10 Cedar Lane Credit Resolution Fund I

10 Cedar Lane · Confidential

Accredited Investor Confirmation

The materials behind this page describe a private offering of securities by 10 Cedar Lane Credit Resolution Fund I LP. They are intended solely for "Accredited Investors" as defined under Rule 501(a) of Regulation D under the U.S. Securities Act of 1933.

To proceed, please confirm that you meet at least one of the following:

Individual income exceeding $200,000 in each of the two most recent years (or $300,000 joint with spouse) and a reasonable expectation of the same this year
Individual or joint net worth exceeding $1,000,000, excluding the value of your primary residence
An entity with total assets in excess of $5,000,000, or in which all equity owners are accredited investors
A holder of a Series 7, 65, or 82 license in good standing

I confirm that I am an Accredited Investor as defined above, and that I will treat the contents of this page as confidential and not distribute them.

This page is for discussion purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any security. The Partnership is offered solely pursuant to its definitive offering documents.

The Opportunity

10 Cedar Lane Credit Resolution Fund I deploys capital into a technology-enabled strategy: acquiring portfolios of charged-off unsecured consumer receivables — credit card, personal loan, and BNPL paper — at deep discounts to face value, and resolving them through a proprietary, machine-learning-driven recovery platform that replaces legacy collections infrastructure. The Fund targets attractive net returns with low correlation to corporate credit and public markets.

Why Now

Record supply. U.S. credit card charge-offs reached ~$60B in 2024, a record. Personal loan charge-offs ~$11B and growing; BNPL is forming a secondary market for the first time.

Legacy disruption. An industry built on call centers and aggressive tactics is mismatched to digital-native borrowers and a consumer-friendly regulatory frame — creating a structural cost and compliance edge for technology-led operators.

Pricing inefficiency. Mid-market portfolios are overlooked by mega-cap public buyers (Encore, PRA), where lighter institutional competition drives more attractive entry pricing.

Why 10 Cedar Lane

Machine-learning-driven underwriting. Per-portfolio recovery models predict collectability at the account level and set disciplined bid floors and ceilings — the structural defense against the single largest source of return drag in this asset class: overpaying for paper.

Proven playbook, sharpened by AI. Recovery runs on the industry-standard toolkit — letter campaigns, agent and dialer-based outreach, skip tracing, settlement offers, structured payment plans, and selective litigation — operated under modern compliance. Cirqul layers data and AI on top: ML-based propensity-to-pay and channel-routing models are live in pilot today; dynamic settlement pricing and NLP-based call QA are on the near-term roadmap.

Disciplined sourcing and segment focus. Multi-channel access (direct lender relationships, brokered flow, marketplace platforms, forward-flow agreements) into the mid-market segment. Credit cards and personal loans (primary), BNPL (emerging); medical, student, and mortgage deficiency excluded.

Indicative Terms

Fund	10 Cedar Lane Credit Resolution Fund I LP — Delaware limited partnership
Operating Affiliate	Cirqul Ventures LLC — purchaser and/or servicer of underlying receivables
Target Fund Size	$5M – $15M of equity capital
Minimum Commitment	$500,000 (lesser amounts at GP discretion)
Investment Period / Term	18 months (extendable to 30) / 4-year fund term + 1-year extension
Management Fee	2.0% on committed capital; 1.0% on third-party leverage deployed
Preferred Return	8% per annum, compounded annually
Carry & Waterfall	20% / 25% above 25% net IRR / 30% above 30% net IRR · whole-fund waterfall · GP clawback
Eligible Investors	Accredited Investors as defined under U.S. securities laws

To request the full investor presentation or schedule an introductory call: investors@10CedarLane.com · 10cedarlane.com

For Accredited Investors Only · Confidential · Not for Distribution. This document is for discussion purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any security. The Partnership is offered solely pursuant to its definitive offering documents, which qualify this summary in its entirety. Past performance is not indicative of future results. All investments involve risk, including possible loss of principal.

Market Opportunity

U.S. Credit Card Charge-Offs ($B, annual)

77M

Americans currently in collections

$0.04–0.08

Typical cents on the dollar paid for charged-off paper

Sources: Federal Reserve G.19 (illustrative), NY Fed Household Debt & Credit Report Q4 2024, TransUnion Q2 2025.

Investment Process

Sourcing

Multi-channel deal flow

Underwriting

ML recovery prediction

Acquisition

Disciplined bidding

Recovery

AI-driven resolution

A Better System for a Better Asset Class

	Legacy Collections Industry	Cirqul Ventures Platform
Underwriting	Spreadsheet-based bid models, blunt segmentation	ML-based per-portfolio recovery prediction at the account level
Recovery Ops	Call-center labor, fixed scripts, high human capital cost	AI-orchestrated digital outreach: optimized channel, timing, message, offer
Consumer UX	Outbound calls, dunning letters, debit-card-only payment	Self-service app, flexible settlement, embedded financial wellness tools
Compliance	Aggressive tactics; elevated FDCPA, TCPA, and litigation exposure	Empathetic, app-mediated, regulator-aligned engagement model
Data Advantage	Static models, limited learning across portfolios	Compounding training data — platform improves with every portfolio serviced

The Investment Thesis

Pricing discipline is the structural defense. The largest source of permanent loss in charged-off paper is overpaying at acquisition. Per-portfolio ML models that score expected recovery at the account level let the platform set bid floors and ceilings before capital is committed.

The platform compounds with scale. Every portfolio resolved through the technology stack adds to the training data. Underwriting models sharpen, recovery orchestration improves, and the operating cost base remains variable rather than fixed — a structural moat legacy buyers cannot replicate.

Key Considerations

Distressed consumer credit involves credit-cycle, operational, regulatory, and platform-execution risks specific to the asset class. The Fund's strategy depends on the performance of its technology partners, the Fund's ability to source attractive portfolios, and ongoing compliance with FDCPA, the CFPB's Regulation F, TCPA, and state-level licensing regimes. Capital is at risk. See offering documents for a full discussion of risk factors.