# Data dashboard on profit and loss of GodHatesNFTees holders

Source: https://flipsidecrypto.xyz/alessio9567/god-hates-nf-tees-profit-and-losses-dashboard-%F0%9F%93%8A-%F0%9F%92%B9-jyxtyx

## Summary

Edisyl sells a "knowledge layer" of Knowledge Packs that pair outside domain knowledge, such as regulatory sources and standards, with a company's own definitions and data. They are installed in the customer's cloud or on premises and run inside Claude, Codex, Gemini and Copilot. It argues that AI tools fail on expert work for lack of context, and that models will keep improving but still need to know which of a company's definitions applies. It holds that context should be treated as an owned asset with versions and maintenance costs, and that the knowledge layer should outlive any single model. Nothing here covers GodHatesNFTees holders or profit and loss.

## 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