Through enterprise engineering and applied AI research, Sai Krishna explores how teams can build dependable systems and measure the value they deliver.
An AI prototype can make a compelling first impression. The harder questions emerge when people begin using it every day. Does it handle unfamiliar inputs? Can its mistakes be traced? How much does each completed task cost?
These questions connect Sai Krishna’s work across enterprise systems and generative AI. His experience includes working with Fortune 50 companies, where architectural decisions affect services that large numbers of people depend on.
Among those projects was his work building and architecting the initial versions of an interactive voice response system for a Fortune 50 company. The system handles approximately 500 million phone calls a year. At that volume, reliability becomes a concrete engineering concern. How requests move through the system, how failures are handled and how services remain available under load all matter to the experience of the caller.
That experience informs his approach to AI adoption. Teams need to define the task, understand its operating conditions and establish how success will be measured. A working demonstration offers an early indication of what is possible. Sustained use requires closer examination of the system’s behavior, its failure cases and its operating costs.
Engineering beyond the prototype
Sai’s work at TrueFoundry and contributions to open-source projects including Cognita and TrueForge bring this perspective to the tools developers use to build AI applications. Connecting a model to information and tools introduces decisions about how the application retrieves context, uses outputs and responds when an operation fails.
These decisions deserve attention alongside model selection. An application may produce a convincing answer while still leaving developers uncertain about how it reached that result or whether a change has improved its performance. Evaluation gives teams a way to investigate those questions and compare versions against a defined task. The comparison should explain what improved, what deteriorated and which uncertainties remain unresolved.
Through his developer relations work, Sai also helps others put these ideas into practice. He conducts workshops at AWS offices and other venues in the area, helping developers understand AI tools, work through implementation choices and apply what they learn to their own projects.
Testing where AI is needed
The relationship between quality and operating cost is central to PLaND, Sai’s research methodology for replacing stable model-mediated operations with evaluated deterministic implementations.
The approach examines a workflow step by step. Where conventional code may handle an operation, a replacement is introduced and tested to determine whether it preserves task quality. Each proposed change can then be accepted or reversed based on the results.
This makes efficiency a measurable engineering decision. Fewer model calls may reduce resource use, but the resulting workflow must still perform its job. A replacement that loses necessary behavior does not justify itself through lower cost alone.
His work on TriageBench examines evaluation and error analysis in a clinical triage setting. It provides a specific domain for his broader interest in understanding AI performance and its limits.
Across these projects, Sai’s focus remains on building systems whose behavior can be examined and improved. The work continues after a successful demonstration, through testing, architectural refinement and evidence that the application can support its intended use.
