blogs

Enterprise Document AI architectures are rapidly evolving beyond traditional OCR toward Vision Language Models (VLMs) and agentic AI systems that can interpret complex layouts, reason over multimodal inputs, and automate end-to-end document workflows. While these approaches unlock significantly higher accuracy and automation, they also introduce new architectural failure modes that are especially critical in enterprise

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