Dhanunjay Mamidi.
As LLMs shift from experimental use to real-world production, a new challenge has emerged: reliability. These systems can produce helpful outputs that often seem just like human input. However, when these outputs are used in actual software, their behavior can become unpredictable. For engineering teams, the main concern is no longer if AI can generate results, but whether those results can be trusted in complex, interconnected systems.
Dhanunjay Mamidi has tackled this problem by looking at the system as a whole. Instead of seeing reliability as just a model issue, he studies how AI outputs are managed, understood, and checked once they are part of a production workflow. This move from focusing on generation to focusing on control is central to his approach.
The Gap Between AI Output and System Behavior
Large language models work well in controlled environments. They answer prompts, create structured outputs, and adjust to context in a single exchange. But enterprise systems bring in challenges that go beyond these simple settings. Outputs need to work with existing code, fit business rules, and function in systems that rely on consistency.
Mamidi found that many failures in AI workflows come from a gap between what the AI produces and how the system behaves. Often, generated responses are seen as finished, without enough checking or understanding of the larger system they are used in. This leads to a mismatch between the model's output and the system's needs.
As systems grow, this gap becomes harder to manage. For instance, one output might start several downstream processes or cause hidden inconsistencies. Traditional testing, which relies on fixed inputs and expected results, is not built to handle this kind of variability.
Mamidi addresses this by treating AI output as just one step, not the end result. The goal is to make sure outputs are understood in context and checked before being used.
Why Prompting and Fine-Tuning Fall Short
Many efforts to make AI more reliable focus on improving the model itself. Methods like prompt engineering and fine-tuning help guide outputs to be more accurate or structured. While these can help in simple cases, they do not fully solve how outputs act when used in bigger systems.
Mamidi recognized that the limitation is not solely within the model, but in the absence of control mechanisms around it. Even well-structured outputs can lead to unpredictable outcomes if they are applied without validation or awareness of system dependencies.
This difference is important. Making outputs better does not always mean they will work reliably in practice. Enterprise systems need outputs that can be trusted in many situations, which goes beyond just improving the model.
A System-Level Approach to AI Reliability
Mamidi's approach is to add structure around AI outputs instead of trying to remove all variability. He sees AI as just one part of a bigger system and focuses on how outputs are handled after they are created. His experience with backend systems and enterprise infrastructure shows that reliability comes from managing how changes move through the system, not from removing all variation. Using this idea, Mamidi built a framework with two main parts: keeping context and checking outputs before they are used.
The Role of Context
A main limitation of large language models is that they do not keep context between interactions. They can handle information in one prompt, but they do not naturally remember details across several steps in a workflow.
Mamidi solved this by adding a memory layer that helps the system keep track of context across different steps. This layer lets the system remember important information, so outputs are understood in a consistent way instead of on their own. This helps decision-making stay connected, and outputs become part of a continuous process that takes into account earlier inputs, the system's state, and what is expected.
This is especially important in enterprise settings, where workflows have many steps and need consistent understanding at each stage. By keeping context, the system can create outputs that better match how the application should work.
Validation Before Execution: Establishing Control
Context alone is not sufficient to ensure reliability, however. Outputs must also be evaluated before they are applied within a system.
To solve this, Mamidi created a validation layer that checks outputs before they are used. This layer makes sure each output matches what the system expects, follows business rules, and fits operational limits. Instead of assuming outputs are correct, the system checks them first. This control is key for using AI in production, where mistakes can spread quickly and cause bigger problems.
The validation layer does not replace current testing methods. Instead, it adds to them by focusing on AI-generated outputs. It makes sure these outputs meet system requirements before they affect other parts of the process.
From Experimentation to Infrastructure
Together, the memory and validation layers represent a shift in how AI is integrated into software systems. Instead of treating AI as an isolated capability, Mamidi's approach embeds it within a structured framework that governs how outputs are generated, interpreted, and applied.
This change lets AI move from just being tested to becoming part of core infrastructure. Systems using AI need to be as consistent as the rest of the application. By adding context and validation, Mamidi's work helps achieve this consistency while keeping the flexibility that makes AI useful.
This approach also shows a wider view of how modern systems change. As software gets more connected and flexible, reliability comes from working together across parts, not just controlling one area. AI should be part of this structure, not treated as something separate.
Iteration as a Path to Practical Solutions
The development of these systems did not follow a linear path. Mamidi's process involved testing multiple approaches, many of which revealed limitations when applied to real-world environments.
Early solutions that worked well in simple tests often did not handle the complexity of enterprise systems. Instead of just improving these ideas in isolation, Mamidi focused on quickly testing, reviewing, and adjusting based on real results. This helped the team see which methods worked reliably and which needed changes. Over time, this led to a framework that better matches how systems work in practice.
Positioning Within an Evolving Field
The challenge of making AI reliable within enterprise systems is becoming increasingly important as adoption continues to grow. And organizations are moving beyond experimentation and integrating AI into core workflows, where consistency and predictability are essential.
Mamidi's work tackles this shift by focusing on context, validation, and system-level integration. He has helped create a more structured way to make AI reliable, matching what modern software needs.
His perspective positions reliability not as a limitation of AI, but as a design problem that can be addressed through architecture and workflow design.
A Framework Built on Experience
Mamidi's work in AI reliability is grounded in a broader track record of building and scaling systems under real-world constraints. Earlier in his career, he led the development of an internal brokerage calculator at Société Générale, replacing a third-party tool and reducing operational costs by approximately one million dollars annually. He later developed and scaled backend infrastructure for SidsFarm during a period of rapid growth, supporting the company as it expanded its operations significantly.
As Co-Founder and CEO of Covlant, he has used this experience to build a company focused on better ways to validate software systems. He led the development of the platform's main technology, guided its growth through many changes, and built a team ready for a fast-changing industry.
His work reflects a consistent focus on identifying structural problems within complex systems and developing solutions that address them with precision. In the context of AI, this has meant moving beyond generation and toward control and ensuring that outputs can be trusted within the systems that depend on them.
As AI becomes a bigger part of software development, the need for reliable, context-aware systems will keep growing. Mamidi's work offers a framework to meet this need, based on both technical know-how and real-world experience.
