extensive experience in AI/ML across search, advertising, and enterprise AI and has built several successful AI SaaS products in both consumer and business domains.

SymphonyAI is an enterprise-AI company focused on building industry-specific AI applications that deliver immediate business value. Instead of generic models, it provides vertical solutions for retail, consumer goods, financial services, manufacturing, media, and IT, tackling challenges like forecasting, fraud prevention, operational optimization, and analytics. Its products are powered by the Eureka AI platform, which blends predictive, generative, and agentic capabilities into workflows tailored to each sector. Founded in 2017, the company has grown into a global leader in vertical AI, serving thousands of enterprise customers with scalable, domain-focused solutions.

You’ve worked at the forefront of AI innovation at Microsoft, Oracle, and now SymphonyAI—what originally drew you into the world of enterprise AI, and how has your perspective evolved over the years?

My journey into enterprise AI began with a core belief that companies should implement AI that solves real business problems, not just create AI for the sake of AI. I’ve seen that generic, broad-based AI solutions rarely deliver transformative value. At SymphonyAI, we’ve built our company strategy and culture on developing AI that understands specific industry challenges, from financial crime detection to shopper-focused retail merchandising to industrial connected worker empowerment. Enterprise-readiness adds another whole dimension – successful enterprise AI requires more than great technology, it demands exemplary data governance and architecture, sophisticated cross-functional collaboration and workflows, and full transparency and auditability.

What specific shortcomings do enterprises encounter with generic pretrained models, particularly in heavily regulated sectors like finance or healthcare? 

Generic pretrained models aren’t built for the high-stakes, heavily regulated environments of finance, healthcare, and grocery. Generic, pre-trained models encounter critical barriers, including the need for essential domain expertise to address industry-specific nuances and meet strict regulatory and compliance requirements that differ across geographies. Most importantly, they can’t deliver the accuracy and traceability that enterprises require, where errors could harm consumers or trigger regulatory violations. Whether it’s complying with anti-money laundering regulations or enabling a grocery to rapidly remove recalled items from distribution centers and shelves, SymphonyAI’s vertical AI technology is specifically built for the industries we operate in and trained on those industries’ ontologies, enabling them to make or automate decisions that directly create business impact.

Combining pretrained models with deep domain logic is increasingly seen as key to unlocking enterprise ROI—what are the essential components, such as industry knowledge, KPI alignment, and regulatory guardrails, that make this approach effective? 

Combining pretrained models with profound domain logic unlocks value by creating AI systems that understand business context and operational requirements. This approach succeeds when models are enhanced with industry-specific ontologies, aligned with enterprise KPIs to ensure that outputs directly serve measurable business objectives and are equipped with regulatory guardrails that provide necessary compliance frameworks and audit trails. When these elements work together, generic AI transforms into business-critical solutions that drive measurable outcomes while maintaining the reliability and compliance that enterprises demand.

IBM recently acquired Seek AI and launched Watsonx Labs in New York City, signaling a potential strategic shift in the AI landscape—what does this indicate about the future of M&A and investment trends in enterprise AI? 

IBM’s acquisition of Seek AI and the launch of Watsonx Labs is validation of the fundamental shift we’ve been anticipating: the enterprise AI landscape has shifted, signaling that the next wave of M&A will prioritize companies with pretrained vertical AI models that arrive with deep industry expertise, governance and regulatory guardrails. and outcome-driven KPIs. Strategic acquirers like IBM recognize that AI agents focused on enterprise data deliver immediate ROI when they understand specific industry workflows. The market is consolidating around the recognition that general intelligence needs vertical specialization to drive enterprise transformation.

At what point does a foundation model evolve into a domain-specific agent—what architectural milestones signal this transition? 

A foundation model does not naturally mature into a domain agent; it must be engineered into one. There is no direct path where a general model simply ‘gets smarter’ and becomes a bank investigator. The transition only happens when engineering teams stop relying on the model’s raw intelligence and start building the governed architecture around it—specifically injecting a context layer (like a Knowledge Graph) and an orchestration layer to force the model to follow a business process rather than its own probabilistic tendencies.

What are the core challenges in building agentic workflows that are both resilient and vertical-specific, and how does SymphonyAI address them? 

