Sei AI

AI operations platform for mortgage lenders, servicers, and banks — voice agents, document intelligence, and call monitoring built for regulated finance.

Where Sei AI ranks

Current edition · updated Oct 20, 2026 · All 6 rankings

About Sei AI

Sei is an AI platform for mortgage lenders, servicers & banks. We're already live with banks, and top IMBs.

Products

What customers say

“A NASDAQ-listed mortgage lender automates 90% of QC and cuts loan review time from 5 hours to 40 minutes with Sei.”
Listed NASDAQ LenderSei AI customer
“Sei helps a large edtech lender in Asia save 60% in costs and increase conversion”
Edtech Lender (Asia)Sei AI customer
  • Document Intelligence

    Better Mortgage uses Sei Document Intelligence for pre-close mortgage QC against agency guidelines and investor overlays. Sei reports review time fell from 4 hours 50 minutes to 47 minutes per file, with 92% of checks…

    Case study
  • Call Monitoring & Compliance

    Better Mortgage uses Sei Call Monitoring to score loan-officer calls against its SOPs and regulations. Sei reports over 1,000 calls audited monthly, a month's calls processed in under an hour, a cleared backlog and 4×…

    Case study

Security and compliance

As reported by Sei AI. Ask for audit reports before you buy.

  • SOC 2 Type IICertified
  • PCI DSSCertified
  • GDPRCompliant
  • AES-256 encryption at rest
  • TLS 1.2+ encryption in transit
  • Private VPC deployment
  • Customer-scoped data isolation
  • RBAC with least-privilege defaults
  • MFA for all administrative actions
  • SSO via SAML and OIDC
  • Okta SAML/OIDC IdP
  • Azure AD SAML/OIDC IdP
  • Google Workspace SAML/OIDC IdP
  • Immutable interaction records
  • Full reasoning trace audit logs
  • Source-document citations on extracted loan data
  • Export-ready logs for regulator and internal audit reviews
  • Annual third-party penetration testing
  • Continuous vulnerability scanning
  • Continuous monitoring (Guard Duty, SIEM tools)
  • Critical patches applied within defined SLAs
  • Customer data never used to train foundation models
  • Sandboxed environments per customer

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