In production · since 2019

The same pattern, three industries deep.

Years of operational data. Decisions that have to be right. AI applied exactly where it pays. Here’s what that looks like in the sectors we run today — and the shape it takes in yours.

Embedded finance

Underwriting across 15 countries, in under an hour.

An emerging-markets lender had borrower signal scattered across mobile-money APIs, bureau pulls in fifteen national formats, and field-agent notes in three languages. Assembling one profile took days. EMBD unified the data into a canonical borrower profile an AI credit agent can reason across — so decisions that took days now happen in under an hour, opening segments that never penciled out before.

3–5 days→< 1 hrDecision time

15 countriesCoverage

$50M+ AUMMonitored

“The behavioral data always predicted repayment better than any bureau score. We just couldn’t use it. Now an agent reads across all of it in seconds.”

Head of credit · emerging-markets lender

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Healthcare procurement

From hours on the phone to one query.

A specialized procurement network was burning hours per case calling suppliers one by one to find a matching graft before a deadline — with no way to see live inventory or compare options. EMBD normalized incompatible nomenclature across the network, connected each supplier’s existing system, and stood up an agent that searches every participating supplier in parallel and returns ranked, deliverable matches in seconds.

2 hrs→2 minPer case

~$1,700 savedPer case

87+ suppliersIn parallel

“We used to take the first option that could make the window. Now we see every match that can — cheaper and better-fit — in the time it takes to read this.”

Procurement lead · surgical supply network

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Mortgage & specialty lending

Legacy loan systems, ready for AI underwriting.

A specialty lender’s deal and performance history lived across legacy origination systems, covenant spreadsheets and six years of vintage files — none of it accessible to AI. EMBD connected those systems, built canonical deal profiles with standardized risk taxonomies, and exposed the normalized history through RAG and live MCP so an underwriting agent can retrieve comparable loans and draft assessments in minutes.

hours→minutesPer assessment

6 yrs vintageMade comparable

live pipelineReal-time view

“The comparable deals were always in the files. Finding them meant knowing they existed. Now the agent pulls the right ones — by sector, leverage and vintage — automatically.”

Director of underwriting · specialty lender

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Run on an ERP, a CRM and a stack of operational systems?

Then the pattern applies. Manufacturing, distribution, insurance, specialty services — start with one use case and let the data compound.

Fragmented dataDecisions that must be rightEarly AI adoptionMeasurable ROI

Your industry

What would this look like in your business?

Book a 30-minute call. We’ll map your highest-ROI use case and what the first agent looks like — grounded in the data you already have.

Book a demo→See how it works hello@embd.finance