> For the complete documentation index, see [llms.txt](https://scys-organization.gitbook.io/scyllaorg.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://scys-organization.gitbook.io/scyllaorg.com/welcome-to-the-future-of-intelligence..md).

# Welcome to the Future of Intelligence.

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### I keep hearing about AI agents... but I don't know how to use them

ScyOrg bridges the gap between hype and hands on usage. It's not just a marketplace for AI agents, it teaches you how to leverage each one as a pro. 
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### I don't have the time to try out 100 AI agents to find what really works more effectively. 

ScyOrg curates the top 1% of AI agents, already tested, proven and filtered. No fluff, just what is actually powerful and practical.
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### Even when I find a good tool, I don't know how to apply it to my work as a business founder or freelancers. 

ScyOrg provides step-by-step learning via pdfs and video tutorials that shows you exactly how to apply each AI agent to your task, business, or industry. 
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### I wish there was a way to support open-learning while still getting access to Elite resource. 

ScyOrg uses a donation-based unlock system. You get the value first — then give what you can via crypto or fiat. This makes it accessible to everyone, but still supports the creators and curators.
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### I want to be AI-savvy without doing a full tech course.

ScyOrg is micro-learning for AI skills. No fluff, no 8-week bootcamps — just straight-to-the-point knowledge tailored per AI agent.
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### I want to use AI to work faster, smarter, and make more money.

ScyOrg doesn’t just show tools — it shows outcomes. How AI agents can help you:\
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■ Save time\
■ Launch faster\
■ Automate work\
■ Stand out in your job or freelance gigs\
■ Build smarter businesses.
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## A New Way To Work.

How Morgan Stanley iterated to ensure quality and safety
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As a global leader in financial services, Morgan Stanley is a relationship business. Not surprisingly, 
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there were some questions across the business about how AI could add value to the highly 
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personal and sensitive nature of the work. \\

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The answer was to conduct intensive evals for every proposed application. An eval is simply a 
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rigorous, structured process for measuring how AI models actually perform against benchmarks 

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in a given use case. It’s also a way to continuously improve the AI-enabled processes, with expert 
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feedback at every step.
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How it started
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Morgan Stanley’s first eval focused on making their financial advisors more efficient and effective. 
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The premise was simple: If advisors could access information faster and reduce the time spent on 
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repetitive tasks, they could offer more and better insights to clients.
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They started with three model evals:
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01 Language translation - Measuring the accuracy and quality of translations produced 

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by a model.
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02 Summarization - Evaluating how a model condenses information, using 

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agreed-upon-metrics for accuracy, relevance, and coherence.
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03 Human trainers Comparing AI results to responses from expert advisors, 
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grading for accuracy and relevance. 

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These evals and others gave Morgan Stanley the confidence to start rolling the use cases 

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into production.

How it’s going —

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Today, 98% of Morgan Stanley advisors use OpenAI every day; access to documents has jumped 
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from 20% to 80%, with dramatically reduced search time; and advisors spend more time on client 
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relationships, thanks to task automation and faster insights.

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The feedback from advisors has been 
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overwhelmingly positive. They’re more 

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engaged with clients, and follow-ups that 

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used to take days now happen within hours.