AI-native lending infrastructure.

From onboarding to decision to disbursement — enriched, scored and approved online.

Every borrower, reconstructed as data.

Built for the people who
lend.

++++++++++++++++++

Banks

Fintech lenders

Financing companies

Credit unions

Neobanks

SOFOMes

Microlenders

BNPL providers

Factoring & leasing

Embedded finance

The gap

Most lenders say
no to businesses
they can’t measure.

Good businesses get rejected — not because they’re risky, but because
the data to measure them isn’t there.

Thin or empty files

A healthy business with no bureau history reads as a blank — and gets an automatic rejection.

Offline underwriting

Manual document chases and branch visits are slow. Good applicants drop off before a decision.

Not enough signal

With limited data you either play it too safe and lose revenue, or misprice risk and absorb defaults.

CapabilitiesEngineered for online lending

01 · Online application

Your borrowers apply entirely online — no branch, no paperwork ping-pong.

02 · Alternative-data enrichment

We enrich every application with public records and proprietary signals.

03 · Decision via API

One API to your decisioning — the yes or no returns in real time.

04 · Online disbursement

Approved borrowers are funded online. Fully digital, end to end.

New application

Legal business name

Grupo Maderas SA

Amount

$250,000

Term

12 months

Submit application

Enriching signals

Supplier data

+0.16

Tax data

+0.21

Telecom

+0.13

Reputation

+0.10

POST/v1/applications200 OK

{
  "decision": "approved",
  "score": 0.91,
  "signals": 240
}

✓

$250,000

funded online · instant

The rails

One layer between borrower and decision.

A mathematical product with unique data and markup. Sylvent reconstructs the borrower from hundreds of live signals — public, financial, behavioural and partner data — and computes a decision your underwriting can act on.

SYLVENT · SCORING ENGINE

x ∈ ℝ²⁴⁰

Tax data
›
Bank & transaction data
›
Documents (OCR)
›
Online reputation
›
Partner data
›

P(credit) = σ( Σ wᵢ·xᵢ )

Σ wᵢ·xᵢ = 0.00

0.00P(CREDIT)

Computed live from

240+ unique signals

✓ APPROVED · FUNDED ONLINE

Outcomes

What changes for your book.

More yes.

Approve the thin-file borrowers you used to reject — safely, on richer data and
sharper scoring.

Lower default risk

Richer data and sharper scoring on every file you decide.

Faster decisions

Minutes, not days — fully automated, end to end.

Fully online

Application to funded loan, with no offline steps.

Outcomes describe what Sylvent is built to deliver and depend on each lender’s portfolio and policy.

Integration

API-first. Live without rebuilding your core.

Sylvent sits on top of your existing decisioning. Your policy, your money — our data and rails.
Send an application, get a structured, scored decision back.

POST /v1/applications
{
  "borrower": "MX-RFC-…",
  "amount": 250000,
  "term_months": 12
}

→ 200 OK
{
  "status": "approved",
  "score": 0.91,
  "signals": 240,
  "documents": "validated",
  "disburse": "online"
}

Products

Three products, one platform.

Build your whole lending system on us, plug our data into the one you have, or let us bring you the borrowers.

app.sylvent.ai

Operating System

Talk to us

Ready to lend to more of your market?

A working demo on your own data flow — no hard sell, straight from the team building Sylvent.

✓A working demo on your data flow
✓Talk directly with the team building Sylvent
✓Leave with a concrete integration plan