AI In the Enterprise Archives - Arize AI
How to measure AI productivity: From LLM token costs to business value with Arize AX
AI productivity is best measured by connecting AI usage to validated downstream outcomes. Tokens, prompts, and generated lines show activity, but they do not prove value. A better measurement model tracks the cost of AI work, scores the quality and task success of that work, then joins each trace to outcomes such as merged PRs,…
8 minutes read
By Duncan McKinnon
How to measure AI productivity
Using context graphs: build a data moat like Google’s using your enterprise data
Enterprise software is on the verge of its first compounding data loop, the same kind of self-reinforcing mechanism that built the most valuable consumer businesses of the last twenty years. The unit of capture is the decision trace: a structured record of how an agent and a human together resolved a decision. As traces accumulate…
7 minutes read
By Jim Bennett
From First Eval to Autonomous AI Ops: A Maturity Model for AI Evaluation
Every team runs evals. Almost none have an evaluation practice. The difference is the gap between a one-off notebook and a system that continuously assesses, alerts, and acts on what it finds. The teams that close that gap don’t do it in one leap — they grow into it. Here’s how we think about that…
5 minutes read
By Cam Young
From First Eval to Autonomous AI Ops
Why Banks Adopt the Arize Ecosystem
This post covers the organizational and regulatory patterns that shape AI platform decisions in banking, and why the Arize ecosystem aligns with how these institutions actually operate. Federated Architectures Large banks rarely operate as a single, centralized technology organization. Instead, they are structured as federated systems of business lines, each with its own priorities,…
12 minutes read
By Dat Ngo
Why Banks Adopt the Arize Ecosystem
Arize AX Adds Native Support for NVIDIA NIM as AI Model Provider
We’re excited to announce that Arize AX now supports NVIDIA NIM as a native AI model provider. Enterprises running NIM-deployed models can now connect them directly to the Arize platform and access them from the playground, run experiments, online evaluations, and enable production monitoring — now as a dedicated first-class integration within Arize. What is…
3 minutes read
By Richard Young
Arize AX Adds Native Support for NVIDIA NIM
OWASP Top 10 for Agentic Applications: Compliance Guide
This guide maps the OWASP Agentic Security Initiative (ASI) top ten risks to specific Arize AX observability features and metrics you should implement to detect, monitor, and mitigate threats in your agentic AI systems. The OWASP Agentic Security Initiative is a specialized project under the Open Web Application Security Project (OWASP) Generative AI Security Project….
8 minutes read
By Natalia Skaczkowska-Drabczyk
OWASP Top 10 for Agentic Applications
EU AI Act Compliance: What AI Engineering Teams Should Monitor
The EU AI Act is no longer a distant regulatory concept; it is in force and enterprises are road testing their real-world implementation. The core law is Regulation (EU) 2024/1689, and the full legal text is available on EUR-Lex. The Act went into effect in August 2024, with different obligations phasing in over the next…
6 minutes read
By Hakan Tekgul
Rise of the Agent Engineer: Trunk Tools’ Bobby Vinson
Trunk Tools is building the brain behind construction, transforming the $13 trillion construction industry. As a premier AI agent platform for the built environment, Trunk Tools deploys solutions that streamline construction data management, automate tedious and repetitive tasks, and minimize waste. In this interview, we catch up with Trunk Tools AI Evaluation Engineer Bobby Vinson…
3 minutes read
By David Burch