blogs

Most banks and insurers are no longer experimenting with AI. They already have chatbots, OCR pipelines, risk models, and fraud classifiers in production. Yet operational costs remain high, cycle times remain slow, and human bottlenecks still dominate critical workflows. Traditional AI systems are good at individual tasks. Financial services, however, are dominated by multi-step, cross-system,

In this blog, we break down why standard chunking fails for structured data and how to design table-preserving chunking strategies using modern RAG best practices. Each approach comes with implementation guidance, use cases, and architecture fit. Why chunking fails on tabular data? Tables aren’t text — they are relational knowledge graphs compressed into rows and

Prompt data types matter more than most developers realize. It started with a simple anomaly. We were building a complex multi-turn conversational agent for a client. The logic was sound, the model was the latest GPT-4, and the context retrieval was optimized. Yet, the agent felt… sluggish. Worse, it was hallucinating during complex reasoning tasks, and

OpenAI’s IndQA benchmark shifts the conversation from translation to native, culture-aware reasoning. Here’s what it means for AI product teams, startups, and enterprises, Published by InteligenAI — 12 November 2025 In today’s age of large-language models (LLMs) and generative AI, one major gap persists: language and cultural depth. Most benchmarks, systems and deployments assume English

November 8, 2025 Open source projects for engineers are transforming development workflows. Discover three open-source projects (opencode, DeepCode, Llama-Factory) that bring AI into developer workflows: inside the terminal, from paper-to-code, and from model-to-deployment. Modern engineering teams want AI that fits their workflow. Whether you’re a backend engineer, a research scientist, or a DevOps lead, developer-native