Your production data is already a goldmine of real tasks, failures, and human corrections. It captures judgment about what a successful outcome requires and over time this judgement encodes into institutional knowledge.
NeoEval, our flagship evaluation engine, turns that experience into evaluation environments grounded in your real tasks, workflows, and edge cases.
Where you can replay your agent in your environment. Evaluate and optimize against the metrics that matter to your business.
That’s the foundation of your own intelligence lab - where production experience compounds into better outcomes and intelligence you own.
We’re slowly opening up access to build with NeoSigma. Join the waitlist here: neosigma.ai/waitlist
Your production data is already a goldmine of real tasks, failures, and human corrections. It captures judgment about what a successful outcome requires and over time this judgement encodes into institutional knowledge.
NeoEval, our flagship evaluation engine, turns that experience into evaluation environments grounded in your real tasks, workflows, and edge cases.
Where you can replay your agent in your environment. Evaluate and optimize against the metrics that matter to your business.
That’s the foundation of your own intelligence lab - where production experience compounds into better outcomes and intelligence you own.
We’re slowly opening up access to build with NeoSigma. Join the waitlist here: neosigma.ai/waitlist
https://x.com/gauri__gupta/status/2118036805865890022?a=b7FK...
From the article, here's examples of ways in which AI can fail. These are the things we, or someone must look out for:
(AI/Agent)
Linked a file that wouldn't open (25 sessions)
Returned a template instead of the finished file (13)
Left placeholders where numbers belong (12)
Made claims the data doesn't support (6)
Ignored an explicit instruction (4)
Couldn't explain missing data (3)
Answered in the wrong language (2)
Refused a how-to question about the product (2)