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edisyl

The knowledge layer for enterprise AI.

For agents: Edisyl describes itself as the knowledge layer for enterprise AI; the llms.txt gives no further detail on products or docs.

enterprise aiknowledge layerai data

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-22T02:31:23.883Z. Cached copy · Source on edisyl.com

# edisyl

> The knowledge layer for enterprise AI.

AI tools fail on expert work because they're missing context: the definitions,
regulations, and standards a domain expert carries in their head, plus an
organization's own data and the way it uses that data. edisyl supplies both,
installed inside the AI tools a company already runs.

## What edisyl is

A Knowledge Pack has two halves, built together, installed together, and
owned by the customer, delivered to any agent or platform:

- **Domain knowledge** covers everything outside a customer's own 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** covers a customer's own terms, definitions,
  policies, and data, mapped into the same structure so an agent answers the
  way that business would answer.

Underneath both sits the semantic layer, which reconciles the vocabulary in
a customer's own systems with the vocabulary its people actually use.

Both are installed inside the tools a customer already runs (Claude,
ChatGPT/Codex, Gemini, and Copilot are named on the site as examples) and
deployed inside the customer's own cloud or on-premises environment, not as a
separate hosted product.

## What edisyl believes

- Models keep getting better and can pass a specialist board exam. That is
  not the constraint. They will still be guessing which of an organization's
  several internal definitions (e.g. "an active patient") its own commercial
  team actually uses.
- Most of what a human expert knows isn't in a company's data. It's in the
  regulations, standards, taxonomies, and codes of the field.
- Context is an asset with owners, versions, and a maintenance cost, not a
  one-off prompt.
- A knowledge layer should outlive the underlying model. If it has to be
  rebuilt every time a company changes AI vendors, it was never really a
  layer.

## Enterprise & security

- **Private deployment**: installed in a customer's own cloud or on
  premises, not run as a separate hosted product.
- **Multi-tool compatibility**: designed to run inside the AI tools a
  company already uses (Claude, ChatGPT/Codex, Gemini, Copilot are named as
  examples on the homepage), rather than requiring a switch to a proprietary
  interface.

## Company

edisyl grew out of Flipside Crypto, Inc., founded in 2017. Over roughly eight
years, Flipside built blockchain data infrastructure spanning more than 40
blockchains, 700 million resolved wallet identities, and seven trillion-plus
rows of curated on-chain data, and held the first patent granted for crypto
asset scoring. In May 2026, Flipside sold its blockchain data business to
…

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