As artificial intelligence moves beyond experimentation and into core business operations, the conversation is shifting from what AI can do to how reliably it can do it. While advances in large language models continue to capture attention, enterprises are increasingly focused on a different set of priorities: trust, governance, scalability, and operational resilience.
For engineers working at the intersection of AI and enterprise software, the challenge is no longer building impressive demonstrations but creating systems that can function consistently in real-world environments. Among those navigating this transition is Vikas Reddy Challaram, whose career spans language research, AI orchestration, and large-scale software infrastructure.
Drawing on experiences across IIT Kharagpur, ConvertIAS, DoubleO.ai, and ZoomInfo, Challaram believes the future of enterprise AI will be shaped as much by engineering discipline as by advances in model intelligence.
From Language Research to Real-World Systems
Challaram’s journey into artificial intelligence began at Indian Institute of Technology Kharagpur, where he worked on natural language processing research, including a study on Sanskrit word segmentation. The experience sparked an interest in language systems, but his career soon evolved beyond academic research into solving practical engineering problems.
Rather than focusing solely on model performance, he became increasingly interested in the infrastructure required to make AI systems useful and dependable in production environments.
Building for Scale in Education Technology
One of the early chapters of his career was at ConvertIAS, where he contributed to the technology supporting large-scale answer evaluation and feedback workflows for civil services aspirants.
Operating in an environment where thousands of students depend on timely and accurate assessments, the platform required software systems capable of handling significant operational demands.
“Whether you are building EdTech infrastructure or AI agents, the core problem is similar,” Challaram said. “You are designing systems that need to be dependable when thousands of users rely on them simultaneously.”
That philosophy carried into his role at DoubleO.ai, a Y Combinator-backed startup focused on AI agent orchestration. There, Challaram worked on systems designed to convert business processes into structured, executable workflows powered by AI.
Following DoubleO.ai’s acquisition by ZoomInfo, he transitioned into a principal engineering role, helping integrate AI technologies into a much larger enterprise software ecosystem.
According to Challaram, one of the biggest misconceptions about enterprise AI is that success depends primarily on model capability. While intelligence remains important, businesses ultimately need systems they can trust.
Many AI applications can deliver compelling demonstrations, but enterprise adoption depends on whether those systems can be monitored, audited, governed, and controlled once they become part of critical workflows.
“The hard part of enterprise AI is not introducing a model into a workflow. It is making that workflow fit the systems, data, controls, and operating standards that already exist around it.” Challaram said. “The real challenge begins when enterprises ask questions around governance, observability, and operational reliability.”