The knowledge layer for enterprise AI.

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

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

  1. 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
  2. 02Most of what an expert knows is not in your data. It sits in the regulations, standards, taxonomies and codes of the field.Knowledge
  3. 03Context is an asset, not a prompt. It has owners, versions and a maintenance cost, the same as any other system of record.Ownership
  4. 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

Contact us

Fill out the form and tell us what you are building, or skip it and email us directly at hello@edisyl.com.