The layer between the data you own and the AI you can trust.

Grand Central™ was built over five years at Untapped Global to consolidate investment data across fragmented markets, giving the team a reliable view of partner performance, repayment behavior and portfolio health.

EMBD, a spin-out wholly owned by Untapped Global, saw that the same canonical models could serve as the layer that sits beneath AI, and took Grand Central™ further. We connect to the systems a company already runs, reconcile them into a single model, and deliver data that AI can act on with confidence.

The platform now applies anywhere data is valuable but scattered, from private credit to biobanking and beyond.

One company, two ways in.

We meet customers as a partner who does the work end to end, and as a platform teams build on directly. Both feed the same compounding asset.

Go to market

Consulting-led delivery

We engage directly - and alongside domain consultants - to ingest, harmonize, enrich and deploy. It’s how we go deep in a sector, prove ROI, and learn the idiosyncrasies that make the normalization layer hard to replicate.

Product vision

A platform that compounds

The same four layers are a productized platform. Once a team builds our AI-ready surfaces into production, switching means re-architecture - and every engagement makes the platform smarter for the next.

Every use makes it smarter.

Most software works the same on day one as on day one thousand. Ours improves with every use, the more your team puts it to work, the sharper and more accurate it gets.

Engage a sector

We go deep on one fragmented market - its data, its decisions, its language.

Normalize the data

Canonical schemas and semantic maps that encode hard-won domain knowledge.

Deploy agents

Live use cases generate the usage data the infrastructure learns from.

Compound

Every engagement sharpens the models. Every new sector deepens the edge.

Domain depth and technical breadth.

Domain experts without AI infrastructure produce well-structured but inaccessible data. AI engineers without domain expertise normalize the wrong things. We're built on both.