Building a fraud investigation agent on TigerGraph for Hacker House Goa

How we combined TigerGraph 4.2.5, Model Context Protocol (MCP), calibrated Graph ML, Vector GraphRAG, and deterministic policy enforcement to build an enterprise-grade autonomous fraud investigator.

Most investment dashboards are easy to build when every asset comes from one provider. The real...

A pilot said the gating made the model MORE sycophantic (+10pp). The full run said +0.5pp of pure noise. Three independent judges then all said: not only useless, slightly harmful. A 38-hour CPU-run experiment log, with data.

The Jev model from TypeSafe AI introduces a System One approach, delivering structured data rapidly...

Team Thota Sai Eswar Srinath — Team Leader & Agentic Architecture Nikhil Kadiri —...

The most useful thing I learned building this: if you want an image model to work well with text,...

Introduction The moment you have more than one LLM call in production, you have a routing...

Grouped Value Attention slashes transformer KV memory by roughly 45 % without hurting benchmark...

An agentic fraud investigator on TigerGraph: GSQL, GraphRAG, MCP, and a deterministic policy core the LLM cannot touch.

Agents report success for actions that never happened — no error, clean trace, and every...