The core challenges in building resilient, vertical-specific agentic workflows are maintaining reliability across complex multi-step processes. SymphonyAI addresses these challenges through its multi-layered architecture, which embeds domain expertise directly into the agent, implements error handling with failure recovery, and maintains persistent context management across multi-session enterprise processes. This enables our agents to operate reliably in high-stakes regulated environments where resilience means maintaining accuracy, compliance, and operational integrity.

SymphonyAI emphasizes robust data foundations, knowledge graphs, and metadata layers—why are these capabilities critical for vertical AI agents, and why do many enterprises struggle to implement them? 

Robust data foundations and knowledge graphs are crucial for vertical AI agents to have meaningful sources, provide contextualized recommendations, and stay current with market, customer, and process changes across all levels of the enterprise. Most enterprises struggle to implement these capabilities because they require significant upfront investment in data architecture, specialized ontology expertise, and fundamental changes to existing data practices that many organizations find organizationally and technically daunting. That’s where an AI technology partner with deep experience and knowledge in that vertical is invaluable, including their ability to pretrain the AI on vast amounts of domain data and sources across myriad real-world customers in that industry.

In real-world scenarios—such as financial crime detection or retail forecasting—how does SymphonyAI merge predictive, generative, and agentic AI into cohesive “skills”?

SymphonyAI merges predictive, generative, and agentic AI into cohesive “skills” by creating integrated workflows where each AI product meets a specific business problem. In financial crime detection, our predictive models identify suspicious transaction patterns, and generative AI creates detailed investigation reports and risk assessments. At the same time, agentic AI orchestrates the entire workflow, automatically escalating cases, coordinating with compliance teams, and adapting investigation strategies based on real-time findings.

The key is that these aren’t separate AI tools, they’re integrated capabilities within domain-specific agents that understand business context, maintain workflow state, and can seamlessly transition between predictive analysis, content generation, and autonomous action to deliver complete business outcomes rather than fragmented AI outputs.

You’ve warned that many enterprise AI agents may stumble without robustness—what are the key characteristics of a well-engineered, fault-tolerant enterprise AI agent? 

Well-engineered, built-for-scrutiny enterprise AI agents require several critical characteristics. Although many businesses are rapidly investing in and deploying AI agents to enhance efficiency, productivity, and innovation, they often underestimate the groundwork necessary for success. Some vital aspects that well-engineered agents need to be successful are:

• Enterprise AI agents operate on enterprise data, which is often siloed and lacks proper programmatic access, permissions, and access controls. Agents need to be armed with the same authentication and authorization provisions as employees.
• Agents also need to recover from all forms of enterprise system failures, network outages, and flaky endpoints. The orchestration layer needs to enable long-running, durable, fault-tolerant workflows, which most popular LLM orchestrators don’t.
• LLMs will be non-deterministic and fail at tasks. Failure recovery, retries, and optimal path discovery need to be key features of agentic systems.

For CTOs contemplating building vertical AI platforms internally versus partnering with niche vendors, what advice would you offer? 

Building enterprise AI solutions across multiple industries, including retail/CPG, industrial, financial services and more, requires mastering both cutting-edge AI technology and deep domain expertise simultaneously to achieve real value from enterprise AI solutions. Our Eureka AI platform demonstrates how vertical-specific data sources, knowledge graphs, predictive models, and agents must be tailored to each industry, but this represents years of research investment and customer iteration that most internal teams lack. For businesses and CTOs looking to invest in AI, I advise them to choose solutions that deliver real results from day one. Vertical AI solutions provide those results, delivering users with data they can then use to create business value.

Looking ahead, how do you envision enterprise AI architectures—will federated vertical agents built on shared foundation models become the norm?

We won’t just see ‘federated’ agents; we will see governed agentic architectures. While shared foundation models provide the reasoning engine, they are essentially commodities. The ‘norm’ for successful enterprises will be deploying specialized, vertical agents that don’t just ‘talk’ to each other, but are rigorously orchestrated through a shared context layer. If you just have ‘federated’ agents built on foundation models, you get a noisy, hallucination-prone system—what we call the ‘leaky pipe’ of enterprise AI. To make this architecture scale in production, you need three specific layers that go beyond simple federation:

• Context (The Domain Knowledge Graph): Agents need to share a single source of truth, not just exchange probabilities.
• Orchestration: You need a ‘master architect’ that decides when to use a specialized agent and when to keep a human in the loop.
• Governance: The output must be legally and operationally safe before it leaves the system.

Thank you for the great interview, readers who wish to learn more should visit SymphonyAI.