Retrieval-Augmented Generation (RAG) has emerged as a dominant framework for leveraging Large Language Models (LLMs) to generate responses grounded in extensive textual corpora. Within this architecture, the retrieval component plays a critical role in determining the overall system accuracy by surfacing the most relevant text chunks based on semantic similarity to the user query. Typically, this… more

Constrained optimization lies at the heart of many complex systems where multiple limited resources, like time, labor, equipment, or energy, must be allocated efficiently while satisfying strict operational constraints. These problems are often high-dimensional, dynamic, and involve tightly coupled dependencies between variables. Traditional methods like Mixed-Integer Linear Programming or Heuris… more

Extended Abstract:- Constrained optimization lies at the heart of many complex systems, where multiple limited resources must be allocated efficiently to achieve specific goals, while satisfying strict operational constraints. These problems generally sit at the core of logistics optimisation, smart scheduling, etc. They are often high-dimensional, dynamic, and involve tightly coupled dependencie… more

We built a production multi-agent assistant for a complex enterprise planning workflow where AI assists, rather than replaces, human decision-making. Users express their intent through natural language, the system extracts structured semantic information for human review, and multiple specialist agents collaborate to retrieve data, perform analysis, and generate recommendations. Our initial archi… more

Abstract Building an agent that works in a demo is no longer the hard part. The challenge begins when the same system must operate reliably in production, handling evolving requirements, multiple agents, long-running workflows, changing prompts, evaluation, latency constraints, and operational failures. Every team encounters different problems, yet many of the underlying engineering questions are… more