Proximitty GTM brain

category

market profile

• text: Commercial loan teams face document collection, financial spreading, covenant tracking and servicing work throughout the loan lifecycle.
• text: Regulated lenders must evaluate model risk, security, auditability and integration alongside operational gains.

personas

• title: Chief credit officer
  pains:

  • Inconsistent spreads and slow credit memos constrain underwriting throughput
  • Model decisions need reviewable evidence and governance
• title: Head of commercial lending
  pains:

  • Borrower document collection extends time to close
  • Growing CRE, C&I and SBA books can require more analyst capacity
• title: Loan operations or servicing leader
  pains:

  • Covenant deadlines and borrower obligations are difficult to track across systems
  • Servicing backlogs reduce visibility into portfolio issues
• title: Model risk or compliance leader
  pains:

  • Agent output requires validation and traceable approvals
  • Procurement requires evidence on data handling and regulatory alignment

icp

• text: US community and regional banks, credit unions and non-bank lenders with commercial lending operations.
• text: Lenders with CRE, C&I or SBA loan portfolios and document-intensive credit and servicing workflows.

message pillars

• text: Automate work across the lending lifecycle.
• text: Produce consistent outputs that credit teams can review.
• text: Track obligations and surface emerging problems.
• text: Preserve customer control over data.

objections

• objection: Our bank cannot use an opaque AI model for credit work.
  response: Review validation artifacts, audit trails, approval controls and model risk procedures with the bank's MRM team.
• objection: Replacing the core lending system is too disruptive.
  response: Assess a bounded browser-agent integration against existing workflows in a sandbox.
• objection: Financial statements vary too much for automated spreading.
  response: Pilot on representative statements and compare assumptions and exceptions with analyst-reviewed spreads.

competitors

• name: nCino
  why: Commercial lending software named in Proximitty's comparison.
• name: Abrigo
  why: Lending and financial spreading software named on the site.
• name: Moody's
  why: Lending Suite covers spreading and credit workflows.
• name: Aloan
  website: https://aloan.ai/solutions/covenant-monitoring
  why: AI commercial lending and covenant monitoring platform.

influencers

• name: Wye Yew Ho
  url: https://councils.forbes.com/profile/Wye-Yew-Ho-Chief-Executive-Officer-Proximitty/528d821f-fa28-46d1-8db0-51e01fe24ba0
  why: Co-founder and CEO discussing commercial lending operations.

keywords

• commercial loan underwriting automation
• AI financial spreading
• loan covenant monitoring software
• commercial loan servicing AI
• SBA loan document automation
• credit memo automation

voice

• Compliance conscious
• Operational and specific
• Confident and results oriented

signals

• text: A commercial lender is expanding its loan book while analyst capacity or servicing backlogs are under pressure.
• text: The institution is modernizing covenant monitoring or replacing manual financial spreading.

stack

• browser agents
• existing banking systems

hiring

• Member of Technical Staff

pricing

motion

channels

• website
• LinkedIn
• YC directory
• industry content

confidence

Founders

• name: Wye Yew Ho
  title: Founder/CEO
  role: CEO
  bio: Co-founder & CEO @ Proximitty (YC W26). Previously, led FinCrime and Growth at Taptap Send ($75m -> $200m ARR) and started his career advising banks and fintechs on risk strategy at McKinsey. Highest ranked Malaysian in history in the Southeast Asian Maths Competition (SEAMC).
• name: Zi Zhang
  title: Founder/CTO
  role: CTO
  bio: Co-founder & CTO @ Proximitty (W26). Previously, led security infrastructure at Bloomberg (securing 300k+ terminals) and Head of Engineering at ACI.dev (built world's first unified MCP with 4k GitHub stars in 1 month).