Kuldeep Paul

Kuldeep Paul

Agentic AI | LLM | Product Management | Product Marketing | Data Science | SaaS

Building AI Products in 2025: A Practical Blueprint For Speed, Reliability, and Scale

Building AI Products in 2025: A Practical Blueprint For Speed, Reliability, and Scale

AI products have moved from prototypes to mission-critical systems. Customer support agents, claims triage assistants, research copilots, and sales outreach bots now drive real revenue and carry real risk. In 2025, the bar is higher than ever: teams must ship faster, measure quality continuously, and prove reliability under real-world conditions.

AI Observability in 2025: How to Monitor, Evaluate, and Improve AI Agents in Production

AI Observability in 2025: How to Monitor, Evaluate, and Improve AI Agents in Production

AI systems have crossed the threshold from prototypes to production-critical infrastructure. Customer support bots resolve thousands of tickets. Document agents triage insurance claims. Voice agents interview candidates in real time. When these systems fail, it impacts user trust, revenue, and brand. AI observability is how you stay ahead of that

LLM Observability: Best Practices for 2025

LLM Observability: Best Practices for 2025

As large language models (LLMs) become integral to enterprise AI applications, the need for robust observability has never been more pressing. In 2025, organizations deploying LLMs must move beyond traditional monitoring tools and adopt best practices tailored to the unique challenges of generative AI. This blog explores the evolving landscape

The Best Prompt Management Tool in 2025: Why Maxim AI Leads the Way

The Best Prompt Management Tool in 2025: Why Maxim AI Leads the Way

Prompt management is now a foundational pillar in the development and deployment of advanced AI systems. As organizations scale their use of large language models (LLMs) and agentic workflows, the complexity and volume of prompt engineering have grown exponentially. In 2025, effective prompt management is not simply a technical requirement—

Choosing the Right AI Evaluation and Observability Platform: An In-Depth Comparison of Maxim AI, Arize Phoenix, Langfuse, and LangSmith

Choosing the Right AI Evaluation and Observability Platform: An In-Depth Comparison of Maxim AI, Arize Phoenix, Langfuse, and LangSmith

As AI agents become integral to modern products and workflows, engineering teams face increasing demands for reliability, quality, and scalability. Selecting the right evaluation and observability platform is crucial to ensure agents behave as intended across varied real-world scenarios. This article provides a comprehensive, technically detailed comparison of four leading

Top 5 LLM Observability Platforms for 2025: Comprehensive Comparison and Guide

Top 5 LLM Observability Platforms for 2025: Comprehensive Comparison and Guide

TL;DR We compare the top 5 LLM observability platforms of 2025, including Maxim AI, LangSmith, Arize AI, Langfuse, and Braintrust. Each platform is evaluated across five key dimensions: tracing, evaluation, integrations, security, and scalability. The comparison outlines their strengths, features, and trade-offs to help you choose the right observability

How to build a Real-Time AI Interview Voice Agent with LiveKit and Maxim: A Technical Guide

How to build a Real-Time AI Interview Voice Agent with LiveKit and Maxim: A Technical Guide

AI-powered interview agents are rapidly transforming the recruitment landscape, enabling organizations to conduct scalable, consistent, and insightful candidate assessments. By leveraging real-time voice capabilities and advanced observability, these systems offer a glimpse into the future of automated interviewing. This guide presents a comprehensive walkthrough for building a robust AI Interview