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Frequently Asked Questions
What is the rationale behind the $10M post-money valuation?
Why is the current raise set at $1.5M, and what runway does this capital provide under base-case and downside scenarios?
What does a $1.5M investment obtain in terms of ownership, rights, and expected investor returns?
Will the business require additional capital beyond this raise, and under what conditions?
How will the $1.5M be allocated across product, operations, sales, compliance, and other functions?
What are the company's current financials (cash position, burn rate, revenue, margins, runway)?
How sensitive is your runway if sales cycles extend or revenue ramps slower than projected?
What milestones will this round unlock, and what specific deliverables or KPIs will you commit to achieving in the next 12-24 months?
What are the key assumptions in your financial model that are most sensitive or least supported by data?
What future fundraising strategy (if any) is anticipated following this round?
What evidence supports lender willingness to pay $40-$75 per application at scale?
What proportion of applicants typically pass initial filters, and what data supports 3%-10% funding rates?
What is the current median loan value and distribution across segments?
What historical data do you have regarding application quality, underwriting outcomes, and default rates?
Which revenue stream is expected to contribute most in the next 24 months and over the next five years?
What are the expected gross margins for each revenue line (application fees, success fees, SaaS, etc.)?
What data supports your projected volume multipliers and application growth assumptions?
What are the sales cycles for private lenders, credit unions, and community banks?
What is today's application volume and revenue run-rate compared to projections?
How many customer commitments, LOIs, or pilot agreements are in place with lenders or financial institutions?
What is the onboarding timeline for new financial institutions, and what are the operational requirements?
What is the actual CAC for lenders and borrowers, and what percentage of each group are repeat users?
How is borrower data stored, shared, governed, and controlled across multiple lenders?
How do you ensure compliance related to secure document handling, AI-assisted underwriting, audit trails, and cross-lender data sharing?
Does the marketplace model trigger lending or broker licensing requirements at the federal or state level?
How do you manage regulatory differences across U.S. states and between the U.S. and Canada?
How do borrowers consent to having their data shared with additional lenders after an initial decline?
How do you audit AI decision-making to ensure fairness, compliance, explainability, and regulator-friendly documentation?
Who are the key competitors, and what are your differentiators in product, data, relationships, and defensibility?
What prevents major core-banking providers or large private lenders from building similar AI-driven underwriting tools?
What structural, technical, or data-based moats protect you from fast-follower entrants?
What proprietary technology, data assets, or algorithms provide a defensible advantage?
Is your AI trained on customer data, and how is regulatory compliance maintained during training and deployment?
Which parts of V1-V3 are fully built, deployed, or in active use today?
What features are included in V2 and V3, and what is the timeline and resource plan to deliver them?
What specific team, capital, and integration requirements are needed to hit each product milestone?
Which components of the platform have been tested in real-world lender workflows, and what validation exists (hours saved, efficiency gains, conversion improvements)?
What feedback or pain points have current or pilot lenders identified?
What operational bottlenecks emerge at 10x current usage (infrastructure, underwriting throughput, onboarding, support)?
What level of customer support is required for private lenders, credit unions, and banks, respectively?
What are the most common reasons lenders decline to adopt, pilot, or expand usage of the platform?
What infrastructure, team, or workflow bottlenecks exist today?
Do you have case studies or pilot data that validate the claimed 85% early-rejection filtering efficiency?
Which roles are essential to achieve near-term product milestones, and how does this align with your current burn rate?
What are the critical hires required in the first 12 months to de-risk execution?
What is the company's long-term exit strategy (e.g., strategic acquisition, platform roll-up, IPO)?
What is the realistic view of AP's potential exit range?
