Make your enterprise systems readable by AI agents.

Vyuh connects to SAP, industrial systems, and custom databases, extracts the shared meaning across them, and compiles a governed semantic model your agents can reason through — without hallucinating entity matches.

How It Works

From raw systems to a unified semantic model

AI agents fail in the enterprise because the same business objects exist in different schemas, definitions are tribal knowledge, and raw API access fails open. Vyuh solves this — not with guardrails bolted on after, but with structure extracted from the source.

01

Connect Your Systems

SAP, industrial systems, Stripe, internal APIs, custom databases. Point Vyuh at what you run today. We connect — you don't change a thing.

02

Extract the Ontology

Vyuh discovers every schema, object, capability, and data flow inside each system. The structure that took years to build — mapped in minutes.

03

Resolve Across Systems

An invoice in SAP. A payment in Stripe. A transaction in your database. Vyuh recognizes they're the same business event and reconciles them into a single entity. Across every system, for every overlapping concept.

04

Build the Governed Model

One unified semantic model with permissions, approvals, and audit trails. Your agents don't get raw API access — they get a governed capability layer that only allows what you approve.

05

Agents Reason Through It

AI agents reason and act across your entire landscape through one model. Not one system at a time — all of them, at once.

Your agents are only as good as the systems they can read.

If you're deploying AI agents across enterprise systems and hitting the wall where nothing reconciles — SAP and Stripe describe the same customer differently, equipment databases don't agree with CAD models, agents hallucinate entity matches — we built the layer that fixes that.

Built by a solo founder — quantitative research at Goldman Sachs, PhD in aerospace engineering.