Statement of Work — frankstickets.com Portfolio Management Platform

01Executive Summary

frankstickets.com is an AI-powered portfolio management platform purpose-built for the Client's personal ticket portfolio. At its core sits an AI intelligence layer comprising AI-driven dynamic pricing (a machine-learning demand-elasticity engine) and an AI hold-or-sell decisioning engine (Monte Carlo simulation across thousands of price paths per position). Around that intelligence layer the platform delivers seat-level cost basis tracking, cross-marketplace distribution across StubHub, Ticketmaster, SeatGeek, and Vivid Seats, and a financial-terminal-grade operator dashboard for the owner.

This Statement of Work ("SOW") covers the full design and development of the platform as the Client's personal investment management system. The engagement is structured in two phases — Phase A (Foundation) delivers a working single-owner MVP with one marketplace integration and rule-based pricing, followed by Phase B (Intelligence + Full Distribution) which activates the full AI pricing intelligence engine, the AI hold-or-sell decisioning engine, and the remaining three marketplace integrations.

A potential future Phase C would extend the platform into a multi-tenant SaaS product sold to other portfolio owners. Phase C is explicitly out of scope for this SOW and would be scoped separately if and when the Client elects to pursue it.

02Scope of Work

2.1 Engagement Approach

Principal engagement. Solo principal (James McEwen, founder of SilverFox Media) acts as designer, architect, and engineer across the full stack.
Integrated workstreams. UX, UI, and quality assurance run continuously through the build cycle rather than as batched upfront phases. Iterative refinement happens against working software, not against deck mockups.
Single-owner platform. The platform is built and operated as the Client's personal portfolio management system. Multi-tenant infrastructure, subscription billing, and external customer onboarding are out of scope for this engagement and would be addressed in a future Phase C if/when the Client elects to commercialize.
Defensive design for future commercialization. Schema and API endpoints are designed so a future tenant scope could be retrofitted without a ground-up rebuild. This is light-touch only — no full tenancy infrastructure is built in this engagement.
Technology stack. React + Vite + Tailwind + TypeScript on the frontend; Node + Express + TypeScript on the backend; MongoDB Atlas for primary storage; a Python microservice for the AI engines (ML demand-elasticity pricing model + Monte Carlo decisioning simulator); BullMQ + Redis for job processing.

2.2 Deliverables by Workstream

Brand finalization — Logo system (mark, wordmark, tagline, responsive lockup specifications at three breakpoints), color palette lock, typography selection, brand guidelines document. Output: brand reference document and exported logo assets.
UX discovery and iterative refinement — Discovery sessions with Client to map his current manual workflow (acquisition, purchase tracking, listing, repricing), journey mapping for the owner role, review of one comparable platform (advantagetixx.com) flagged during discovery, and ongoing UX refinement during the build cycle.
UI design system — Design token specification (colors, spacing, typography, motion), component specifications produced live as components are coded, responsive specifications for mobile, tablet, and desktop, dark-mode primary aesthetic consistent with the marketing site.
Frontend build — Marketing site updates, single-owner authentication (login, optional MFA), portfolio dashboard, positions list and detail views, manual purchase + PSL entry flow, pricing engine settings, hold-or-sell decision interface, marketplace distribution panel, analytics and reports, tax and reporting export interface, settings. Real-time updates via Socket.io. Full WCAG AA accessibility audit.
Backend build — Single-owner authentication (JWT with refresh tokens, optional MFA), four marketplace API integrations (StubHub, Ticketmaster, SeatGeek, Vivid Seats), portfolio core (cost basis, NAV mark-to-market, realized and unrealized P&L), AI dynamic pricing engine (Python microservice running a machine-learning demand-elasticity model, retrained nightly per league), AI hold-or-sell decision engine (Monte Carlo simulator running 10,000-plus price paths per position with confidence bands), analytics and tax reporting (1099-K, Schedule D, QuickBooks export), BullMQ and Redis job queues, notification system (in-app, email, optional SMS), basic owner admin tools.
QA and testing — Continuous testing integrated with development. Unit tests, integration tests, end-to-end tests via Playwright, security testing, and load testing prior to Phase B completion.
Deployment and infrastructure — Render or AWS hosting, MongoDB Atlas, Redis (Upstash), S3 file storage, CDN, SSL, secrets management, Sentry observability, CI/CD pipelines, backup and disaster recovery strategy.
Owner onboarding and iteration — Hands-on onboarding of the Client onto the platform with his actual portfolio data, instrumented product analytics (PostHog or Mixpanel), friction tracking and resolution, performance optimization, bug resolution from real-world usage as the Client begins managing his portfolio through the system.

