Production Graveyard #1: Why naive prompting isn't a data agent
A staging Postgres taken down. Wrong customers in dashboards. 4% revenue drift. Three production failures that taught us a data agent is context engineering, not prompting.
The Semantic Model Is Having Its Moment
A decades-old BI concept just hit its all-time search peak. Every AI data agent in production now sits on top of a semantic model, and most are locked to one vendor's stack. Here is the history, the current landscape, and the open standard that breaks the lock-in.
Buy a CRM Subscription. Own Your Data Agent.
AI is killing SaaS, but the data agent is the one piece you should own. Here is the architecture OpenAI, Meta, and Notion converged on, and three ways to ship yours.
Document Processing - The Mapping Problem
Semantic mapping turns extracted data into reliable system state. Learn how to build, evaluate, and productionize it.
Document Processing: Validation is Plan A
Most document AI systems fail after extraction. This deep dive explains why validation breaks on an example invoice pipeline and how to fix it.
Document Processing isn't just Extraction
Document processing isn’t just LLMs and OCR. Learn how validation, normalization, and workflows make AI production-ready.
A Roadmap for CIOs to Turn AI Budgets into ROI
AI pilots stall before delivering value. This guide shows CIOs a six step roadmap to focus budgets on high-ROI workflows and measurable outcomes.
Why Most Generative AI Pilots Fail (and How to Be in the 5% That Succeed)
The State of AI in Business 2025 Report found that 95% of generative AI pilots fail to deliver ROI. This post summarizes the report’s findings and shares Agami’s perspective on how to design pilots that scale into production.
Unlocking the Potential: Top 3 AI Use-Cases in Commercial Lending
AI is no longer a pilot experiment. It is rapidly redefining how commercial lending teams assess risk, move files, and make decisions. For leaders who own credit quality and throughput, the question is how to harness AI to improve margin, control, and speed. Top 3 workflows that matter the most
Unlocking the Future: The Critical Importance of Private AI for Enterprises
Privacy is not a nice-to-have for regulated industries. It is the difference between experimenting with AI and deploying it into production with confidence. Leaders who own security, compliance, and uptime know the path to value looks very different when sensitive data is on the line. The enterprise AI fork: public
Distilling a High-Performance Language Model
How Agami turned a large, costly AI model into a lean, production-ready asset for enterprise use.
Context Engineering vs Fine-Tuning vs Distillation: How to Decide
Choosing between context engineering, fine-tuning, and distillation is key to making LLMs work in production. This guide explains each approach, when to use them, their trade-offs, and how to balance cost, accuracy, and scalability.