Technology

Purpose-Built Technologies for the Full Enterprise AI Lifecycle

DistilleryWeaveContext MeshContext ViewsJourneyPersonalizationApprentice

The semantic layer for enterprise knowledge.

Context Views is the semantic layer that organizes enterprise data into a foundation of human-understandable objects. Your experts navigate, curate, and build applications with it.

The Problem

Enterprise data sits in dozens of systems, in forms only the people who built it can interpret. Every team ends up reinventing the same translations from raw data into the objects they actually work with.

Context Views — Enterprise Semantic Layer

Context Views is the semantic layer in Distillery's Context Layer. It draws on the governed context in Context Mesh to produce a foundation of human-understandable objects: business entities, relationships, and constraints that your experts can navigate, your developers can build on, and your agents can reason from. The same layer serves all three.
Context Views illustration

Capabilities

01

Task-Specific Context

The right view of the data, for the work in front of you.

Context Views creates representations structured around the task itself. Each schema is task-shaped: assembled from the right business objects, the right relationships, the right constraints, in the order the work requires. Built for SMEs who know the work, built for developers building agents, built for agents reasoning at runtime.

02

Compounding SME Knowledge

Every SME judgment, propagated across the fleet.

Directed Review lets SMEs resolve ambiguity in enterprise knowledge once. The resolution becomes a reusable update to the schema itself: better business objects, sharper relationships, cleaner constraints. Every agent that follows benefits from every SME decision that came before.

03

Provenance and Governance

Every piece of context, accountable.

Every business object an agent reads through Context Views is traceable to its source, time-stamped to its currency, and gated by who's authorized to see it. The provenance is in the schema itself, not in a separate audit log. Trust is structural.