03Phasing and Pricing

3.1 Phase A — Foundation

Duration: 4–6 months from SOW execution

Scope: Single-owner authentication. Portfolio data model and seat-level cost basis tracking. Manual purchase and PSL entry flow with full acquisition history. Owner dashboard with positions list, detail views, and NAV / mark-to-market reporting. One marketplace integration (StubHub recommended — most reseller-friendly API and largest secondary market share). Rule-based pricing with floor and ceiling guardrails (the full AI pricing engine (ML) activates in Phase B once the model has accumulated training data). Display-only decision analytics (the AI hold-or-sell engine (Monte Carlo) activates in Phase B). Deployment to production. Hands-on owner onboarding with real portfolio data and initial friction iteration.

Fixed fee $45,000

Payment schedule:

Milestone	%	Amount
On SOW execution	30%	$13,500
Foundation deployment to production	40%	$18,000
Phase A completion (owner onboarded, friction iteration complete)	30%	$13,500

3.2 Phase B — Intelligence + Full Distribution

Duration: 5–7 months following Phase A completion

Scope: Remaining three marketplace integrations (Ticketmaster, SeatGeek, Vivid Seats). Full AI dynamic pricing engine — a machine-learning demand-elasticity model with nightly retraining per league. Full AI hold-or-sell decision engine — Monte Carlo simulation running 10,000-plus price paths per position with statistically validated confidence bands. Tax and reporting (1099-K, Schedule D, QuickBooks export). Advanced analytics (cohort analysis by team, venue, and acquisition channel; Sharpe-style risk metric tuned for ticket volatility).

Fixed fee $65,000

Payment schedule:

Milestone	%	Amount
Phase B kickoff	30%	$19,500
Feature-complete milestone	40%	$26,000
Phase B completion (Client acceptance)	30%	$19,500

3.3 Total Engagement

Total fixed-fee commitment (Phase A + Phase B) $110,000

A potential future Phase C — converting frankstickets.com into a multi-tenant SaaS product sold to other portfolio owners — is explicitly out of scope for this SOW. If/when the Client elects to pursue Phase C, it will be scoped and priced separately.

3.4 Engagement Value vs. Agency Comparable

The scope outlined in this Statement of Work would typically be delivered by a multi-disciplinary agency team of three to five specialists supported by dedicated project management. This engagement delivers the same scope, the same quality standard, and the same end deliverable through a senior solo principal — eliminating the team-handoff overhead, project-management layer, and profit-margin stack that drive agency pricing materially higher.

	SilverFox Media	Typical Agency Comparable
Engagement model	Senior solo principal	3–5 person team + PM
Calendar time to ship	4–6 months (Phase A)	6–9 months (equivalent scope)
Phase A — Foundation	$45,000	$75,000 – $140,000
Phase B — Intelligence + Full Distribution	$65,000	$90,000 – $180,000
Total fixed-fee	$110,000	$165,000 – $320,000
Ongoing retainer (monthly)	$1,500 – $5,500	$4,000 – $12,000
Single point of accountability	Yes	PM-mediated

Bottom line: the same platform ships 2–3 months sooner and costs $55,000 to $210,000 less than an equivalent agency engagement. The savings come from eliminating team-handoff overhead, project-management layering, and agency profit margins — without compromising senior-developer execution or accountability.

