Featured
Snowflake Cortex Analyst vs Databricks Genie vs BigQuery Gemini: warehouse-native AI compared
An honest head-to-head of the three warehouse-native AI products. Where each wins, where each loses, and the architectural blind spot they all share.
Agami Founders
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.
Agami Founders
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.
Agami Founders
Latest
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
Agami Founders
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
Agami Founders
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.
Agami Founders
How AI Is Transforming Client Delivery for Market Research Firms
How AI helps market research teams scale client delivery with quality, consistency, and insight This is the fourth post in our five-part series on how AI is transforming key workflows in market research firms. In previous posts, we covered the big picture, business development, and knowledge management. “Can You Just
Agami Founders
Context Engineering, Part 3: Making Context Work
What does it take to make Context Engineering work in production? In Part 3, we break down the platform components, team setup, key pitfalls, and the real tradeoffs between building your own stack or buying a platform like Agami.
Agami Founders
AI for Knowledge Management in Market Research Firms
Market research firms amass vast insights, but poor discovery leaves them underused. AI-powered knowledge management democratizes internal expertise, enabling semantic search, AI summarization, and knowledge graphs that make insights instantly accessible.
Agami Founders
How AI Helps Market Research Firms Win More Business
Solving the business development bottleneck with speed, context, and consistency This is the second post in our five-part series on how AI is transforming key workflows in market research firms. If you missed the first post on the big picture, you can read it here. The Proposal Is Due in
Agami Founders
Scaling Health Insurance Claims Operations: Achieving Efficiency Without Increasing Headcount or Sacrificing Accuracy
Real-Life Scenario: The Mounting Challenge of Health Insurers’ Claims Operations Picture a leading health insurer at the peak of annual enrollment. The claims operations team faces mounting pressure as incoming claims flood their systems. Every day, a battalion of reviewers sifts through piles of structured data, scanned medical records, and
Agami Founders
Open source
LiteBi is the open-source skill that powers Agami's data agent.