# Data dashboard on Profit and Losses of $DUMMY token

Source: https://flipsidecrypto.xyz/alessio9567/dummy-profit-and-losses-dashboard-wLnUlu

## Summary

Edisyl argues that AI tools fail on expert work because they lack the definitions, regulations and standards a domain expert carries, along with a company's own data and how it uses that data. Its Knowledge Pack has two halves: domain knowledge, covering partner data, regulatory sources, market data and standards, which is built and maintained by agents steered by subject matter experts, and proprietary knowledge, covering a company's own terms, policies and data. A semantic layer reconciles the vocabulary in a company's systems with the vocabulary its people use. The knowledge layer is installed in a customer's cloud or on its premises and works inside Claude, Codex, Gemini and Copilot.

## Article

The knowledge layer

Domain knowledge, built and maintained by agents. Proprietary knowledge, mapped from your systems. Delivered to any agent or any platform.

AI tools fail on expert work because they are missing context: the definitions, regulations and standards a domain expert carries in their head, plus your own data and the way your company uses it. Edisyl supplies both, inside the tools you already run.

DeploymentInstalled in your cloud or on your premisesWorks inside the AI tools you already run

Claude

Codex

Gemini

Copilot

We work with enterprise teams building on their own data. Tell us what you are building and we will tell you whether the knowledge layer fits.

Contact us

The unit of value

A Knowledge Pack. Two halves, built together, installed together, and owned by you.

Domain knowledge

Everything outside your walls: partner data, government and regulatory sources, market data, standards and codes. Built and maintained by fleets of agents, steered by subject matter experts, so it stays current.

Proprietary knowledge

Your own terms, definitions, policies and data. Mapped into the same structure, so an agent answers the way your business would answer.

Semantic Layer

Underneath both sits the semantic layer, which reconciles the vocabulary in your systems with the vocabulary your people use.

What we believe

01The models will keep getting better, and can pass a specialist board exam. And that is not the constraint. They will still be guessing which of your three definitions of an active patient your commercial team uses.Capability

02Most of what an expert knows is not in your data. It sits in the regulations, standards, taxonomies and codes of the field.Knowledge

03Context is an asset, not a prompt. It has owners, versions and a maintenance cost, the same as any other system of record.Ownership

04The knowledge layer should outlive the model. If your context has to be rebuilt every time you change vendors, it was never a layer.Durability