04Ongoing Platform Services

The following services are separate from and in addition to the fixed development fees outlined in Section 3. They cover the ongoing hosting, maintenance, and management of the platform once it is in active production use by the Client. Third-party infrastructure costs (hosting, database, monitoring, and similar subscriptions) are pass-through expenses owned directly by Client and are not included in the retainer fees below.

Client selects one of the following monthly retainer tiers at or before Phase A go-live. The selected tier may be upgraded at any time with thirty days written notice.

Service	Basic $1,500 / mo	Standard $3,500 / mo	Full Managed $5,500 / mo
Hosting and infrastructure oversight	✓	✓	✓
Uptime monitoring and alerting	✓	✓	✓
Security and dependency updates	✓	✓	✓
SSL and domain renewals	✓	✓	✓
Backup and disaster recovery management	✓	✓	✓
Minor bug fixes	—	✓	✓
Performance optimization	—	✓	✓
Incident response and escalation	—	✓	✓
Monthly development hours (feature updates)	—	—	15 hrs / mo

Retainer engagements begin the month following Phase A go-live and are billed monthly. Either party may terminate the retainer with thirty days written notice.

05Timeline

Milestone	Target
SOW execution and Phase A deposit	Week 0
Brand finalization complete	Week 4
Phase A: Foundation deployment to production	Month 4–6
Phase A: completion (owner onboarded with real portfolio)	Month 6
Phase B: kickoff	Month 7
Phase B: feature-complete milestone	Month 11
Phase B: completion (Client acceptance)	Month 11–13
Ongoing platform services retainer begins	Phase A go-live

Timeline assumes Client meets the two-business-day feedback turnaround commitment described in Section 8.

06Included in Scope

All design work — brand, UX, UI, and design system
Full frontend and backend implementation for single-owner production use
All four marketplace API integrations (delivered across Phase A and Phase B)
Full AI intelligence layer — AI dynamic pricing engine (ML demand-elasticity) and AI hold-or-sell decision engine (Monte Carlo simulation)
Single-owner authentication and basic owner admin tooling
Deployment, monitoring, and CI/CD configuration
Hands-on owner onboarding with real portfolio data and friction iteration
Tax and reporting exports (1099-K, Schedule D, QuickBooks)

07Not Included in Scope

Marketplace API access fees, developer-program registration, and partnership-approval timelines (each marketplace operates its own approval cycle; Client procures access independently)
Multi-tenant SaaS infrastructure, subscription billing, tenant-scoped credential vault, role-based access across tenants, onboarding wizards for external customers, and the legal document set required to sell the platform to third parties (ToS, Privacy Policy, DPA, Subscription Agreement). All of these are reserved for a future Phase C engagement.
Native iOS or Android mobile applications (web responsive is included; native is a separate engagement)
Marketing, paid advertising, SEO content production, or PR
Customer support staffing
Ongoing maintenance beyond the Section 4 retainer, hosting fees, and third-party service subscriptions (Sentry, MongoDB Atlas, and similar) — Client owns these accounts directly
Custom enterprise integrations beyond the four named marketplaces

08Key Assumptions

Client provides timely access to all required marketplace API credentials and signs partner agreements with the four marketplaces.
Client responds to design reviews, scope decisions, and feature feedback within two business days.
Stack decisions documented in this SOW remain fixed unless changed by mutual written agreement.
Hosting selection (Render or AWS) is finalized during Phase A discovery.
Client provides representative portfolio data (existing PSL inventory, historical acquisitions, recent listing activity) for use during onboarding and as initial training data for the AI engines.

09Risks and Dependencies

Marketplace API constraints. StubHub and Ticketmaster reseller APIs have approval workflows that may take six to twelve weeks and gate-keep certain features. Some functionality may require workarounds, including the possibility that one or more marketplaces require routing distribution through an aggregator (such as Automatiq Lysted) rather than a direct seller API. A discovery call with each marketplace partner is recommended before Phase B begins to confirm direct-integration feasibility.
AI model cold-start. The AI pricing model's accuracy improves with data volume. Initial three to six months of production use may rely on rule-based fallback behavior while the underlying machine-learning model accumulates training data from the Client's own portfolio activity.
Future commercialization scope. If/when the Client elects to convert frankstickets.com into a multi-tenant SaaS product (Phase C), the codebase will require a tenancy retrofit. This SOW includes light-touch defensive design to keep that retrofit feasible but does not pay for tenancy infrastructure today. The Phase C retrofit will be scoped and priced separately.
Calendar dependency on Client responsiveness. The integrated UX and UI approach requires tight feedback loops. Delays in Client response extend the timeline proportionally and may trigger schedule re-baselining.

10Change Management

Any change to scope, deliverables, or timeline must be documented in a written Change Order signed by both parties. Vendor will provide an impact assessment (hours, cost, and timeline impact) within five business days of any Client-requested change.

11Intellectual Property

All code, designs, documentation, and other work product created under this SOW becomes the property of Client upon receipt of final payment. Vendor retains the right to reference the engagement and high-level descriptions in Vendor's portfolio and marketing materials, without disclosing Client's confidential business information.

12Acceptance Criteria

Each phase is considered complete when:

All scoped deliverables are demonstrated functioning in the production environment.
Client has had ten business days to review and submit acceptance feedback.
All P0 (critical) and P1 (high-priority) issues identified during the review period are resolved.

13Cancellation

Either party may cancel this SOW with thirty days' written notice. Upon cancellation, Vendor will be paid for all work performed through the cancellation date plus thirty days of transition support. All work product completed and paid for transfers to Client.

14Confidentiality

Both parties agree to keep confidential all non-public information shared during the engagement. This obligation survives the termination of this SOW for a period of three years.

15Agreement

This Statement of Work outlines the proposed scope, deliverables, timeline, and pricing for the engagement between SilverFox Media and [CLIENT NAME]. It is provided for review and remains a non-binding draft until both parties execute a separately binding contract.

Upon mutual agreement to proceed, the parties will enter into a formal written contract incorporating the terms described herein, along with any revisions agreed during negotiation. That executed contract — not this document — will govern the legal relationship between the parties.

AGlossary of Technical Terms

Terms marked throughout this document are defined here in plain language.

AI Hold-or-Sell Decision Engine (Monte Carlo)

An AI decision-support engine built into the platform dashboard. Powered by Monte Carlo simulation — the same technique used by quantitative finance funds to model uncertainty — it runs tens of thousands of price-path simulations against historical market data and surfaces a probability-scored recommendation. Example output: "73% chance this seat increases in value if held two more weeks." The trader keeps control; the AI does the math.

AI Dynamic Pricing Engine (ML Demand-Elasticity)

The platform's AI pricing brain. A machine-learning demand-elasticity model continuously learns from historical ticket sales data — across leagues, venues, teams, opponent strength, and time-to-event decay — and automatically calculates the optimal listing price for each seat in your inventory. The model retrains nightly per league and surfaces recommended prices directly in your dashboard, replacing manual repricing with market-calibrated AI suggestions.

NAV (Net Asset Value) / Mark-to-Market

NAV is the real-time estimated total value of your entire ticket portfolio, calculated by comparing what you paid for each seat against what it would sell for on the market right now. Mark-to-market means that value is updated continuously as market prices move — the same way a brokerage account shows you your current portfolio value.

Realized and Unrealized P&L

P&L stands for Profit and Loss. Realized P&L is the actual profit or loss on tickets you have already sold. Unrealized P&L is the gain or loss on tickets you still hold, based on their current market value versus your cost basis.

WCAG AA Accessibility

The widely adopted industry standard for digital accessibility. Building to this standard ensures the platform works correctly for users with visual or motor impairments and is increasingly required by enterprise clients.

1099-K / Schedule D Export

The platform automatically generates the tax reporting documents your accountant needs at year end. The 1099-K summarizes gross ticket sales receipts; Schedule D covers capital gains and losses from ticket resale activity.